How to Become a Data Scientist in 2023

by Imed Bouchrika, Phd
Chief Data Scientist & Head of Content

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Data were once passive, disparate units of digital information that usually end up untouched in archives and storage facilities. Now,  it has become a robust tool in organizations, enabling them to make groundbreaking, evidence-backed decisions. And at the core of this significant, burgeoning need to improve data use is the data scientist.

With each and every like, share, and swipe, organizations are improving the influence of data, producing essential insights to make better choices, augment their operations, and more effectively make create value for their stakeholders. Because they cross over and merge business and IT, these data science experts have securely established a crucial role in today’s digital market environment.

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This article on how to become a data scientist offers timely and relevant guidelines and related information, such as the essential skills, duties, and organizational readiness to employ these highly in-demand data professionals.

How to Become a Data Scientist Table of Contents

1. What Is a Data Scientist?
2. How to Become a Data Scientist?
3. Must-Have Skills for Data Scientists
4. Typical Job Responsibilities of Data Scientists
5. When Is an Organization Ready for a Data Scientist?
6. Top Online Courses and Certifications for Data Scientist

We have generated so much data that 90% of the total information worldwide was created only over the last two years. The total volume of data generated, captured, duplicated, and consumed worldwide is predicted to grow very fast, estimated to reach 59 zettabytes in 2020 and 149 zettabytes by 2024 (Holst, 2020).

The entire world creates around 2.5 quintillion bytes of data on a daily basis (Gour, 2020). And with the proliferation of the Internet of Things (IoT), this pace is likely to further accelerate, predicted to create 79.4 zettabytes of data by 2025 (O’Dea, 2020).

Along with the breakneck speed of data explosion is the corresponding expanding demand for data scientists. For public, nonprofit, and for-profit organizations, this means an ever-increasing reliance on this new breed of data experts to help the organizations thrive and remain relevant in this constantly changing data-driven world.

I. What Is a Data Scientist?

What’s the “sexiest job” of the twenty-first century? In a Harvard Business Review article, authors and IT experts Davenport and Patil (2012) raised that evocative question to provide an illuminating discussion about data scientists. (Of course, their answer to that question is data scientist.) Today, as organizations from all sectors and industries struggle with unparalleled amounts of data, they also compete in a tight race to address their need to hire data scientists.

Definitions

Data science is an emerging field that uses combined algorithms and scientific methods to draw out knowledge and gain insights from structured and unstructured data (Bowne-Anderson, 2018). Data scientists are analytical data experts with specialized technical skills and inquisitiveness to detect and solve problems. They collect, analyze, and interpret massive volumes of data to help businesses make better decisions and create value to profit from them.

In a nutshell, data scientists are a new generation of data experts with specialized skills to solve data-related problems and unique acumen to decipher complex technical issues. A data scientist is part business analyst, statistician, programmer, coder, database administrator, artificial intelligence (AI)/machine learning (ML) specialist, trend spotter, data miner, project manager, etc.

In addition, the growing importance of data scientists signals the collective shift towards the increasing reliance of businesses on big data, AI, and ML (Agarwal & Dhar, 2014). The enormous volume of unstructured information had ceased to be neglected and unutilized. With data scientists, this massive amount of data produced on a daily basis has become a virtual organizational resource for market information that can be easily converted into profits and new growth opportunities.

At present, breakthroughs in automation, AI, big data, and ML have heightened the benchmarks of data science tools for business. From working mostly by themselves, data science teams have been formed, composed of business analysts, engineers, citizen data scientists, and expert data scientists. Their presence and impact extend across business units.

Origins

Many data scientists tend to start their careers as data analysts or statisticians. But as the creation of massive data started to further accelerate, those initial job functions changed as well. Data has stopped being a mere second thought for IT to manage. It has become essential information that needs scrutiny, creative interest, and a flair for converting high-tech concepts into new strategies to create value for the organization.

It was in 2008 when the term data scientist became widely-used. After less than a decade, pursuing a data analytics degree is considered as one of the top careers in the United States by 2016 (Mills et al., 2016). This relatively new job position has become so essential to organizations across industries that it is one of the highest-paid jobs in the tech industry. Today, entry-level data scientists earn an average salary of $85,143, while their senior counterparts earn $158,462 on average (Burtch Works, 2019).

The data scientist function also originated from the academe. Around a decade ago, colleges and universities started to recognize the urgency of incorporating data science fundamentals into their curricula. Initially, professors gradually integrated data science subjects in their syllabi until their institutions are able to offer actual data science degrees to tertiary students.

Moreover, secondary and primary schools around the world have started to integrate data science into their K-12 subjects, such as the Mobilize Introduction to Data Science curriculum. Mobilize breaks barriers by teaching students to apply concepts and practices from computer science and statistics in order to learn science and mathematics (Gould et al., 2016).

II. How to Become a Data Scientist?

Data scientists are well-trained and highly educated although there are some rare instances where robust educational training is tapped to develop the extent of requisite knowledge and skills to be a data scientist. According to Simplilearn (2018), 46% of all data scientists have a doctorate degree, while 88% are master’s degree holders.

As the shortage of data experts continues, pursuing a career in data science remains a smart choice. Aside from having numerous employment options, it also offers an opportunity to start your own company by learning the steps to starting a LLC. In addition, it allows you to grow and develop in an IT field teeming with creativity and innovation. Here are the two most common paths to becoming a data scientist:

Students

To become a data scientist, you need to earn a bachelor’s degree in data science. In case local tertiary schools do not offer this program, the option is to complete courses in statistics, physical sciences, social sciences, and computer science. Earning a degree in any of these courses will provide you with the foundational knowledge and skills to handle and analyze any type and volume of data.

A crucial first step is to select the right school that offers a good data science degree or an available equivalent. Princeton, Cambridge, and Yale are considered among the top schools in the U.S. to earn an undergraduate degree in data science (Bayern, 2019).

According to the Data Science Degree Programs Guide, the top schools that offer the best data science master’s programs for 2019 include Purdue University, University of Rochester, and New York University (DSD, 2019).

Professionals

Aside from formal training in data science, data scientists usually undergo prior training and experience as statisticians or data analysts. However, many also have nontechnical backgrounds, such as management, economics, or marketing.

One might ask, how can people from different backgrounds eventually share a similar career? For an answer, you need to examine what aspects they share in common. All of them have an insatiable desire to know how things work, the proficiency to communicate well, and a talent for solving complex problems.

Foremost of these shared characteristics among data scientists is their profound curiosity. A deep craving to probe the underlying cause of a problem, examine the questions at their core, and condense them into highly understandable inferences that can be analyzed (Davenport & Patil, 2012) is also a prominent attribute of these individuals. This usually involves corollary thinking that defines the most innovative scientists in any discipline.

A Suggested Path to Becoming a Data Scientist

Step 1: Determine if a data science career suits you

Data science is a highly challenging job, from the perspective of both educational requirements and intellectual demands. Take time to study if this career is the right one for you before you make actual steps to pursue it. If you have a passion for using technology and creativity to solve organizational issues or to find better ways of doing things, then a career in data science may be the right fit for you. 

Step 2: Select an academic path

Data science is a highly-specialized field that an undergraduate degree must be enhanced with relevant work experience to match the actual job requirements. Another option is to earn at least a master’s or a Ph.D. degree in data science or related fields (which many data scientists in leading organizations actually have). Courses to be taken include information science, domain knowledge, computer science, statistics, and mathematics.

Step 3: Establish a concentration area

As a growing field, it is best to choose an area of specialization as a data scientist. Having a definite and suitable data science field to concentrate on will help a lot in further honing your skills and enhancing your job performance and output. Since data scientists are deployed in (See Table below for a list of concentration areas that data scientists specialize in) the following areas:

Step 4: Get certified

Aside from completing formal education, it is also helpful to earn data science certifications from established training firms. This will greatly enhance your skills and help you become more marketable. Since there are many excellent training options, you need to take the program that suits your specific certification requirements. (See Top Online Courses and Certifications for Data Scientists below for actual samples.)

Step 5: Start working as a data scientist

After completing your formal education or training, you need to apply and get hired as a data scientist to actually become one. It is also important that you work in an organization that corresponds to your values and mindset to ensure success.

Thanks to the Internet, anyone planning to pursue a career in data science can choose to learn the required knowledge and skills on their own, at their own pace. It also helps to network with data scientists within your organization or through an online community.

III. Must-Have Skills for Data Scientists

As the data science field is still evolving, Garten (2018) suggests the value of distinguishing between the deliverables data scientists create as an expedient approach to categorize them. The first type of data scientists can be called modeling scientists. They generate output for machine consumption, e.g., algorithms, training data, and models. Decision scientists comprise the second type. They create output for human consumption, e.g., strategy and product recommendations.

According to LinkedIn, these are the most essential skills set that data scientists should have today:

• Data intuition – Organizations need data-driven problem-solvers to help them address today’s complex market problems
• Text analytics – the process of studying unstructured data to extract important business insights.
• Statistics – A reliable knowledge of statistics is crucial to a data scientist’s day-to-day job. 
• Data preparation – involves converting raw data into a different format to allow for easier consumption.
• Data wrangling – Always expect to work with unruly data that you must process and make orderly and usable. 
• Programming skills – Whatever organization you will be working for, you are likely going to be expected to know how to utilize various data-focused technologies.
• Pattern recognition – Technical knowledge that identifies patterns in data.
• Machine learning – A type of artificial intelligence that mainly employs automation and mathematical algorithms.
• Deep learning – A set of algorithms in machine learning research that utilizes data to frame complex abstractions for automated classification and prediction.
• Software engineering – It is indispensable to have a strong software engineering background
• Data visualization – An approach used to present data in a graphical format to allow for easy analysis.
• Multivariable calculus and linear algebra – Crucial when working for companies that produce and/or sell data-defined products and services.

IV. Typical Job Responsibilities of Data Scientists

As a relatively new and evolving organizational function, the data scientist’s role is still in a state of flux. But these are some duties and responsibilities that data scientists are likely to perform:

• Gather large volumes of disorderly data and convert it into a useful format.
• Employ data-driven methods to solve business-related problems.
• Operate using a toolbox of programming languages, such as Python, R, Java, and MATLAB.
• Perform statistical data treatments, such as factor analysis and tests.
• Apply sophisticated technologies such as text analytics, deep learning, and machine learning.
• Collaborate and communicate with both business and IT units.
• Search for patterns and order in data (e.g., useful trends), to frame meaningful insights to support decision-making.

There are several data science processes or lifecycle frameworks that aim to comprehensively establish what the job of a data scientist is. Mason and Wiggins (2010) proposed a simple, yet robust model called the OSEMN framework, which effectively captures a data scientist’s job, from gathering the data, up to data analysis, and results presentation. Moreover, this framework can be used as a guide for solving data problems.

V. When Is an Organization Ready for a Data Scientist?

Being the current highest-paying job (Comparably, 2019) in the U.S., it is relatively quite easy for data scientists to accept offers or get hired. However, before applying for or accepting a data scientist job in a company, there are a few considerations about the organization you should examine:

• How it values data. Key to making the decision of whether to hire a data scientist or not is a firm’s organizational culture. It should have an environment that supports data analytics and unwavering executive backing. In the absence of these factors, any investment in data science will be hampered and remain ineffective.
• Big data handling capability and protocols. A company must have two major requisites to justify the hiring of a data scientist. First, it should have the need to manage massive volumes of data. Second, they should be dealing with complicated data-related issues on a daily basis. 
• Readiness for change. Data scientists have gained their industrial importance mostly due to the extensive possibilities for advancement that they bring to any organization. However, these potentials would be worthless unless the company is willing to follow the path to organizational change that data scientists recommend.

Just like any method or tool, data science is not for everyone. An organization should clearly determine that it needs a data scientist to improve its operational performance. Likewise, an organization must have the right mindset and the willingness to embrace change.

VI. Top Online Courses and Certifications for Data Scientists

Here are some of the top online courses and certifications currently available for those seeking to learn or upgrade their data science knowledge and skills:

Data Science Online Courses

• Python for Data Science and Machine Learning Bootcamp (Udemy).  Teaches the use of Tensorflow, Machine Learning, Scikit-Learn, Plotly, Matplotlib, NumPy, etc.
• CS109 Data Science (Harvard University). This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set, among others
• MicroMasters Program in Statistics and Data Science (MIT, edX). It helps you to master the skills needed to solve complex data challenges.
• Data Science Courses (Dataquest). Offers hands-on courses, such as Python for Data Science: Fundamentals, which allow you to learn through coding so that you pick up concepts quickly.
• Applied Data Science with Python (University of Michigan and Coursera). A five-course data science specialization that includes Introduction to Data Science in Python and Applied Plotting, Charting, and Data Representation in Python.
• Introduction to Data Science (Metis). An accredited data science boot camp course that teaches core data science concepts and technologies.
• Data Science Specialization (Johns Hopkins University and Coursera). A ten-course introduction to data science designed and given by acknowledged data science experts.

Data Science Certifications

• Google Certified Professional Data Engineer. This is suited to individuals with fundamental knowledge of Google’s cloud platform and experience creating and managing applications using the platform.
• Microsoft MCSE: Data Management and Analytics. Covers an extensive array of IT specialties and skills. Microsoft offers two courses for data science certifications, one that focuses on data management and analytics, and another on business applications.
• Dell EMC Data Science Track. This Dell certification course includes two programs: the Data Science Specialist certification and the Data Science Associate v2 certification.
• IBM Data Science Professional Certificate. This comprises nine courses, including Python, Databases and SQL, data visualization, machine learning, data analysis, data science methodology, etc. The final phase is an applied data science capstone. (edX also offers its own IBM certification version.)
• Cloudera Certified Professional: CCP Data Engineer. This is promoted by Cloudera as one of the most challenging and “demanding performance-based certifications.”
• Certified Analytics Professionals. This certification is earmarked for data science practitioners who seek to advance their careers. CAP also offers certifications for entry-level professionals (Associate CAP) and CAP for organizations.

The costs of these courses and certifications vary, including their locations and duration (e.g., self-paced). Certifications are typically valid for as short as two, three to five years, while others have no expiration.

Do You Have the Fortitude of a Data Scientist?

So how does one become a data scientist? Plan. Study. Train. Gain experience. But more than anything, you should be very willing to make discoveries as you swim in massive oceans of data.

As a data scientist, you should be totally comfortable with threading between IT and business. In addition, you must have that unceasing drive to identify patterns or establish order on seemingly unconnected or unusable volumes of data so as to allow for order, evaluation, and perusal.

In today’s highly competitive environment where rules continuously change and massive data are constantly generated, data scientists empower decision-makers to move from impromptu analysis to a continuing conversation with data. And at the rate things are developing, it is likely that data scientists are meant to assume more crucial organizational functions and responsibilities in the years to come.

References

• Agarwal, R. & Dhar, V. (2014). Big data, data science, and analytics: Opportunity and challenge for IS research. Information Systems Research, 25 (3), 443-448. https://doi.org/10.1287/isre.2014.0546 
• Bayern, M. (2019, May 1). The top 10 schools to study data science. TechRepublic.
• Bowne-Anderson, H. (2018, August 15). What data scientists really do, according to 35 data scientists. Harvard Business Review.
• Burtch Works (2019). The Burtch Works Study: Salaries of Data Scientists & Predictive Analytics Professionals. Evanston, IL:  Burtch Works, LLC. 
• Comparably (2019, April 10). Study: Highest paying entry-level jobs in tech. Comparably. 
• Davenport, T.H., & Patil, D.J. (2012). Data scientist: The sexiest job of the 21st Century. Harvard Business Review, 90 (10), 70-6, 128. PMID: 23074866.
• DSD Staff (2019, April). 30 Best Master’s in Data Science Degree Programs 2019. Data Science Degree Programs Guide.
• Finzer, W., (2013). The data science education dilemma. Technology Innovations in Statistics Education, 7 (2). http://escholarship.org/uc/item/7gv0q9dc
• Garten, Y. (2018, November 6). The kinds of data scientist. Harvard Business Review.
• Gould, R., Machado, S., Ong, C., Johnson, T., Molyneux, J., Nolen, S. . . . Zanontian, L. (2016). Teaching data science to secondary students: The Mobilize Introduction to Data Science curriculum. In J. Engel (Ed.), Promoting understanding of statistics about society. Proceedings of the Roundtable Conference of the International Association of Statistics Education (IASE), July 2016, Berlin, Germany. http://iase-web.org/documents/papers/rt2016/Gould.pdf
• Gour, R. (2020, June 27). What does the future of Data Science hold for you? Medium.
• Holst, A. (2020, July 7). Information created globally 2010-2024. Statista.
• Mason, H., & Wiggins, C. (2010, September 25). A taxonomy of data science. Dataists.
• Mills, R., Chudoba, K., & Olsen, D. (2016). IS programs responding to industry demands for data scientists: A comparison between 2011-2016. Journal of Information Systems Education, 27 (2), 131-140.  https://jise.org/Volume27/n2/JISEv27n2p131.pdf 
• O’Dea, S. (2020, February 28). Data volume of IoT connected devices worldwide 2018 and 2025. Statista.
• Simplilearn (2018, May 23). 9 Must-have skills you need to become a data scientist (Updated). KDNuggets.