How to Change Your Career to Data Science

7 min read

Are you an effective problem-solver? Do you enjoy coding and programming? Are you intrigued by statistics and probability? If you’ve answered yes to any of these questions, you may want to consider exploring a career in data science — even if you think your current job has nothing to do with this popular field. 

If the idea of a data science position hasn’t exactly crossed your radar, consider this: The position of data scientist is ranked #6 on U.S. News & World Report’s list of 100 Best Jobs of 2022 with a median salary of $98,230, an unemployment rate of 2% and an estimated 19,800 jobs opening up between 2020 and 2030. In addition, the World Economic Forum’s The Future of Jobs Reports 2020 lists data analysts, data scientists, AI and machine learning specialists, and big data specialists among the top roles with increasing demand.

The good news is that many jobs and related positions can easily transition to a successful career in this popular field. Let’s dive in. 

Getting Started in Data Science

For aspiring data scientists who are just getting started, the path to a career in data science typically includes:

  • Expanding your technical skills by studying some of the fundamental disciplines within data science and practicing data science techniques, 
  • Networking and finding a mentor
  • Gaining work experience by securing an internship or an entry-level data analytics or data science job
  • Expanding your knowledge and skills with online courses, bootcamps, certifications or an advanced degree 

According to The Muse, a bachelor’s degree in computer science, math, statistics, engineering or a related field is usually required. In many cases, companies prefer applicants with advanced degrees.

Transitioning to Data Science from Different Backgrounds

Many skilled professionals who aspire to become data scientists have a strong educational and professional background in science, mathematics, engineering, information technology or computer science.

Here are some other important tips:

  • Read as much as you can about data science. This is especially helpful if you’re completely new to data science. Seek out everything from blogs and tutorials to articles, books, magazines, etc.  
  • Look for opportunities in your current organization. Talk to your hiring manager or human resources department and discuss your interest in data science. There may be data science-related opportunities available in your current organization that you’re unaware of.

Fields That Transition Well to Data Science

It may go without saying, but we’ll say it anyway: careers related to data are often a good way to transition to data science. Other fields that transition well include:

  • Business and finance
  • Software engineer
  • New computer science, math or physics graduates

It helps data science career changers to have some background and skills in creative problem solving, coding/programming and statistics and probability. 

That doesn’t mean, of course, that you shouldn’t pursue a data science career if you come from a completely different background. Check out books, videos and tutorials. Reading as much as you can about data science, taking online courses and tackling data-related projects can certainly add to your experience and help make for an easier transition to a data science career. There are also prerequisite courses designed to help prepare students for a data science degree program. 

Pursuing a Data Science Career for Beginners & Recent Grads

One of the first steps toward pursuing a career in data science — whether you’re a recent college graduate or a skilled professional looking for a change — is to explore data-related careers

According to an article from U.S. News & World Report, “Data science might be for you if you’re interested in drawing insights from the patterns and trends you notice in data sets. You’ll need an inquisitive mind and a head for numbers as well as a knack for computer programming.” 

To get started in data science, you’ll need a basic understanding of statistics, probability, programming languages and machine learning.

Identifying the Right Companies

If you’re exploring a career in data science, it’s important to know which companies are hiring. Do your research and consult with industry professionals. Reach out to colleagues and connections on LinkedIn. See which companies are making headlines and great strides in the data science industry. 

According to Glassdoor, the following top companies are hiring for data scientist jobs:

  • IBM
  • Microsoft
  • Amazon
  • Meta
  • Booz Allen Hamilton
  • Capital One
  • Walmart
  • Google
  • Uber
  • Deloitte
  • LinkedIn
  • J.P. Morgan 
  • AT&T

It’s also important to note that companies large and small — in practically every type of industry — are hiring data scientists. According to a recent search on LinkedIn, there are more than 125,000 job postings (please note that this number will change daily) at a wide range of companies and organizations, including:

  • Lowe’s 
  • Airbnb
  • Fidelity Investments 
  • Apple
  • NASA
  • Spotify 
  • Universal Orlando Resort 
  • CVS Health 
  • Centers for Disease Control & Prevention (CDC)
  • Starbucks
  • The Hershey Company

Some additional resources include:

How to Choose the Right Data Science Job

The good news: There is an abundance of open data science positions. But how do you navigate all the postings and job sites to find the right one? Here are some tips from Indeed that can help you figure out which companies and positions are the right fit:

  • Coordinate informational interviews. An informational interview is an informal discussion with a professional in a particular industry or at a particular company. If you’re interested in working at a specific business or organization, see if you have any connections (or mutual connections) on LinkedIn who work there and reach out for an informational interview. Benefits of an informational interview include:
    • Interviewing in a low-stress/low stakes environment
    • Gaining insider information about a particular company or position
    • Building connections that could help your job search now (or in the future!)
  • Network on a regular basis. If possible, attend professional development conferences, workshops, seminars and social engagements. Connect with colleagues and people you meet on LinkedIn. You never know where you might find your dream data science position, so it doesn’t hurt to let people know you’re looking. 
  • Research companies. Do your homework. Learn as much as you can about a company’s culture, benefits, products and services, etc. This is valuable in determining whether or not you see yourself working there. 

It’s also helpful to keep up to date on the latest in data science by reading related news stories and bookmarking informative data science blogs. Articles such as 10 Data Science Jobs – Which One Is Right for You? could prove to be beneficial resources.

Exploring Jobs That Are Closely Connected to Data Science

There are many different pathways for entering data science and specialties in the field. Here are some of the most common:

  • Data Analyst — Often considered an ideal position from which to advance to a data scientist, data analysts evaluate specific data to generate valuable insight.
  • Data EngineerThese professionals are mainly responsible for ensuring that the data used to drive informed strategies, decisions and actions is production-ready — a process that can consist of proper formatting, scaling, resilience and security. 
  • Data ArchitectA data architect creates and oversees the large-scale management plan for a company or organization’s data — both future and current. 
  • Machine Learning EngineerThis type of engineer builds and manages platforms for machine learning projects. 
  • Business Intelligence AnalystThis is a skilled professional who is responsible for transforming data into insights that drive value for their organization and communicating such opportunities to company decision-makers. 
  • Marketing Analyst — These positions examine sales and marketing data to assess the effectiveness of marketing campaigns.

Other positions related to data science include Data Science Manager and Quantitative Analyst.

Showing Your Skills by Starting a Data Science Project

Hands-on experience is important, which means being involved in data-related projects is especially valuable. If you aren’t ready to tackle your own project, Towards Data Science offers these helpful tips: 

  • Get your feet wet with statistics
  • Learn Python and SQL
  • Learn linear algebra fundamentals
  • Learn data manipulation

How to Write Your Resume and Cover Letter for a Career Transition

Your cover letter and resume can make — or break — your chances of securing an interview. Indeed provides some helpful tips in its Career Change Cover Letter Guide. These include:

  • Highlight successes from previous positions. You want to show your future employer how you can add value to the company.
  • Include skills that can transfer to your new position. Remember to include both hard and soft skills. (For example, communication and problem-solving are important, no matter the position or industry!)
  • Incorporate research in your cover letter. Play up your strengths and experience. Tailor your cover letter to the company and position. 
  • Explain why you want to change careers. Here is your chance to tell your potential new employer why you’re looking to change fields or industries. 

The Balance Careers offers these additional tips: 

  • Illustrate your transferable skills. If possible, use anecdotes and examples from your work to show how your skills would translate to this new position. 
  • Express passion for the company. There’s nothing wrong with showing some excitement about a new position and company in your cover letter.

Data Science Tools of the Trade: R vs. Python

Both R and Python are popular tools in the data science world. Created first and foremost for statistical computing, R allows users to implement a variety of statistical and graphical tools that are easily expanded by third-party packages. 

Like R, Python Programming Language is a free software. While R was developed with the goal of creating a statistical programming environment, Python was (and still is) a general purpose programming language with many applications. 

Here’s a helpful explanation from IBM

“The main distinction between the two languages is in their approach to data science. Both open source programming languages are supported by large communities, continuously extending their libraries and tools. But while R is mainly used for statistical analysis, Python provides a more general approach to data wrangling.”

Data Science Career Networking

Networking is one of the most important things you can do in your career — no matter your profession. As Indeed explains, “networking enables you to take advantage of personal and business connections, rather than relying solely on your resume. These connections are not only beneficial for you but also for employers, who hire many new employees through networking.”

Here are some helpful networking tips, especially if you aren’t sure where to start:

  • Write down a list of everyone in your network. This includes family, friends, colleagues, former co-workers, neighbors, connections on social media, people you volunteer with, etc. 
  • Reach out to your network and let them know you’re in the market for a new job.
  • Be specific and know what kind of company/position you want. 
  • Be yourself and be considerate. 
  • Ask people in your network for help or advice — not job leads. 

Bookmark these links to top networking resources: 

Data Science Career Resources [+ Research Materials]

If you’re exploring a career in data science — or you’re ready to make the switch — it’s important to have the right resources at your fingertips. Here are some to bookmark:

This report was brought to you by the University of San Diego’s Master of Science in Applied Data Science, which is taught by experienced data science practitioners and can be tailored to the needs of specific industries and employers.

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