How to Become a Data Scientist – A Complete Career Guide
With Kat Campise, Data Scientist, Ph.D.
Part mathematician, part computer scientist, and part business strategist, data scientists must have expertise in several different disciplines at once. This complex skill set means that data scientists need to consistently have one foot in the information technology sector, and another planted firmly in the business world. That’s part of what places this expertise in such high demand and why becoming a data scientist is one of the best career decisions you can make.
What is a Data Scientist?
Data science is primarily focused on deep knowledge discovery through data exploration and inference and a good data scientist must possess both the statistical knowledge and computer skills that are needed for solving complex problems. This discipline focuses on using mathematical and algorithmic techniques to solve some of the most analytically complex business problems, leveraging troves of raw data to figure out the hidden insight that lies beneath the surface.
Given the exponential amount of data being churned out via our smartphones, desktops, and the vast array of IoT devices throughout the world, governments and private enterprises are interested in gleaning insight from their extensive data collection processes. At first glance, one may assume that data analysts and data scientists are interchangeable – meaning there is a mutual one-to-one correspondence between the two, but this is not the case.
The work of a data scientist quite often is centered around precise and exacting minutia-driven analysis and yet data scientists must also possess exceptional verbal, written, and visual communication skills. This is because it will fall to them to express their findings and analysis to a host of others who may find highly sophisticated data-driven jargon difficult to follow. This may include not just their own superiors and colleagues on different teams, but also high-level company stakeholders. A data scientist will need to convey what they’ve discovered and what needs to be done about it now that the information is known, all in a comprehensive and easily-digestible way.
What does a Data Scientist do?
Essentially, a data scientist extracts meaning from the varying types of data (e.g., structured, unstructured, semi-structured) that flow into the enterprise. On any given day, a data scientist may be extracting data from a database, preparing the data for various analyses, building and testing a statistical model or creating reports that include easily understandable data visualizations. There is a data science cycle which isn’t a set of rules as much as it is a heuristic:
- Data collection
- Data preparation
- Exploratory data analysis (EDA)
- Evaluating and interpreting EDA results
- Model building
- Model testing
- Model deployment
- Model optimization
The above is iterative, meaning a data scientist will be in “evaluation mode” throughout the entire process. Or, perhaps, after the EDA phase, they find that the data doesn’t fit the problem they are trying to solve (or the question they are attempting to answer). They may need to start over or carefully choose which portions of the data that does apply, then go back and collect additional data. Such is the reason they need a higher level of combined skills including research design.
While data scientists can (and do) perform data analysis, they do so within the realm of building and deploying predictive models which often incorporate machine learning and deep learning protocols. Data scientists must also have a meta-level understanding of which models are the best fit for the data being analyzed. Since all models are approximations of current and future environments, they require fine-tuning which, in turn, relies on the data scientists’ mathematical expertise. Although data scientists are not data engineers, they should (ideally) have some knowledge of how databases are constructed, and how to pull data from an organization’s preferred database management system (DBMS). Due to the extensive knowledge requirements, including academic and professional training and/or experience different factions (companies, research organizations, and governmental agencies) are scrambling to find qualified data scientists.
This guide provides a basic overview of some of the opportunities in this emerging field and lists the steps required to become a data scientist. For informational purposes, a detailed job description, salary information, and preferred qualifications are included along with the projected data science job outlook beyond 2021.
Six steps to become a Data Scientist
Step 1: Preparation
Future data scientists can begin preparations before they even step foot on a university campus or launch themselves into an online degree program. Becoming proficient with the most widely-used programming languages in data science such as Python, Java, and R — and refreshing their knowledge in applied math and statistics — will help aspiring data scientists get a head start. In fact, entering college with an already established skillset frequently improves a student’s learning rate. Most importantly, early exposure to data science knowledge requirements is helpful for determining whether a data science career is even the right fit for you.
Step 2: Complete undergraduate studies
The most sought-after majors for data science are statistics, computer science, information technologies, mathematics, or data science, if available. If you’re already going through a different undergraduate program and you’re not prepared to make the switch, then at least minoring in one of the fields is also recommended. Continue to learn programming languages, database architecture, and add SQL/MySQL to the “data science to-do list.” Now is the time to start building professional networks by looking for connections within college communities, look for internship opportunities, and ask professors and advisors for guidance.
Step 3: Obtain an entry-level job
Companies are often eager to fill entry-level data science jobs. Search for positions such as Junior Data Analyst or Junior Data Scientist. System-specific training or certifications in data-related fields (e.g., business intelligence applications, relational database management systems, data visualization software, etc.) might help when looking for entry-level data science jobs. Make sure to brush up on your interview skills before you begin the process, particularly as they relate to a data science career.
Step 4: Earn a Master’s Degree or a Ph.D.
Data science is a field where career opportunities tend to be higher for those with advanced degrees like a Master’s or Ph.D. The in-demand graduate degrees for data science include the exact same specifications for an undergraduate degree: data science (if available), computer science, information technology, math, and statistics. However, many companies also accept STEM degrees such as biotechnology, engineering, and physics (among others). Also keep in mind that data scientists need to understand how to use enterprise-grade data management programs and how distributed storage and computation operate (e.g., Hadoop, MapReduce, and Spark) in relation to model building and predictive analytics.
Step 5: Get promoted
Additional education and experience are key factors that lead to being promoted or becoming a data scientist in high demand. Businesses value results. Coupling strong technical skills with project management and leadership experience will generally chart a course towards more significant opportunities and higher compensation.
Step 6: Never Stop Learning
Staying relevant is crucial to the ever-evolving field of data science. In this age of constant technological innovation, continuing education is a hedge against shifts in the career market. This is also the case for data science since the field isn’t as established as other statistically and technologically focused careers. A career-oriented data scientist is always learning and evolving with the industry. Continue to network and look for educational and professional development opportunities through boot camps and conferences.
Data Scientist job description
While data science projects and tasks may vary depending on the enterprise, there are primary job functions that tend to be common among all data science positions such as:
- Collecting massive amounts of data and converting it to an analysis-friendly
- Problem-solving business-related challenges while using data-driven techniques and tools.
- Using a variety of programming languages, as well as programs, for data collection and analysis.
- Having a wealth of knowledge with analytical techniques and tools.
- Communicating findings and offering advice through effective data visualizations and comprehensive reports.
- Identifying patterns and trends in data; providing a plan to implement improvements.
- Predictive analytics; anticipate future demands, events, etc.
- Contribute to data mining architectures, modeling standards, reporting and data analysis methodologies.
- Invent new algorithms to solve problems and build analytical tools.
- Recommend cost-effective changes to existing procedures and strategies.
Data Scientist Skill Set
- Experience and Fluency in many of these computer/coding programs: SAS, SPSS, MATLAB R, Python, Java, C/C++, Hadoop Platform, SQL/NoSQL Databases.
- Business Savviness: Data scientists need to understand the business sector they are working in and create solutions to complex problems that align with business logic/objectives.
- Communication skills: A data scientist can clearly and fluently translate their technical and analytical findings to a non-technical department. They must also be able to understand the needs of their non-technical departments (such as business development or marketing teams) in order to analyze the data correctly. A data scientist must empower the business to make decisions by presenting robust and verifiable information.
- Expert Technical skills in the following:
- Math (g., linear algebra, calculus, and probability)
- Machine learning tools and techniques
- Data mining
- Data cleaning and munging
- Data visualization and reporting techniques
- Unstructured data techniques
Data Scientist salary
The current base annual salary for data scientists is $116,000 as of July 2021 according to Glassdoor. However, this figure can grow dramatically, depending on the size and sector of the enterprise. Additional factors include a data scientist’s educational level, years of experience, location, certifications, and the involvement one has in professional organizations. According to the Bureau of Labor Statistics, states that feature high levels of employment for data scientists, as well as top salaries include California, New York, Washington, Texas, District of Columbia (plus Maryland and Virginia), and Illinois.
Read more about Data Science salaries and growth projections in this guide.
Data Scientist Job Outlook
More than perhaps any other, data science is a career on the rise, consistently regarded as one of the most in-demand fields for much of the past decade, and in 2021 this shows no sign of slowing down at all. In fact, the U.S. Bureau of Labor Statistics projects a 27.9% growth in data science occupations through 2026. LinkedIn reported a shortage of over 151,000 data scientists across the United States in 2018, particularly in the New York City, San Francisco, and Los Angeles metro areas.
More and more companies are looking for data scientists to help them process and understand data. Look at the most dominant and influential companies in the world like Amazon, Google, Apple, or Facebook, each of which thrives foundationally on data-driven decision making. For instance, Amazon uses data analytics to drive sales and marketing algorithms, recommending products to customers based on past purchases and behaviors. Apple, meanwhile, makes product decisions based on when and how its iPhones, iPads, Macbooks, and other devices and tech are being used by you, the customer. The collection of our data informs all those decisions and more, and it is data science professionals who are responsible for influencing those decisions.
It’s difficult for companies to survive today without adopting the kind of data-driven approach that modern businesses thrive on. Yet, the supply of data scientists still remains quite low and, even in 2021, it’s still a relatively new and emerging field and there are vast shortages of qualified candidates in a rapidly growing industry. While other 21st century careers like web design and programming have already started to become part of the curriculum of traditional education systems, that’s not always true of data science.
Be mindful that many companies that classify a data scientist as a “data architect,” “data engineer” or “data analyst,” may not understand the differences between each of these job requirements. In general, the data science job outlook continues to be on the upward trajectory as the influx of data isn’t likely to cease anytime soon and enterprises will need someone with the skills to parse through data tangle and help increase its value.