Imagine a world where data isn’t just static numbers in rows and columns, but a lively stream of information, flowing constantly, telling the story of what’s happening at this very moment across systems and applications. That’s the world of event streaming, a vital artery in the heart of modern technology and data science. 

And Apache Kafka is one of the key tools enabling this innovative approach to data collection.

In this article, we’ll break down Kafka, demystifying its workings, exploring its features, and, most importantly, discovering how it’s being used to push the boundaries of what’s possible with data today. 

What Is an Event?

Events are the foundational concept of event streaming. These events can be anything: a click on a website, a payment transaction, a change in inventory level, or a sensor emitting a new reading. They are the occurrences that businesses must capture, process, and react to, not tomorrow, not in a few hours, but the instant they happen. Event streaming platforms empower organizations to do just that, transforming businesses into living organisms that sense and respond in real time.

While there are numerous event streaming platforms on the market, Kafka has become synonymous with event streaming because it’s more than just a messaging queue or a simple pipeline. It’s a robust, distributed system that can not only handle the velocity and volume of data but do so reliably, at scale, and with an ease of integration.

Understanding Apache Kafka

Apache Kafka’s inception at LinkedIn was a response to the need for handling high volumes of data and supporting real-time processing. It was built to address the shortcomings of existing systems in scalability and performance. Kafka quickly evolved from a messaging queue to a comprehensive event streaming platform, adept at managing the flow of data across an enterprise.

Kafka’s rise to prominence in the open-source community as an Apache project underscores its robustness and ability to tackle vast and complex data streams effectively. Now, it’s a key player in many organizations’ tech stacks, essential for real-time data insights and operations.

How Kafka Works

Kafka’s architecture might seem complex at a glance, but its beauty lies in the way its components work in harmony to facilitate seamless data streaming. Let’s peel back the layers and explore the mechanics of Kafka.

Topics and Partitions

Data within Kafka is organized into topics. You can think of a topic as a category or folder within which records are stored and published. Each topic is then split into partitions, which allow for the data within a topic to be scaled out and distributed across multiple nodes in the Kafka cluster for fault tolerance and increased throughput. This means that each partition can be hosted on a different server, allowing Kafka to parallelize processing, which is a key factor in achieving high performance.

Producers

Producers are the components that publish data to Kafka topics. They are responsible for determining which record goes to which partition within a topic. This can be done in a round-robin fashion for load balancing, or it can be directed by a key within the record (for example, ensuring all records for a specific user ID end up in the same partition to maintain order).

Brokers

A Kafka cluster is composed of multiple brokers. A broker is essentially a server that stores data and serves clients. Each broker holds certain partitions of topics, and by extension, the brokers make the cluster. They also take care of the nitty-gritty details, like handling requests from producers and consumers, maintaining the integrity and persistence of the data, and keeping track of the offsets.

Consumers and Consumer Groups

Consumers read data from brokers. They subscribe to one or more topics and pull data from the brokers that hold the partitions they’re interested in. Consumers work as part of a consumer group, which is a way to scale data processing by dividing the work of processing records across multiple consumers in the group. Each consumer in the group is assigned a set of partitions from the topics they subscribe to, ensuring that each partition is only consumed by one consumer in the group. Kafka manages the balance of partitions across consumers dynamically, which is particularly helpful as consumer numbers change.

Offsets

An offset is a unique identifier for records within a partition and denotes the position of a consumer in the partition. As a consumer reads records, it increments its offset. This allows consumers to resume from where they left off in the event of a failure or restart. Kafka stores the offset information in a special topic, so consumers can recover their position.

Replication

Kafka ensures data is never lost by replicating partitions across multiple brokers. This means if one broker fails, another can take over, ensuring high availability. The replication factor, which is configurable, determines how many copies of a partition exist in the cluster.

By understanding these mechanics, we can appreciate Kafka’s ability to provide a robust, scalable solution for handling large streams of data in real time. Its design caters to modern data requirements, ensuring that events are not only captured and stored but also made available for immediate processing, thus enabling real-time analytics and decision-making.

Key Features of Kafka

Now that we’ve delved into the architecture and mechanics of Kafka, let’s review the features that elevate Kafka beyond a mere messaging system to a comprehensive event streaming platform.

High Throughput for Massive Data Flows

One of Kafka’s hallmark features is its high throughput. It can handle hundreds of thousands of messages per second, even with modest hardware. This is achieved through efficient data structures and the partitioning mechanism that allows Kafka to parallelize processing across a cluster of machines. The ability to maintain high throughput makes Kafka ideal for scenarios that involve massive data pipelines or require real-time data feeds to power analytics and decision-making.

Scalability

Scalability is at the core of Kafka’s design. Whether it’s scaling out to accommodate more data or scaling up to support more intensive processing, Kafka handles it gracefully. You can increase Kafka’s capacity simply by adding more brokers to the cluster and rebalancing partitions across these brokers. This ease of scaling helps organizations maintain performance as their data demands grow.

Fault Tolerance

Fault tolerance is a critical aspect of distributed systems, and Kafka excels here. It’s designed to handle failures of its components without data loss. This includes broker failures, network issues, or consumer failures. Kafka’s fault tolerance mechanisms, such as replication and the ability to reassign partitions to different brokers, ensure that the system as a whole remains robust and operational despite individual component failures.

Real-Time Processing

The ability to process and analyze data in real time is invaluable. Kafka’s real-time processing capabilities enable applications to react to data as it arrives. Whether it’s triggering alerts based on live data streams or updating live dashboards, Kafka provides the necessary infrastructure to enable immediate data processing and response.

Practical Applications of Kafka

Kafka’s real-world applications are as varied as they are impactful. Here are concise examples of how Kafka is used across different industries:

• Financial Services: Banks use Kafka to process transactions in real time, enabling immediate fraud detection by analyzing patterns in transaction data as they occur.
• E-Commerce: Retail giants implement Kafka to manage customer activity streams, such as clicks, searches, and purchases, to provide real-time product recommendations and inventory updates.
• Logistics and Transportation: Companies track vehicle and shipment locations in real time, optimizing routes by processing streams of GPS data.
• Telecommunications: Telecom operators utilize Kafka to monitor network performance metrics in real time, allowing for quick responses to outages or service degradations.
• Gaming: Online gaming platforms leverage Kafka to track player actions and game state changes, enabling instant analysis for enhancing player experience and detecting cheating.

Key Kafka Skills

To fully harness the capabilities of Kafka, developers and data engineers need a certain set of skills and knowledge. Proficiency in Kafka can empower teams to build robust, scalable, and high-performing data-intensive applications. Let’s outline the skill set essential for any professional looking to master Kafka.

Understanding of Distributed Systems

Kafka is a distributed system, and having a solid grasp of the principles of distributed computing is fundamental. Developers and engineers should be comfortable with concepts such as consistency, partitioning, replication, and fault tolerance. Understanding these concepts helps in designing systems that can effectively utilize Kafka’s strengths and in troubleshooting issues that may arise.

Kafka APIs and Ecosystem Tools

Deep knowledge of Kafka’s APIs is crucial. This includes the producer API, consumer API, streams API, and connector API (for Kafka Connect). Each API serves a different purpose and provides a different interface for interacting with Kafka. Additionally, familiarity with ecosystem tools like Kafka Connect and Kafka Streams is valuable for building end-to-end data pipelines.

Debugging and Monitoring Kafka Clusters

The ability to monitor the health of a Kafka cluster and troubleshoot issues is vital. This involves setting up proper logging, metrics, and alerting systems. Kafka comes with tools like JMX metrics, which can be used to monitor various aspects of the broker, producer, and consumer performance. Knowledge of monitoring tools and platforms that integrate with Kafka can greatly simplify operational management.

Security Features and Best Practices

Because data is such a valuable asset, understanding Kafka’s security features is non-negotiable. This includes setting up authentication and authorization using Kafka’s ACLs, encrypting data in transit using SSL/TLS, and ensuring data at rest is secured. Awareness of the best practices around securing a Kafka cluster is a critical skill set for Kafka practitioners.

Performance Tuning

To optimize Kafka for various workloads, developers must be adept at performance tuning. This includes optimizing topic, partition, and consumer group designs, choosing the right configuration parameters for producers and consumers, and understanding how to scale a Kafka cluster efficiently without sacrificing performance.

Kafka Community and Resources

Staying engaged with the Kafka community and keeping up to date with the latest developments is beneficial. Contributions to Kafka’s open-source community and participation in forums and discussions can also be an excellent way for professionals to sharpen their skills and knowledge.

Embracing Kafka for Future Data Challenges

Kafka’s evolution from a high-throughput message broker to a comprehensive event streaming platform anticipates the future of real-time data handling. Its ability to bridge the gap between data sources and real-time decision-making is becoming increasingly crucial in a world awash with information.

Looking ahead, Kafka’s role is set to expand as the Internet of Things (IoT) and edge computing push the boundaries of data volume and velocity. As businesses demand more instantaneous insights and actions, Kafka’s scalable and fault-tolerant architecture will be pivotal in enabling these next-generation data infrastructures.

For those on the cutting edge, embracing Kafka means staying ahead in the race to harness real-time data’s strategic value. As we venture further into this data-centric era, Kafka’s importance in building responsive, data-driven applications is not just anticipated — it’s inevitable.

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The post What Is Kafka? Inside the Powerhouse of Real-Time Data Streaming appeared first on HackerRank Blog.

In a world increasingly shaped by vast quantities of data, the field of data science has never been more crucial. From small startups to global corporations, organizations are leveraging data to drive decision-making, identify new opportunities, and gain a competitive edge. But these insights are only as good as the data that informs them. In order for organizations to harness the full potential of their data, they need it to be clean, accurate, and readily usable. And they need people with the skills to wrangle it. 

As the sheer volume of data has grown in recent years, the demand for skilled individuals with data wrangling capabilities has grown too. In fact, HackerRank’s 2023 Developer Skills Report found data wrangling to be the sixth most in-demand technical skill, growing by 405% year over year. 

Data wrangling is the bridge between raw data and the refined, structured information that businesses rely on to make informed decisions. In this post, we’ll explore the specific steps involved in the process, the techniques employed, and the tools and skills necessary to excel in this discipline.

Understanding Data Wrangling and Its Importance

Data wrangling is the process of cleaning, structuring, and enriching raw data into a desired format for better decision making and analysis. This process is a fundamental step in turning disjointed data into a valuable resource. 

The importance of data wrangling is evident when we consider the nature of data in its raw form. It’s often messy, incomplete, and inconsistent. For instance, consider a dataset of customer information where some entries might have missing values, inconsistent date formats, or typos in the customer names. Without proper cleaning and structuring, it’s nearly impossible to derive accurate insights. In essence, data wrangling is the necessary first step in the data science pipeline.

The benefits of data wrangling include:

• Data Quality and Accuracy: Inaccurate or low quality data can lead to incorrect conclusions. Data wrangling helps to mitigate this by cleaning and transforming data, ensuring that the final dataset is of the highest quality.
• Time Saving: While data wrangling might seem like a time-consuming process, it actually saves time in the long run. Clean, well-structured data is easier to interpret and less likely to cause issues during the analysis phase.
• Informed Decision Making: The ultimate goal of any data-driven organization is to make better business decisions. Data wrangling is key to achieving this, as it transforms data into a format that can be easily understood and analyzed, leading to more informed choices.

The Data Wrangling Process

The process of data wrangling can be broken down into several key steps, each of which plays a crucial role in transforming raw data into a clean and usable format.

1. Data Collection

Data collection is the first step in the data wrangling process. This involves gathering data from various sources, such as databases, CSV files, APIs, and more. The goal is to ensure that the data collected is relevant and comprehensive. Gathering data from different sources, including internal and external databases, social media platforms, and more, is a crucial part of this step. It’s also important to ensure that the data collected is relevant to the analysis or decision-making process at hand and that it is comprehensive enough to provide a complete picture.

2. Data Cleaning

Once the data is collected, the next step is to clean it. This involves handling missing values, correcting inconsistencies and errors, and more. Handling missing values can be done through various methods, such as imputation, deletion, or other context-dependent methods. Another crucial aspect of data cleaning is addressing and correcting inconsistencies and errors, such as fixing typos, standardizing date formats, and more.

3. Data Transformation

Data transformation involves converting data into a suitable format for analysis. This might include creating new variables or features that are more relevant to the analysis. Converting data into a suitable format can involve changing data types, normalizing data, and more. Additionally, creating new variables or features, such as creating a new column that represents the age of a customer based on their date of birth, can provide more relevant information for analysis.

4. Data Enrichment

Data enrichment involves merging data from different sources and adding new information to enhance the dataset. Combining data from different datasets can create a more comprehensive dataset that provides a fuller picture of the situation. Adding new information to the dataset, such as additional context or data, can also enhance the analysis.

5. Data Validation

The next step in the data wrangling process is to validate the data to ensure its accuracy and reliability. Ensuring accuracy and reliability involves checking the data against predefined criteria to verify its validity. 

Say, for example, you were analyzing a list of email addresses. Data validation checks you could use to test the data include:

• Format Validation: Ensure that the email address provided follows the correct format for an email address.
• Domain Validation: Check if the domain of the email address is a real and active domain by performing a DNS lookup on the domain.
• Blacklist Check: Check if the email address is on a blacklist of known disposable email services or known spammers.
• Length and Character Limit: Ensure the email address isn’t too long and doesn’t contain illegal characters.

6. Data Publishing

Once the data has been collected, cleaned, transformed, enriched, and validated, the final step is to publish it. This involves making the data available for analysis or reporting. Data publishing can take various forms, such as creating dashboards, reports, or other visualizations that make the data easy to understand and interpret. The goal is to ensure that the data is accessible and usable for decision-makers and other stakeholders.

Data Wrangling Techniques

Data wrangling involves employing a range of techniques to transform raw data into a usable format. These techniques include:

• Data Filtering involves sifting through data to remove any irrelevant or unnecessary information. This step is crucial as it ensures that the dataset only contains pertinent data, making the analysis process more straightforward and accurate.
• Data imputation is the process of replacing missing or null values with substitute values. This is important because missing data can lead to inaccurate analyses and biased results. There are several imputation methods available, such as mean imputation, where missing values are replaced with the mean value of the variable.
• Data encoding and decoding involve converting data from one form to another. For example, categorical data, which might include variables like color or gender, can be converted into numerical format to facilitate analysis. This step is essential, as most analysis methods require numerical input.
• Regular expressions are sequences of characters that are used as a search pattern. They can be used to extract information from text data, such as emails or phone numbers, or to find and replace specific patterns in a dataset. This technique is powerful for cleaning and organizing textual data.
• Data aggregation involves summarizing and grouping data to provide a more comprehensive view of a dataset. For example, data can be aggregated to calculate averages, sums, or counts. This technique is useful for identifying trends and patterns in a dataset, facilitating more informed decision-making.

Key Data Wrangling Skills

Data wrangling requires a unique set of skills and competencies to effectively transform raw data into a clean, usable format. Here are some of the key skills and competencies that are essential for successful data wrangling:

Technical Skills

• Programming: A strong grasp of programming languages such as Python or R is crucial, as these are often used for data wrangling tasks.
• Database Management: Familiarity with database management tools like SQL or MongoDB is important for querying and manipulating data.
• Data Visualization: Knowledge of data visualization tools such as Tableau or Power BI can be helpful for creating visual representations of data.
• Anomaly Detection: The ability to identify and address anomalies in data is an essential skill in ensuring the quality and accuracy of the dataset. This involves detecting outliers or unusual patterns that may indicate errors or other issues.

Analytical Skills

• Critical Thinking: The ability to think critically and ask the right questions is important for understanding the data and the context in which it will be used.
• Problem-Solving: Data wrangling often involves solving complex challenges, so strong problem-solving skills are essential.
• Attention to Detail: A keen eye for detail is necessary to spot errors and inconsistencies in the data.

Communication Skills

• Clear Communication: The ability to communicate clearly and effectively is important, as data wranglers often need to explain their findings to non-technical stakeholders.
• Data Storytelling: The ability to tell a story with data is crucial, as it helps to make the data more accessible and understandable.

Other Important Competencies

• Domain Knowledge: Understanding the specific industry or domain is important, as it helps to provide context for the data.
• Adaptability: The world of data is constantly evolving, so the ability to adapt to new tools and technologies is crucial.
• Collaboration: Data wrangling is often a collaborative process, so the ability to work effectively with others is important.

Preparing for a Data-Driven Future

The world is witnessing exponential growth in data, with an estimated 328.77 million terabytes of data being generated every day. This growing ecosystem of data presents tremendous opportunities for businesses to extract valuable insights and make informed decisions. However, it also highlights the growing need for skilled professionals who can wrangle this vast sea of data effectively.

To meet this need, businesses will need to simultaneously level up their tech hiring strategies and invest in upskilling their teams. Even as we begin to see greater integration of automated tools and artificial intelligence in the data wrangling process, the human element will remain indispensable. Investing in the development of data wrangling skills and tools has become necessary step to future-proof your organization. 

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Amid the pantheon of programming languages, R holds a special place. While it may not boast the broad-based appeal of Python or the legacy status of Java, R has carved out a unique niche.

R’s domain? Data science and statistical computing. Developed by statisticians, for statisticians, R brings together an unmatched suite of tools for data manipulation, analysis, and visualization. This makes R an exceptional asset for professionals in fields that need to make sense of data, from business and healthcare to research and beyond.

Unveiling the strength of R isn’t merely about expanding technical knowledge. It’s about appreciating the transformative power of a language specifically designed to unlock insights from data. In this blog post, we’ll dive into the nuts and bolts of R, unraveling its origins, exploring its key features and advantages, and understanding its pivotal role in the realm of data science. By the end of this guide, you’ll have a solid understanding of this potent, yet often under-appreciated, language and be one step closer to harnessing its power. 

What is R?

Often referred to as the “lingua franca of statistics,” R is an open-source programming language and environment designed specifically for statistical computing and graphics. R was conceived in the early 90s by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand. It was their love for crafting a solution to handle data analysis with ease and precision that led to the birth of R.

R’s name itself is a clever nod to its origins, named after the first letters of its creators’ names, Ross and Robert. It’s also a playful homage to the S programming language, from which R draws much of its inspiration.

At its core, R is a reinterpretation of the S language — the pioneering language of statistical computing. But it’s not just a carbon copy. R enhances many aspects of S and adds its functionality, particularly in the area of data visualization, making it more adept at dealing with the complex, data-heavy tasks of today’s world.

The development of R was like throwing a pebble into a pond. The ripples it created extended far and wide, leading to the establishment of the Comprehensive R Archive Network (CRAN) in 1997. Today, CRAN serves as a vast repository of R packages contributed by developers worldwide, continually expanding R’s capabilities.

So, what started as a statistical language in the academic world has now evolved into a leading tool in data analysis and visualization, widely adopted across numerous industries. In the following sections, we’ll look at why R has gained such popularity and how it stands out in the programming world.

Key Features of R

R isn’t your average programming language. It’s a multifaceted tool tailored for data manipulation, analysis, and visualization. Here are some of the features that make R a standout choice for professionals working with data:

• Open-Source and Cross-Platform: Like many of its contemporary languages, R is open source, meaning its source code is freely available. This openness allows for continuous enhancements by its community of dedicated users and developers. Additionally, R is cross-platform, meaning it works smoothly on different operating systems, including Windows, Mac OS, and Linux.
• Powerful Statistical Analysis: R is a force to be reckoned with when it comes to statistical analysis. From simple descriptive statistics to advanced machine learning algorithms, R can handle it all. It’s like having a powerful calculator that can perform a myriad of statistical tasks.
• Data Visualization Capabilities: In data science, a picture is worth a thousand data points. R excels in creating high-quality, customizable graphics. Packages like ggplot2 offer advanced visualization capabilities, helping users create complex multi-layered graphs with relative ease.
• Comprehensive Packages: R’s power lies in its vast ecosystem of packages. The CRAN repository currently hosts over 15,000 packages, offering a diverse range of functions, from data manipulation to bioinformatics, web scraping to natural language processing, and much more.
• Reproducibility: R is highly favored in academic and research circles for its reproducibility. Researchers can bundle their data, statistical code, and the output of their analysis into a single document using R Markdown. This makes it easier for others to verify their results and fosters more transparent and reliable research.

Advantages of R

Understanding R’s features gives us a glimpse of its capabilities, but why exactly should someone choose R over other programming languages? Here’s a closer look at some of the advantages that R brings to the table:

• Unrivaled Support for Data Science and Statistics: Born from the world of statistics, R has a natural affinity for data science. It offers a wide array of statistical tests, model-building tools, and machine learning algorithms, making it a go-to language for data scientists and statisticians. Whether it’s data cleaning, exploration, visualization, or deploying complex models, R is designed to handle these tasks effortlessly.
• Wide Community and Support: With a large and active global community, finding support for R is never a problem. There are numerous online forums, tutorials, and resources to help you troubleshoot, learn new techniques, and stay updated with the latest developments.
• Compatibility and Interoperability: One of R’s strengths is its ability to play well with others. R can easily interface with other languages like C, C++, and Python. This flexibility makes R a great companion language that can fit seamlessly into a variety of workflows.
• Continuous Improvements and Updates: The open-source nature of R means that it is continuously being improved and updated by its user community. New packages are regularly added to CRAN, expanding R’s capabilities and keeping it at the forefront of statistical programming and data science.

Use Cases & Applications of R

R’s versatile nature allows it to be used across a myriad of industries. Let’s explore a few of these applications:

• Healthcare: In the healthcare sector, R is used to analyze patient data, predict disease outbreaks, and even identify patterns in DNA sequences. For instance, hospitals can use R to build models that predict patient readmission rates, helping to improve patient care and reduce costs.
• Finance: R’s powerful statistical capabilities make it a popular choice in the finance industry. It’s used for tasks like predicting stock prices, evaluating investment risks, and optimizing portfolios. By providing robust quantitative analysis, R helps financial analysts make informed decisions.
• Academia: Academic researchers heavily rely on R for a variety of tasks, from analyzing experimental data to testing new statistical methods. Its emphasis on reproducibility makes R an excellent tool for scholarly research, fostering transparency and reliability in academic findings.
• Tech Industry: In the tech world, R is utilized for machine learning, web analytics, A/B testing, and much more. Companies like Google and Facebook use R to understand user behavior and improve their services. 

The Current Hiring Outlook for R Programmers

Understanding the R language is more than just an academic exercise. It’s a skill that’s in high demand in today’s job market. Let’s unpack the current hiring landscape for those armed with R skills:

Why Businesses Are Looking for R Skills 

Given the importance of data-driven decision-making in today’s business landscape, companies are on the hunt for professionals who can turn data into insights. With its powerful data handling and statistical analysis capabilities, R has become a desirable skill for data analysts, data scientists, and statisticians.

Average Salary and Job Demand 

As per recent job market trends, the average salary for R programmers is quite competitive, often exceeding that of other programming languages. According to ZipRecruiter, the average salary of an R developer in 2023 is $123,147 per year. Demand for R programmers has also seen a steady rise — growing by 60 percent in 2022 — reflecting the increasing importance of data analysis and machine learning in various industries. 

The Significance of R in Hiring and Talent Acquisition

For hiring managers, candidates with R skills represent a valuable asset. These candidates are equipped to handle complex data tasks and derive actionable insights while brining a strong foundation in statistics. For job seekers, having R in your skillset is a powerful differentiator. It’s a clear indicator of your ability to tackle data challenges head-on and provide data-driven solutions.

Key Takeaways

As we wrap up this exploration of R, it’s clear that this language isn’t just a tool — it’s a powerhouse for data analysis, a cornerstone for statistical computing, and an essential skill in the evolving field of data science. Its versatile applications touch numerous industries, from healthcare and finance to academia and the tech sector.

And as we look to the future, the importance of R only seems to be growing. With an ever-increasing amount of data at our disposal, the need for capable, comprehensive tools to analyze and draw insights from this data is paramount. R, with its impressive suite of capabilities, is uniquely positioned to meet these challenges head-on.

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The post What Is R? A Guide to the Lingua Franca of Statistics appeared first on HackerRank Blog.

In the world of tech, the only constant is change, and this is especially true within the realm of data science. This discipline evolves at such a lightning pace that what was cutting-edge a few years ago is considered commonplace — or even antiquated — today. In fact, according to the World Economic Forum, 50% of all employees will need reskilling by 2025 as the adoption of technology increases.

As a tech leader, hiring manager, or recruiter, it’s important to not just hire for the right skills — particularly at a time when 60% of hiring managers say data science and analytics roles are the toughest to hire for. It’s also critical to continuously invest in your team’s development. It’s not about playing catch-up with the latest tech trend but about staying on the wave of evolution, ready to ride its crest. 

In 2023, upskilling your data science team isn’t just a nice-to-have but a need-to-have strategy. The benefits of this upskilling strategy are multifold: not only does it future-proof your organization but it also increases your team’s productivity, lowers turnover, and helps maintain a competitive edge in the market.

So, whether you’re hoping to dive deeper into machine learning, harness the latest in artificial intelligence, or make the most of data visualization tools, this blog post is your guide to upskilling your data science team effectively and efficiently. With a strong upskilling strategy, your data science team will be prepared to navigate the future of this exciting, fast-paced industry for years to come.

Why You Should Upskill Your Data Science Team

According to the U.S. Bureau of Labor Statistics, data science jobs are expected to grow at a rate of 36% between now and 2031 — far faster than the 5% average growth rate for all occupations. This rapid rise in demand is also creating a shortage of data science talent, making upskilling an increasingly appealing — and necessary — strategy. But its benefits extend beyond simply filling in the skills gap. 

Firstly, upskilling increases productivity. An up-to-date, well-equipped data scientist will be more efficient, better able to troubleshoot issues, and more likely to find innovative solutions. It’s simple – if your team has a better understanding of the tools at their disposal, they will be more effective at their jobs. 

Secondly, investing in your team’s growth can also have a positive impact on employee satisfaction and retention. A LinkedIn report shows that 94% of employees would stay at a company longer if it invested in their learning and development. Upskilling gives your data scientists a sense of professional progression and satisfaction, which translates to a more committed and stable team.

Lastly, but importantly, upskilling keeps you competitive. The field of data science is racing ahead, with advancements in AI, machine learning, and big data analytics becoming commonplace. Businesses not only need to keep up, but they also need to be ready to leverage these advancements. A data science team that is proficient in the latest technologies and methodologies is a huge competitive advantage.

In essence, upskilling your data science team is about more than just learning new skills. It’s about fostering a culture of continuous growth and learning, which enhances your team’s capabilities, morale, and ultimately, your organization’s bottom line.

Determining the Skills Gap

Before you can effectively upskill your data science team, you need to identify your skills gaps. This involves both a high-level overview of your team’s capabilities and a deep dive into individual competencies.

Start by reviewing your current projects and pipelines. What are the common bottlenecks? Where do the most challenges or errors occur? Answers to these questions can shed light on areas that need improvement. For instance, if your team frequently encounters difficulties with data cleaning and preprocessing, it may be beneficial to focus on upskilling in this area.

Next, look at the individual members of your team. Everyone has their own unique set of strengths and weaknesses. Some may be fantastic with algorithms but could improve their communication skills. Others might be proficient in Python but not as adept with R. You can identify these individual skill gaps through regular performance reviews, one-on-one check-ins, or even anonymous surveys. 

Remember, the goal here is not to criticize or find fault but to identify opportunities for growth. The process of determining the skills gap should be collaborative and constructive and should empower team members to take ownership of their professional development.

Once you have a clear picture of the skills gaps in your team, you can start to strategize about the most effective ways to bridge these gaps. Whether it’s through online courses, in-house training, or peer-to-peer learning, the key is to create a supportive environment that encourages continuous learning and improvement.

Key Skills to Invest in 2023

With a clear understanding of where your team stands, let’s now focus on the pivotal data science skills that your team should be honing in 2023.

• Advanced Machine Learning and AI: Machine learning and AI continue to dominate the data science field, with technologies becoming more advanced and integrated into a myriad of applications. Upskilling in areas like deep learning, reinforcement learning, neural netorks, and natural language processing can give your team a significant advantage.
• Cloud Computing: With the increasing amount of data being generated, cloud platforms like AWS, Azure, and Google Cloud are becoming increasingly essential. Cloud computing skills can enable your team to handle large datasets more efficiently and perform complex computations without heavy investment in infrastructure.
• Data Visualization: The ability to communicate complex results through intuitive visuals is crucial. Tools like Tableau, PowerBI, and Python libraries such as Matplotlib and Seaborn are continually evolving. Therefore, keeping up to date with these tools can help your team better communicate their findings and make data-driven decisions more accessible to stakeholders.
• Ethics in AI and Data Science: As AI and data science technologies become more advanced and pervasive, ethical considerations become even more critical. Understanding bias in datasets, privacy issues, and the ethical implications of AI decisions will be an important skill for the foreseeable future.
• Communication and Storytelling: A great data scientist isn’t just someone who can crunch numbers but someone who can explain what those numbers mean. Good storytelling helps translate the complex into the understandable, turning raw data into actionable insights. In 2023, soft skills like communication and storytelling continue to be in high demand alongside technical expertise.

While the technical skills needed can vary depending on your industry and specific company needs, these are areas that are becoming universally important in data science. Providing opportunities to upskill in these areas can ensure your team remains adaptable and ready to tackle the future of data science.

Upskilling Strategies

Now that we’ve highlighted the importance of upskilling and outlined the key skills to invest in for 2023, let’s discuss some effective strategies to upskill your data science team.

• Online Courses and Certifications: The internet is a treasure trove of learning resources, with platforms like Coursera, edX, and Udacity offering specialized courses in data science. These platforms offer up-to-date courses in partnership with leading universities and tech companies, ensuring your team gets quality and relevant learning materials. Encouraging your team to pursue relevant certifications can be a great way to upskill.
• Mentoring and Peer Learning: Internal mentoring programs where less experienced team members learn from their more experienced colleagues can be an effective way to transfer knowledge and skills. Similarly, encouraging peer learning — perhaps through coding challenges or pair programming sessions — can foster a healthy learning culture within your team.
• In-house Workshops and Seminars: Organizing in-house workshops on critical topics can be another excellent way to upskill your team. These can be led by team members who have a strong grasp of a particular area or by external experts. Regular seminars keep the team updated about the latest trends and advancements in data science.
• Participation in Data Science Communities and Forums: Online communities like Kaggle, GitHub, or Stack Overflow are places where data scientists from all over the world share their knowledge and learn from each other. Encouraging your team to participate in these communities can expose them to a diverse range of problems, solutions, and innovative ideas.

Remember, the goal of these strategies is not just to teach your team new skills but also to cultivate a culture of continuous learning. When your team sees upskilling as a valuable, ongoing process rather than a one-time task, they’ll be better equipped to keep up with the rapidly changing field of data science.

Measuring Success and Tracking Progress

With the strategies in place and the team ready to plunge into upskilling, the next important step is to track the progress of these initiatives. How do you know if your upskilling efforts are effective? Here are some ways to measure success:

• Improvement in Project Outcomes: As your team members start applying their newly acquired skills, you should observe noticeable improvements in the quality of work and efficiency. It could be faster processing times, more accurate models, or clearer data visualizations.
• Increased Efficiency: Upskilling should make your team more autonomous and efficient. This can look like bringing tasks in-house that were previously outsourced or realizing efficiency gains in tasks that once took a long time. 
• Feedback from Team Members: Regularly check in with your team. Are they finding the upskilling initiatives useful? How do they feel about their progress? Their feedback can provide valuable insights into what’s working and what needs improvement. 
• Skill Assessments: Regular skill assessments can help measure the level of improvement in the specific skills targeted by the upskilling initiative. This can be done through quizzes, presentations, or project-based assessments.
• Retention Rates: As mentioned earlier, employees are likely to stick around longer if they feel the company is investing in their growth. So, consider monitoring turnover rates before and after implementing the upskilling initiatives. A decrease in turnover can be a good indication that your upskilling efforts are successful.

Remember, the goal of tracking progress is not to introduce a punitive or high-pressure environment but to better understand how the team is evolving. Celebrate the wins, and take the challenges as opportunities to refine your upskilling strategy. The journey to upskilling your data science team is iterative and adaptive, just like the data science discipline itself.

Preparing for the Future With Upskilling

Navigating the ever-changing landscape of data science might seem daunting, but with a systematic approach to upskilling, your team will be ready to not only weather the storm but also ride the waves of change.

Upskilling your data science team isn’t just about staying current — it’s about looking ahead and being prepared for what’s coming. It’s about creating a team that’s resilient, adaptable, and always ready to learn. It’s about setting the pace, not just keeping up with it. 

So, as a tech leader, recruiter, or hiring manager, remember that the key to a successful data science team lies not just in hiring the right people but also in continuously investing in their growth. Provide them with the tools, resources, and opportunities to learn and improve, and you’ll have a team that’s not just prepared for the year ahead, but also for the many exciting developments that lie beyond.

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With 328.77 million terabytes of data being created each day, harnessing the power of data has become more crucial than ever. Once a distinct competitive advantage, unlocking the secrets hidden within this data is now a business imperative. The fingerprints of data science are everywhere in the tech we see today, from online ads to the navigation apps we rely on to show us the best route to our destination. But what exactly is the magic behind data science? And what makes it so indispensable? 

Simply put, data science is the process of extracting actionable insights from raw data. It’s a discipline that uses a variety tools, algorithms, and principles aimed at finding hidden patterns within the troves of data we produce daily. And it’s the driving force behind technologies like artificial intelligence and machine learning.

Whether you’re an experienced hiring manager or a budding data enthusiast, this article will give you a glimpse into the real-life applications of data science. Instead of an abstract, hard-to-grasp concept, we’ll see data science in action, breathing life into various industries, shaping our world, and quietly revolutionizing the way we do business. 

Banking and Finance

Data science has become an invaluable asset in the banking and finance sector, allowing companies to refine risk models, improve decision-making, and prevent fraudulent activities. With the increasing complexity and volume of financial data, data science tools help these companies dig deep to unearth actionable insights and predict trends. Let’s take a look at how it’s done in practice.

Fraud Prevention

American Express (Amex) has been making effective use of AI and machine learning (ML) to tackle an increasingly sophisticated form of credit card fraud: account login fraud. Amex developed an end-to-end ML modeling solution that assesses risk at the point of account login, predicting whether the login is genuine or fraudulent. High-risk logins are subjected to further authentication, while low-risk logins enjoy a smooth online experience. This real-time fraud detection model leverages vast amounts of customer data, assessing the most recent information, and continually calibrating itself. The success of this predictive model has been marked by a significant decrease in fraud rates over time, making it more effective than most other third-party models in the marketplace.

Automated Trading 

High-frequency trading firms, like Renaissance Technologies and Citadel, utilize data science to automate trading decisions. They process large volumes of real-time trading data, applying complex algorithms to execute trades at high speeds. This allows them to capitalize on minor price differences that may only exist for a fraction of a second, creating an advantage that wasn’t possible before the advent of data science.

Gaming

The gaming industry, one of the most data-intensive sectors, is reaping the benefits of data science in an array of applications. From understanding player behavior to enhancing game development, data science has emerged as a key player. With its predictive analytics and machine learning capabilities, data science has paved the way for customized gaming experiences and effective fraud detection systems. Let’s examine how the gaming giants are leveraging this technology.

Player Behavior Analysis

Electronic Arts (EA), the company behind popular games like FIFA and Battlefield, uses data science to comprehend and predict player behavior. They collect and analyze in-game data to understand player engagement, identify elements that players find most compelling, and tailor their games accordingly. This data-driven approach not only improves player satisfaction but also boosts player retention rates.

Game Recommendations 

Steam, the largest digital distribution platform for PC gaming, utilizes data science to power its recommendation engine. The platform analyzes players’ past behavior and preferences to suggest games they might enjoy. This personalized approach enhances the user experience, increases engagement, and drives sales on the platform.

Cheating Prevention

Riot Games, the creator of the widely popular game League of Legends, deploys data science to detect and prevent cheating. Their machine learning models analyze player behavior to identify anomalous patterns that could indicate cheating or exploitation. This not only maintains a fair gaming environment but also preserves the integrity of the game.

Retail

The retail sector is another industry where data science has made significant strides. It has transformed the way businesses manage their supply chains, predict trends, and understand their customers. From optimizing product placement to forecasting sales, data science is giving retailers the insights they need to stay competitive. Here are a few examples of how data science is reshaping the retail industry.

Real-Time Pricing

OTTO, a leading online retailer in Germany, has effectively implemented dynamic pricing to manage and optimize the prices of its vast array of products on a daily basis. Leveraging machine learning models, including OLS Regression, XGBoost, and LightGBM, OTTO predicts sales volume at different price points to ensure efficient stock clearance and maintain profitability. Their cloud-based infrastructure, developed to handle the computational load, allows for the price optimization of roughly one million articles daily. This innovative application of data science has enabled OTTO to significantly increase its pricing capacity, delivering up to 4.7 million prices per week.

In-Store Analytics

Amazon’s physical retail and technology team recently introduced Store Analytics, a service providing brands with anonymized, aggregated insights about the performance of their products, promotions, and ad campaigns in Amazon Go and Amazon Fresh stores in the U.S. enabled with Just Walk Out technology and Amazon Dash Cart. These insights aim to improve the shopper experience by refining store layout, product selection, availability, and the relevance of promotions and advertising. Brands gain access to data about how their products are discovered, considered, and purchased, which can inform their decisions about product assortment, merchandising, and advertising strategies. 

Healthcare

Harnessing the power of data science, the healthcare industry is taking bold strides into previously uncharted territory. From rapid disease detection to meticulously tailored treatment plans, the profound impact of data science in reshaping healthcare is becoming increasingly apparent.

Disease Detection

Google’s DeepMind, a remarkable testament to the capabilities of AI, has made significant inroads in disease detection. This system, honed by thousands of anonymized eye scans, identifies over 50 different eye diseases with 94% accuracy. More than just a detection tool, DeepMind also suggests treatment referrals, prioritizing cases based on their urgency.

Personalized Medicine

Roche’s Apollo platform, built on Amazon Web Services (AWS), revolutionizes personalized healthcare by aggregating diverse health datasets to create comprehensive patient profiles. The platform has three modules: Data, Analytics, and Collaborations. With it, processing and analysis times for data sets have been dramatically reduced, facilitating scientific collaboration and expanding the use of AI in Roche’s R&D efforts. In the future, Roche plans to add new machine learning capabilities and initiate crowdsourcing for image data annotation.

Social Media

In the hyper-connected landscape of social media, data science is the force behind the scenes, driving everything from trend prediction to targeted advertising. The explosion of user-generated data provides an opportunity for deep insights into user behavior, preferences, and engagement patterns. Data science is key to deciphering these massive data sets and propelling the strategic decisions that make social media platforms tick.

Trend Identification

Twitter uses data science, specifically sentiment analysis, to uncover trending topics and gauge public sentiment. By analyzing billions of tweets, Twitter can identify patterns, topics, and attitudes, giving a real-time pulse of public opinion. This data is valuable not only for users but also for businesses, governments, and researchers who can use it to understand public sentiment toward a product, policy, or event. However, it’s worth noting that earlier this year, Twitter shut down access to its free API, which gives people access to its platform data, causing panic among both researchers and businesses that rely on Twitter data for their work.

Ad Targeting

Facebook leverages the power of data science for personalized ad targeting, making advertising more relevant and effective for its users and advertisers alike. By using machine learning algorithms to analyze user data — likes, shares, search history, and more — Facebook predicts user preferences and interests, allowing advertisers to tailor their ads to target specific demographics. The result is a more personalized, engaging experience for users and a more successful, profitable platform for advertisers.

Transport and Logistics

As we zoom into the bustling world of transportation and logistics, we find data science playing a crucial role in streamlining operations, reducing costs, and enhancing customer experiences. From predicting demand to optimizing routes, data science tools and techniques allow for smarter decision making and improved efficiencies.

Route Optimization

Uber’s groundbreaking business model would not have been possible without the powerful capabilities of data science. For instance, Uber uses predictive analytics to anticipate demand surges and dynamically adjust prices accordingly. Additionally, data science helps in optimizing routes for drivers, ensuring quicker pickups and drop-offs, and an overall smoother ride for the customer.

Supply Chain Optimization

Global logistics leader DHL uses data science for efficient logistics planning. By analyzing a vast array of data points such as transport times, traffic data, and weather patterns, DHL can optimize supply chain processes and reduce delivery times. This data-driven approach allows DHL to manage its resources more efficiently, saving costs, and improving customer satisfaction.

Energy

The energy sector stands to gain immensely from the incorporation of data science. From optimizing power generation and consumption to enabling predictive maintenance of equipment, data science is transforming how we produce and consume energy. The intelligence gleaned from data is helping companies reduce their carbon footprint, boost operational efficiency, and generate innovative solutions.

Optimizing Power Distribution

Siemens, a global leader in energy technology, is leveraging data science to optimize power distribution through their Smart Grid solutions. By collecting and analyzing data from various sources, including sensors, smart meters, and weather forecasts, Siemens can predict and manage energy demand more effectively. This enables utilities to balance supply and demand, optimize grid operations, and reduce wastage. The integration of data science into the energy grid allows for greater reliability, efficiency, and sustainability in power distribution systems.

Predictive Maintenance

General Electric (GE) is another prime example of a company harnessing the power of data science in the energy sector. Their wind turbines are embedded with sensors that collect data to be analyzed for predictive maintenance. Through advanced algorithms, GE can predict potential failures and schedule maintenance in advance. This proactive approach not only prevents expensive repairs and downtime, but it also extends the life expectancy of their equipment, providing a significant boost to efficiency and profitability.

The Transformative Power of Data Science

As you can see, data science has become an indispensable tool across various industries, revolutionizing the way businesses operate and making significant advancements possible. The application of data science techniques, such as predictive analytics, personalization, and recommendation systems, has enabled organizations to make data-driven decisions, improve operational efficiency, enhance customer experiences, and drive innovation. As we look to the future, the potential for data science applications continues to expand, promising even more transformative outcomes in the industries we discussed here — and beyond. 

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Data is growing at an unprecedented rate, with 328.77 million terabytes of data being created every single day. Businesses have more data at their fingertips than ever before, but harnessing that data is rarely a simple task. The challenge that arises for businesses and organizations is not just to gather this data, but to make sense of it, to unravel the stories hidden beneath the numbers. That’s where data visualization comes into play.

Leaders need to decipher complex data and act quickly, and that’s where the power of data visualization shines. Data visualization serves as our map in the vast landscape of data, guiding us to insights that could easily get lost in the rows and columns of raw data sets. It helps businesses understand their performance, customers, and market, and aids in predicting future trends. 

As data visualization continues to play a key role in business decision-making, the demand for professionals such as data scientists is only expected to grow. By understanding how data visualization is changing the face of data analysis and how it can be used to create compelling data narratives, businesses and data professionals alike can stay ahead of the curve and build the skills and teams needed to thrive in a data-driven future. 

What is Data Visualization?

Data visualization is the graphical representation of data and information. Using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. It’s a bit like translating numbers into a language we can all intuitively understand — the language of visuals.

For instance, imagine you’ve got a spreadsheet in front of you with thousands of rows of data on global climate change. You could spend hours poring over it, or you could plot it onto a world map that color codes each region based on the increase in temperature. Which do you think would help you — and others — understand the data better and faster? 

Why is Data Visualization Important?

In our data-saturated world, the ability to translate complex datasets into digestible, understandable, and actionable visuals is critical. It enhances the comprehensibility of data and enables decision makers to see analytics visually, understand complicated concepts, and identify new patterns that might go unnoticed in text-based data.

Data visualization isn’t just a pretty way to see data; it’s a way to bring data to life and tell its story. It can help show how things have changed over time, how variables interact with each other, and how certain factors could potentially affect future outcomes. 

Who Uses Data Visualization?

Almost everyone in an organization can benefit from data visualization. From top-level executives looking for industry trends to make strategic decisions, to marketing teams analyzing campaign results, to IT departments tracking software performance, data visualization can deliver insights for all. 

In particular, data scientists, data analysts, and statisticians often use data visualization to check the quality of their data and to explore it for patterns, trends, relationships, and anomalies. It’s an integral part of their workflow — a means to “speak” data more eloquently.

Use Cases for Data Visualization

Data visualization can transform raw data into a form that’s easier to understand and interpret, making it a powerful tool for anyone looking to extract insights from their data. Let’s look at a few of the ways in which data visualization is commonly used.

Tracking Changes Over Time

One of the most common uses of data visualization is to track changes over time. Line graphs are particularly effective for this purpose. For instance, if a business wants to monitor their sales performance, a line graph showing monthly sales over several years can help identify patterns, trends, and potential anomalies.

Comparing Categories of Data

Comparing different categories or groups of data is another common use of data visualization. Bar charts and pie charts are often used for this purpose. Suppose a company wants to understand its market share. A pie chart can illustrate the company’s share of the market compared to its competitors, providing a clear picture of its competitive landscape.

Identifying Relationships Between Variables

Data visualization can also be used to identify relationships or correlations between different variables. Scatter plots are typically used for this purpose. For example, a marketing team may want to understand if there’s a relationship between advertising spend and website traffic. A scatter plot could help visualize any correlation.

Highlighting Patterns and Trends

Data visualization can help highlight patterns, trends, or anomalies that may not be immediately apparent in raw, tabulated data. Heat maps are a great way to visualize complex datasets, and can be particularly useful when trying to identify patterns or correlations.

Communicating Insights

Finally, one of the most important uses of data visualization is to communicate findings and insights to others, especially those who may not be data experts. A well-designed, clear visualization can tell a story about the data, making the insights it contains accessible to a wide audience.

Remember, the key to effective data visualization is to choose the right kind of visualization for your data and what you want to communicate. Not every visualization works for every type of data, so it’s important to understand your data and your goals before deciding how to visualize it.

Data Visualization Techniques

When it comes to data visualization, there isn’t a one-size-fits-all approach. The technique you choose depends on what you want to communicate. Here are a few common techniques:

• Charts and Graphs: These are the most common techniques used. Line charts are perfect for showing changes over time. Bar graphs compare different groups, while pie charts show parts of a whole.
• Heat Maps: Heat maps use color intensity to represent different values. These are particularly useful when you want to show patterns or correlations within large data sets, like user behavior on a website or geographical data.
• Scatter Plots: Scatter plots show the relationship between two numerical variables and are often used to identify trends, correlations, and outliers within a data set.
• Box Plots: Box plots are great for statistical studies, especially when you want to compare data across categories and identify outliers or patterns.
• Geospatial Visualization: Geospatial visualization, or map-based visualization, is used when geographic data is crucial to the story the data tells. For example, tracking disease outbreaks or visualizing demographic data.
• Interactive Dashboards: Interactive dashboards compile multiple visualizations onto a single screen, allowing users to interact with the data, change variables, and see the impact in real time.

Data Visualization Tools and Technologies

The tools you choose for data visualization can significantly affect your ability to interpret the data and extract insights. Some technologies offer a robust set of out-of-the-box tools for data visualization, while others require experience coding in languages like Python or JavaScript. Here are some of the most popular data visualization tools:

Tableau

Tableau is widely recognized for its intuitive drag-and-drop interface and its ability to create interactive dashboards quickly. It allows you to work with data from numerous sources, from Excel spreadsheets to cloud-based databases. You can then turn this data into comprehensive visualizations and even combine them into interactive dashboards.

PowerBI

Microsoft’s PowerBI is a tool that integrates seamlessly with other Microsoft products, making it an excellent choice for businesses already operating in a Microsoft environment. Like Tableau, PowerBI supports a wide variety of data sources and offers robust features for creating interactive reports and dashboards.

Matplotlib & Seaborn

For those comfortable with coding, Matplotlib is a versatile Python library for creating static, animated, and interactive visualizations. Seaborn is another Python library built on top of Matplotlib that provides a high-level interface for drawing attractive and informative statistical graphics.

D3.js

For web-based visualizations, D3.js is hard to beat. This JavaScript library gives you the tools to create sophisticated, custom visualizations that can interact with web users. However, D3.js is not for beginners — it requires a solid knowledge of JavaScript, which can make the learning curve steeper than for other visualization tools.

Qlik Sense

Qlik Sense is known for its responsive design and touch interaction, making it an excellent choice for organizations that want to create visualizations accessible on various devices. It’s also praised for its “associative model” that helps users find unexpected insights.

Wrapping Up and Looking Ahead

As we move into an era where data is increasingly voluminous and complex, the role of data visualization is set to grow in scope and significance. It’s not just about providing clarity to the here and now; it’s also about pioneering the exploration of the unseen and the uncharted territories of data.

The future of data visualization is as dynamic as the data it seeks to represent. Technological advancements and innovations will continue to shape its course. As machine learning and artificial intelligence technologies evolve, we can expect to see more advanced, automated, and insightful visualization techniques. These innovations will enable us to not only visualize and understand data at unprecedented scales but also uncover patterns and insights that would be impossible to detect otherwise.

Emerging fields such as augmented and virtual reality also offer intriguing possibilities for data visualization, potentially allowing us to explore data in a fully immersive 3D environment, where complex data sets can be investigated from every angle, literally bringing the data to life.

But as much as data visualization is about technology, it’s equally about people. As the volume and complexity of data grow, so does the need for skills to interpret and communicate that data effectively. The data scientists, analysts, and storytellers of the future will need to master the art and science of data visualization to ensure data can be understood, decisions can be made, and innovation can be driven.

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The post What Is Data Visualization? The Art and Science of Seeing Data appeared first on HackerRank Blog.

From large language models to AI-power climate science, the data science field is producing more diverse and exciting innovations than ever before. In the coming decade, data science will transform entire societies, governments, and global economies. Even the next evolution of humanity is in the works.

But as the possibilities of data science have expanded, so have the skills necessary to succeed as a data scientist. Top data scientists will need to balance their skill set between exciting new disciplines like natural language processing while maintaining traditional skills like statistics and database management. 

Artificial Intelligence

Artificial intelligence (AI) is the ability of a digital computer to perform tasks associated with intelligent beings. 

First theorized by Alan Turing in 1950, artificial intelligence (AI) has become a fast evolving discipline behind the world’s most innovative technologies. While AI has been around for decades, we’ve only begun to unlock its potential. Some experts believe AI is poised to usher in the next era of human civilization, with Google CEO Sundar Pichai comparing the advancement of AI to the discovery of fire and electricity. Given the nearly endless number of potential applications—including cancer treatment, space exploration, and self-driving cars—the tech industry’s need for data scientists with AI skills is vast.

AI is a complex and evolving field with numerous branches and specializations.

Natural Language Processing

Natural language processing (NLP) is the branch of AI focused on training computers to understand language the way human beings do. Because NLP requires massive quantities of data, data scientists play a significant role in this advancing field. With applications including fake news detection and cyberbullying prevention, NLP is among the most promising trends in data science.

Machine Learning

Machine learning is the use and development of computer systems that are able to learn and adapt without following explicit instructions. Machine learning algorithms are dependent on human intervention and structure data to learn and improve their accuracy. Data scientists build machine learning algorithms using programming frameworks such as TensorFlow and PyTorch. 

Deep Learning

Deep learning is a sub-field of machine learning characterized by scalability, consumption of larger data sets, and a reduced need for human intervention.

The launch of ChatGPT in late 2022 was a pivotal moment for deep learning, giving consumers their first hands-on exposure to the potential of the discipline. With applications including autonomous vehicles, investment modeling, and vocal AI, deep learning is an exciting field of artificial intelligence.

Deep learning frameworks that data scientists use include TensorFlow, PyTorch, and Keras.

Database Management

Database management is the process of organizing, storing, and retrieving data on a computer system. Data scientists use database management to cultivate and interpret data.

Database skills can be divided into two different categories. The type of databases data scientists work with will vary depending on their specialization or the needs of a given project.

Relational databases use structured relationships to store information. Data scientists use the programming language SQL to create, access, and maintain relational databases. Relational database tools include SQL Server Management Studio, dbForge SQL Tools, Visual Studio Editor, ApexSQL.

Non-relational databases store data using a flexible, non-tabular format. Also known as NoSQL databases, non-relational databases can use other query languages and constructs to query data. Non-relational database tools include mongoDB, Cassandra, ElasticSearch, Amazon DynamoDB.

Data Wrangling

Before business can make data-driven decisions, data scientists have to detect, correct, and remove flawed data. Data wrangling is the process of transforming raw data into a format more valuable or useful for downstream applications. Also referred to as cleansing or remediation, data wrangling is an essential of data science and analysis. However, this process is both time and labor intensive. Some sources estimate that data scientists spend most of their time on this mundane but vital task. Automated data cleansing using AI-based platforms is emerging as an efficient and scalable way for data scientists to work.

Because the creation of raw data is accelerating, it should come as no surprise that employer demand for the ability to transform data is accelerating. In 2022, demand for data wrangling grew by 405%, tying for first in our list of fastest-growing technical skills.

Data Modeling

Data modeling is the process of creating and analyzing a visual model that represents the production, collection, and organization of data. Data models are vital for understanding the nature, relationships, usage, protection, and governance of a company’s data. Tools that data scientists use for data modeling include ER/Studio, Erwin Data Modeler, SQL Database modeler, DBSchema Pro, and IBM InfoSphere Data Architect.

In 2022, demand for data modeling grew by 308%, ranking third in our list of fastest-growing technical skills.

Data Visualization

After unlocking valuable insights from raw data, data scientists need to communicate their findings in a clear and visual format. Data visualization is the process of creating graphical representations of data for presenting insights to technical and non-technical stakeholders. Data scientists create visuals like graphs, charts, and maps using data visualization tools such as Tableau or front-end languages such as JavaScript.

Like data wrangling, employer demand for this skill is accelerating. In 2022, demand for data visualization also grew by 405%, tying for first with data wrangling in our list of fastest-growing technical skills.

Programming

Data scientists use a range of programming languages to work with data. While there are a number of languages used in the field of data science, an individual data scientist might only learn a few languages that align with their specialization, interests, and career path.

Languages used for data science include:

• Python (used for math, statistics, and general programming)
• Java (used for data analysis, data mining, machine learning)
• Julia (used for numerical analysis and computer science)
• MATLAB (used for deep learning and numerical analysis
• R (used for statistical computing and machine learning
• Scala (used for big data and scalability)
• C/C++ (used for scalability and performance)
• JavaScript (used for data visualization)

Math and Statistics

One skill emphasis that makes data scientists unique is mathematics. While a strong background in math is important to any programmer, it’s essential to data scientists. Data science is equal parts statistics and computer engineering, so while a job description may or may not mention it, competency in the following subjects is vital:

• Statistics
  • Probability theory
  • Classification 
  • Regression
  • Clustering
• Linear Algebra
• Calculus

The post The 10 Most Important Data Science Skills in 2023 appeared first on HackerRank Blog.

Called the sexiest job of the twenty-first century, data science is one of today’s most promising technical disciplines. So promising, in fact, that Google CEO Sundar Pichai compared data science’s ongoing development of artificial intelligence (AI) to the discovery of fire and electricity.

In the coming decade, data science will transform entire societies, governments, and global economies. Even the next evolution of humanity is in the works. Here are the eight data science trends that will drive that transformation in 2023.

What is Data Science?

Companies of every size and industry need data to make informed business decisions. Doing so requires technical professionals who use statistics and data modeling to unlock the value in unprecedented amounts of raw data. Data scientists use statistical analysis, data analysis, and computer science to transform this unprocessed data into actionable insights.

On a more technical level, the core job responsibilities of data scientists include:

• Writing code to obtain, manipulate, and analyze data
• Building natural language processing applications
• Creating machine learning algorithms across traditional and deep learning
• Analyzing historical data to identify trends and support decision-making

2023 Data Science Trends

Automated Data Cleansing

Before business can make data-driven decisions, data scientists have to detect, correct, and remove flawed data. This process is both time and labor intensive, which drives up costs and delays decision making. Automated data cleansing is emerging as an efficient and scalable way for data scientists to outsource labor-intensive work to AI-based platforms. This will give data scientists more time and resources to focus on higher-impact actions, like interpreting data and building machine learning (ML) models.

AutoML

Automated machine learning (AutoML) is the process of “automating the time-consuming, iterative tasks of machine learning.” With AutoML, data scientists are able to build machine learning models in a development process that’s less labor- and resource-intensive. Efficient, sustainable, and scalable, AutoML also has the potential to increase the production of data science teams and make machine learning more cost-effective for businesses. Tools like Azure Machine Learning and DataRobot are even making it possible for users with limited coding experience to work with ML.

Customer Personalization

Have you ever received an ad for a product right after you thought about it? It wasn’t a coincidence. And brands aren’t able to read a consumer’s mind (yet). It turns out that data science is to blame. 

Data scientists are using artificial intelligence and machine learning to make recommendation systems so effective that they can accurately predict consumer behaviors. And it turns out that consumers are surprisingly excited about this new approach. 52% of consumers expect offers from brands to always be personalized. And 76% get frustrated when it doesn’t happen. To deliver on these expectations, companies need to collect, store, secure, and interpret huge quantities of product and consumer data. With the skills to analyze customer behavior, data scientists will be at the forefront of this effort.

Data Science in the Blockchain

By 2024, corporations are projected to spend $20 billion per year on blockchain technical services. So, it shouldn’t come as a surprise that data science is poised to help companies make sense of the blockchain. Data scientists will soon be able to generate insights from the massive quantities of data on the blockchain.

Machine Learning as a Service

Machine Learning as a Service (MLaaS) is a cloud-based model where technical teams outsource machine learning work to an external service provider. Using MLaaS, companies are able to implement ML without a significant upfront investment of budget and labor. With such a low cost of entry, machine learning will spread to industries and companies that would otherwise not be able to implement it. 

Use cases for MLaaS include: 

• Analyzing product reviews
• Powering self-driving cars
• Designing chatbots or virtual assistants
• Performing predictive analytics
• Improving manufacturing quality
• Automating natural language processing
• Building recommendation engines

Leading MLaaS providers include AWS Machine Learning, Google Cloud Machine Learning, Microsoft Azure Machine Learning, and IBM Watson Machine Learning Studio.

Natural Language Processing

Natural language processing (NLP) is the branch of AI focused on training computers to understand language the way human beings do. Because NLP requires massive quantities of data, data scientists play a significant role in this advancing field. 

There are a variety of use cases for natural language processing, including: 

• Credit score analysis
• Customer service chatbots
• Cyberbullying prevention
• Fake news detection
• Language translation
• Speech and voice recognition
• Stock market prediction

With so many potential applications, NLP is among the most promising trends in data science.

TinyML

TinyML is the implementation of machine learning on small, low-powered devices.  Instead of running on consumer CPUs or GPUs, TinyML devices can run microcontrollers which consume 1,000x less power. With such a high cost-efficiency, TinyML provides the benefits of machine learning while avoiding.

Synthetic Data

In 2021, GPU Manufacturer Nvidia predicted that data would be the “oil” that drives the age of artificial intelligence. And with 912.5 quintillion bytes of data generated each year, it might seem like the supply to drive this revolution is endless. But what if you could make your own oil? 

Much like natural resources, access to data isn’t distributed evenly. Many companies don’t have access to the huge quantities of data they need to drive AI, machine learning, and deep learning initiatives. 

That’s where synthetic data can help. Data scientists use algorithms to generate synthetic data that mirrors the statistical properties of the dataset it’s based on. 

Unsurprisingly, the potential use cases for synthetic data are as limitless as the data it creates: 

• Autonomous robots
• DevOps
• Fraud detection
• Medical training resources
• Privacy protection
• Product development and testing
• Self-driving cars
• Video surveillance

But there’s one effect of synthetic data that’s a guarantee: more data. With synthetic data, the world’s data generation will accelerate to a rate the human mind can’t begin to fathom.

The post Top 8 Data Science Trends for 2023 appeared first on HackerRank Blog.

Data scientists are responsible for transforming the world’s growing ocean of data into valuable and actionable information. With such a niche and in-demand skill set, connecting to individuals with data science expertise is easier said than done.

To effectively attract and hire data scientists, companies will need to craft job descriptions that sell candidates on the opportunity of the position. In this post, we break down how hiring managers and recruiters can write successful data scientist job descriptions.

What Does a Data Scientist Do?

Before writing a compelling job description, companies should have a clear understanding of data science and the core requirements of the role.

Companies of every size and industry need data to make informed business decisions. Doing so requires people with knowledge of statistics and data modeling to unlock the value in unprecedented amounts of raw data.

Data scientists use statistics, data analysis, and computer science to transmute unprocessed data into actionable insights.

On a more technical level, the core job responsibilities of data scientists include:

• Using database tools and programming languages to obtain, manipulate, and analyze data
• Building natural language processing applications
• Creating machine learning algorithms across traditional and deep learning
• Analyzing historical data to identify trends and support optimal decision-making
• Communicating with both technical and non-technical stakeholders 
• Keeping up-to-date with advancements in technology

What Should a Job Description Include?

Company Value Proposition

In 2020, there was a shortage of 250,000 data scientists across the world. With this level of demand for their skills, data scientists have an endless array of opportunities to choose from. So, what will make your open roles stand out? The first section of a job description needs to address this question.

Communicating the opportunity of the role at hand – along with the employer brand, value proposition, and company culture – is essential in crafting a successful job description.

Responsibilities

The responsibilities section lists the core activities of the role. There are a number of different ways to represent these responsibilities, including daily tasks, monthly responsibilities, objectives of the role, and key outcomes. 

Common data science responsibilities include:

• Analysis
• Coding
• Planning
• Communication
• Collaboration
• Storytelling

Basic Qualifications

The basic qualifications section has two main components.

First is the required degree level. A common qualification for data science roles is for the candidate to have a minimum of a bachelor’s degree, though it’s also common for the role to require a master’s or even a doctorate. One study found that 88% of data scientists have a master’s degree or higher — and 48% had a doctoral degree or higher.

Second is the required years of experience scaled to the seniority of the role. This requirement is communicated as either a range or a minimum requirement:

• Entry-level: 0-2 years
• Mid-level: 3-5 years
• Senior-level: 5+ years

Because of the field’s heavy emphasis on graduate degrees, data scientists often begin their careers at the senior level. Companies looking to hire data scientists with advanced degrees need to account for this fact while communicating the experience level of the position. For example, a company hiring a senior data scientist would consider the following experience levels equivalent:

• PhD with 2+ years of experience
• MS with 4+ years of experience
• BS with 6+ years of experience

Lastly, some companies use this section to list experience with a minimum number of programming languages as a basic qualification.

Required Qualifications

The required qualifications section is one of the core sections of a job description. While basic qualifications focus on education and experience, this section gives employers the space to list the must-have technical competencies and soft skills required by the role. 

Common technologies found in this section include:

• Python
• R
• SQL
• MySQL
• NoSQL
• Oracle
• C/C#
• Go
• MATLAB
• Java
• PHP
• Ruby
• Scala
• AWS
• Azure
• GCP

Common technical competencies found in this section include:

• Advanced knowledge of machine learning and artificial intelligence
• Experience with big data tools
• Ability to engage in data storytelling
• Extensive experience with relational databases
• Domain expertise in a particular industry or use case
• Knowledge of cloud computing and infrastructure
• Experience working in an agile environment
• Knowledge of industry-wide technology trends and best practices

Common mathematical skills found in this section include:

• Statistics
  • Probability theory
  • Hypothesis testing
  • Regression
  • Nonparametric statistics
• Linear Algebra
  • Vectors
  • Matrixes
  • Tensors
  • Linear systems
• Calculus
  • Differentials
  • Integrals
  • Partial Derivatives
  • Directional gradients

Common soft skills found in this section include:

• Communication
• Technical communication
• Project management
• Time management
• Problem solving

Other Desired Skills & Nice-to-Haves

While not present on every job description, some companies will choose to mention additional skills that the company desires applicants to have, but are not required to succeed in the role. Examples of nice-to-haves for a data science role would be experience with visualization tools such as Tableau, experience with Apache Hadoop, or an understanding of JavaScript.

Sample Job Description

Job descriptions for data science roles can vary widely, depending on the responsibilities, compensation, and seniority of the position. That said, there are commonalities between descriptions that companies can take advantage of. Here’s an example of a job description for a senior-level data scientist role:

Title: Senior Data Scientist

Full-time. Associate.

Responsibilities

Analysis – Leads and participates in the development, validation, and delivery of algorithms, statistical models, and reporting tools.

Delivery – Delivers agile solutions aligned to business needs while maintaining a high standard of quality.

Collaboration – Partners with product owners to understand business requirements to translate them into intelligence solutions. Spreads data best practices to the team.

Communication – Clearly communicates roadmap, backlog, and team updates across the organization. Communicates actionable insights to non-technical stakeholders.

Qualifications

Basic Qualifications

• 3-5 years of data science experience mining actionable insights from data sets.
• Programming experience with at least two languages.
• PhD with 2+ years of experience, MS with 4+ years of experience, or BS with 6+ years of experience in a quantitative discipline.

Qualifications

• 3+ years of hands-on experience with relational databases (SQL, MySQL)
• 3+ years of experience in machine learning (regression, classification, clustering)
• Experience using C, C++, R, Python, or Go
• Knowledge of industry-wide technology trends and best practices.
• Strong mathematical skills (statistics, calculus)

Other Desired Skills

• Experience with Apache Spark, Apache Hadoop, and TensorFlow
• Understanding of visualization tools and front-end programming languages

The post How to Write a Data Scientist Job Description [Template] appeared first on HackerRank Blog.

Every day, the world creates 2.5 quintillion bytes of data. That’s 912.5 quintillion bytes each year — a staggering rate that’s only accelerating. With this massive rate of data generation in mind, it should be no surprise that over 90 percent of the world’s data was generated in the last two years alone. 

Data scientists are responsible for interpreting, modeling, and transforming this growing ocean of data into valuable and actionable information. In this post, we’ll break down the statistics, job requirements, and responsibilities of a career in data science.

Overview of the Duties of a Data Scientist

Companies of every size and industry need data to make business decisions. And businesses need people with knowledge of statistics and data modeling to unlock the value within this mountain of raw data.

Data scientists use statistical analysis, data analysis, and computer science to transmute unprocessed data into actionable insights.

On a more technical level, the core job responsibilities of data scientists include:

• Writing code to obtain, manipulate, and analyze data
• Building natural language processing applications
• Creating machine learning algorithms across traditional and deep learning
• Analyzing historical data to identify trends and support optimal decision-making
• Communicating with both technical and non-technical stakeholders 
• Keeping up-to-date with advancements in technology
• Working in an agile environment

What Kinds of Companies Hire Data Scientists?

Any company that’s looking to collect, manage, and interpret data to make business decisions will need to hire data scientists. With companies in every industry becoming increasingly data driven, the demand and opportunities for data scientists is endless. To name a few, these industries and applications include:

• Finance. Data science applications include risk management, fraud detection, algorithmic trading, and consumer analytics
• Healthcare. Data science applications include medical imaging, gene sequencing, predictive analytics, patient monitoring, and disease prevention 
• Insurance. Data science applications include risk pricing, customer profiling, call center optimization, and fraud detection
• Pharmaceuticals. Data science applications include drug development, patient selection, safety assurance, and targeted marketing and sales
• Retail. Data science applications include fraud detection, inventory management, product recommendations, price optimization, trend prediction, and sentiment analysis
• Supply chain. Data science applications include distribution, pricing, sourcing/procurement, and demand forecasting
• Telecommunications. Data science applications include network optimization, service personalization, sentiment analysis, and some customer experience

Types of Data Science Positions

The titles data scientists hold vary drastically, depending on their experience, education, and the company they work at. The title of a graduate from a coding bootcamp might look different than a candidate with a four-year degree. And the role of a data scientist in a five-person startup will be different than at a 5,000 person company.

The role in which a data scientist starts their career will depend on their education level. Some data scientists will start out with an entry-level role like junior data scientist or data science analyst. A new data scientist usually works in one of these roles for one to three years.

However, because many data scientists obtain master’s and doctorate degrees, a recent graduate might have enough equivalent experience to start their career in a senior-level role. 

Data scientists that progress to or start in senior-level roles will hold job titles with hands-on analytical experience, such as senior data scientist, data scientist II, or senior data analyst. While they spend several years honing their skills, their responsibilities expand to include taking ownership of projects, working independently in a team environment, and mentoring project team members. Senior data scientists who have not completed a doctorate degree might pursue one to advance their career opportunities.

Beyond experience and experience, data science titles also vary depending on area of specialization. Examples of specializations include:

• Data engineer
• Data architect
• Data storyteller
• Business intelligence developer
• Machine learning scientist
• Machine learning engineer

With some experience under their belt, a data scientist often faces a crossroads in their career, having to choose between two paths. 

The first path is to pivot into people and team management functions. Hiring, mentoring, resource planning and allocation, strategy, and operations become a larger component of the responsibilities of data scientists pursuing this career path. At the higher levels of an organization, these job functions might include:

• Director of Data Science
• Data Science Manager
• Data Operations Manager
• Information Systems Manager
• Chief Information Officer (CIO)
• Chief Technology Officer (CTO)

The second possible career path is to continue as an individual contributor. Many data scientists opt to continue their careers as individual contributors, enjoying equally fulfilling careers and developing deeper technical expertise in various technology languages and frameworks.

The motivation behind this decision is that experienced data scientists may not be interested in or qualified to be managing a team. And scientists in an individual contributor role have the opportunity to focus on growing their technical skills and learning the newest emerging technologies.

However, the career path for senior data scientists working as individual contributors is still being defined, as data science is still a relatively new field. So, while data scientists are able to make a significant contribution early in their careers, the playbook for a career in data science is still very much being written. 

Salary Comparisons & Job Outlook

On average, data scientists tend to receive a salary significantly higher than the national average in their country.

For example, in the U.S. the average salary in 2020 was $53,400. In contrast, the average base salary for data scientists in the U.S. is $110,000 to $129,000. That’s 105.9 to 141.6 percent more than the national average.

Junior data scientists can expect to occupy a lower salary band at the beginning of their career. In contrast, senior positions provide a higher average compensation, though data for this specific salary band is hard to find. Industry and company size also affect the salary band dramatically.

Historically, though, geography has had a significant impact on the compensation of technical talent — and that includes data scientists. The U.S. leads the world in data scientist salaries by a margin of 8.1 percent. The remaining members of the top five highest paying countries are Australia, Israel, Canada, and Germany.

But compensation also varies within each country, not just internationally. For example, while data scientists in Seattle make an average of $147,900 a year, most data scientists in Chicago earn $120,448. That’s a 22.8 percent variation in compensation.

The job outlook for data scientists is equally promising. As the quantity of data the world produces accelerates, so too will the demand for scientists to analyze that data. From 2020 to 2030, the U.S. Bureau of Labor Statistics projects the number of employed computer and information research scientists in the U.S. to grow by 22 percent — almost triple the 8 percent average growth rate for all occupations. 

As data science is still a maturing field, the role of data scientists will continue to evolve. Data scientists will play a critical role in the development of the world’s most promising technologies, including, artificial intelligence, deep learning, natural language processing, robotics, and self-driving vehicles.

Requirements to Become a Data Scientist

Technical Skills

Data scientists use a range of technologies to work with data. These include, to name a few:

• SQL
• MySQL
• NoSQL
• MATLAB
• Python
• Go
• Julia
• Ruby
• R
• Scala

Many data science roles also require knowledge of at least one major programming language, such as C, C+, C#, or Java. And skills for cloud technologies such as AWS are in demand for certain roles, too. Data scientists also might use data visualization tools such as Tableau or front-end languages such as JavaScript to create visuals.

Recruiters and hiring managers looking for data scientists should look for proficiency with in-demand competencies and frameworks. These include:

• Apache Spark (data processing)
• Hadoop (big data processing)
• Hive (data warehousing)
• Keras (neural networks)
• Pig (data analytics)
• PyTorch (natural language processing)
• TensorFlow (neural networks)

It’s worth noting that there’s a degree of fluidity to the technologies that data scientists use. A framework that’s in demand today might be outdated a year from now.

One skill emphasis that makes data scientists unique is mathematics. While a strong background in math is important to any programmer, it’s essential to data scientists. Data science is equal parts statistics and computer engineering, so while the job description might not mention it, competency in the following subjects is vital:

• Statistics
  • Probability theory
  • Classification 
  • Regression
  • Clustering
• Linear Algebra
• Calculus

Technical recruiters and aspiring data scientists alike will notice that there’s no standard way to learn this skill set. There’s a huge variety in the technologies data scientists know and the order they learn them in.

Soft Skills

Technical competency alone isn’t enough to succeed in a data science role. Mathematical, analytical, and problem-solving skills are a must in any technical role. And soft skills are even more critical in a digital-first or digital-only environment.

Employers may put even more stock into data scientists with strong soft skills, such as:

• Time management
• Communication
• Project management
• Problem solving

Communication skills, in particular, are critical to data science. One of a data scientist’s main responsibilities is to communicate complex information to nontechnical stakeholders in other departments. The ability to translate technical subject matter into digestible, actionable information that anyone can understand is highly valuable to data scientists — and the teams that employ them.

Experience & Education

After competency, the most important qualification for data scientists is experience. On-the-job experience and training is a critical requirement for many employers.

Then there’s education. While a university education is common in technical professions (about 75 percent of developers have a bachelor’s degree or higher), the field of data science tends to place a greater emphasis on postgraduate education. One study found that 88% of data scientists have a master’s degree or higher. Doctorate degrees are also common — and sometimes required. Recruiters interviewing for data science roles should anticipate that most candidates will have a postgraduate degree and that most employers will require a degree. 

But competition for skilled data scientists is fierce, and it’s common for job openings requiring degrees to go unfilled. Companies looking to hire data scientists will have access to a much larger pool of talent if they recognize other forms of education and experience. Even in the field of data science, online training, bootcamps, and independent learning are popular ways to learn new technical skills. 

Resources for Hiring Data Scientists

Developer Hiring Solutions

HackerRank Projects for Data Science

Resources for Becoming a Data Scientist

HackerRank for Developers

Getting Started with Data Science

The post What Does a Data Scientist Do? Job Overview & Skill Expectations appeared first on HackerRank Blog.