To begin, let’s offer a definition of data science. According to Wikipedia, data science “is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms, and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data”[1]. It encompasses various techniques, procedures, and tools to process, analyze, and visualize data, enabling businesses and organizations to make data-driven decisions and predictions. The primary goal of data science is to identify patterns, relationships, and trends within data to support decision-making and create actionable insights.

You are not alone in your interest in data science – it was called by the Harvard Business Review one of the sexiest jobs in the 21st century [2], and stories of data scientists earning enormous salaries in the six-figure range are not uncommon. Data scientists are often looked...

Performing data science work is often an iterative process, where the data scientist needs to return to earlier steps if they run into challenges. There are many ways to categorize the data science process, but it often includes:

• Data collection
• Data exploration
• Data modeling
• Model evaluation
• Model deployment and monitoring

Let’s briefly touch on each step and discuss what’s expected of the data scientist during them.

Data collection

Data collection and preprocessing involves gathering data from various sources (such as databases, APIs, and web scraping), then cleaning and transforming the data to prepare it for analysis. This step involves dealing with missing, inconsistent, or noisy data and converting it into a structured format. Depending on the organization, a team of data engineers support this step of the data science process; however, it is common for the data scientist to manage this process...

Now that we have defined some of the critical aspects of the role of a data scientist, it is clear that the role often covers many different skills. Data scientists are frequently asked to perform a variety of data-related tasks, including designing database tables to collect data, programming ML algorithms, understanding statistics, and creating stunning visuals to help explain interesting findings to others, but it is difficult for any single person to master all of these skill areas.

Therefore, we often see data scientists who are particularly skilled in one or two areas and have basic competencies in the others. Their talents could be considered T-shaped, where they are proficient across many areas such as the horizontal line of a T, while they have deep knowledge and expertise in a few areas such as the vertical portion of the letter:

Figure 1.2: Example of the ‘T of Competencies’

While this...

The field of data science is rapidly evolving, drawing professionals from various backgrounds and disciplines. This dynamic landscape has given rise to a multitude of career paths in data science, each bringing their unique perspectives, skills, and experiences to the table. In this section, we will explore three primary types of data scientists: the traditionalist, the domain expert, and the off-the-beaten path-er. Does one of these career paths best fit you?

The traditionalist

The traditionalist data scientist has followed a more conventional educational path toward data science. They typically possess a strong background in computer science or mathematics, often with a minor in the other. Other common majors include operations research, statistics, physics, and engineering. These individuals often go on to earn an advanced degree in these fields, including a master’s degree or even a Ph.D. Their rigorous academic training equips...

So, you want to be a data scientist? Welcome to The Hunger Games: Data Science Edition!

While that may sound like an exaggeration, the increasing demand for data scientists has turned the interview process into a battleground for candidates with various backgrounds and expertise.

But fear not – just as with The Hunger Games, the odds can be in your favor.

The fact that there is competition should not scare you away from entering the field. You’ve already shown your interest and commitment by reading this book, and as you progress through it, you’ll learn how to prepare for data science interviews, regardless of your background. In addition, we will share strategies to fill gaps in your experience to make you a stronger candidate. Remember – you have your own set of strengths and weaknesses. You can come out on top by focusing on your gaps and understanding your unique skills.

Believe it or not, it's incredibly...

Here’s the deal – the interview is a critical component of the data science job application process, where you can showcase your skills, knowledge, and personality to potential employers. The interview process is crucial for several reasons:

• Employers can assess your technical skills, problem-solving abilities, and critical thinking
• It lets you demonstrate your communication skills, teamwork, and cultural fit
• It allows you to ask questions and gather information about the company and role to ensure it aligns with your career goals and values
• Preparing for the interview is essential to stand out in the competitive job market and secure your dream role

Preparing for the data science interview is essential to success. In fact, it’s one of the most useful activities that you can do for your career. This is not only true for prospective data scientists looking to land their first job in the field...

The field of data science continues to evolve, both in terms of the tools used and the type of work conducted. This evolution is driven by advancements in technology, the increasing availability of data, and the growing demand for data-driven insights in a wide range of industries. As a result, it is critical for those interested in entering the field to not only learn fundamental techniques and technologies of data science but also to stay diligent and current on new developments and technologies.

New models

One of the most significant ways in which the field of data science is evolving is through the development of new ML and AI algorithms and techniques. As AI continues to become more sophisticated, data scientists are able to build more accurate and powerful predictive models that can be used to solve a wide range of complex problems. This includes the implementation of methods borrowed by other fields in industry and academia such...

In this chapter, you’ve learned about the modern data science landscape, what the role entails, what skills and competencies are expected of a prospective candidate, and the most common paths to becoming a data scientist. Furthermore, you’ve learned about the multi-faceted functionality of data science and how it leads to a diverse workforce of data scientists with different specialties and backgrounds.

With this in mind, you may determine what your path might look like or what knowledge gaps you hope to close. Whichever the case, you are now prepared to move forward with your interview preparation.

In this next chapter, we will begin the data science job search journey by mentally (and emotionally!) prepping you for the road ahead. We’ll discuss some underrated tips on how to identify the right job opportunity, where to find it, how to create a stand-out application, and how to stay ahead of the curve in a sea of evolving technology, project portfolios...

• [1] Data science from Wikipedia: https://en.wikipedia.org/wiki/Data_science
• [2] Is Data Scientist Still the Sexiest Job of the 21st Century? by Thomas H. Davenport and DJ Patil, from Harvard Business Review: https://hbr.org/2022/07/is-data-scientist-still-the-sexiest-job-of-the-21st-century
• [3] Data Scientists from U.S. Bureau of Labor Statistics: https://www.bls.gov/ooh/math/data-scientists.htm#tab-1
• [4] The Digitization of the World by David Reinsel, John Gantz, and John Rydning, from International Data Corporation: https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf