How to Become a Data Scientist

How to Become a Data Scientist
How to Become a Data Scientist

Data science shifted from the academic world to the general public in the early 21st century. According to Google Trends, searches for the term ‘data scientist’ went from being almost non-existent in 2004 to becoming one of the most searched in 2020. Inquiring minds wanted to know more about the field starting, naturally, with the question of how to become a data scientist.

The steady growth in public interest coincides with increasing flows of data from everyday activities. Accumulated global data grew from 4.4 zettabytes in 2013 to 44 zettabytes in 2020. From smartphones to smart traffic lights, tidal waves of data are flowing into databases around the world.

Data scientists develop the methods for converting data into insights and recommendations. Analytical minds armed with graduate degrees and abundant curiosity find a variety of data science career paths on the horizon. Answering the question of how to become a data scientist starts with learning about the day-to-day tasks of its practitioners before understanding what data scientist qualifications involve.

How Do Data Scientists Spend Their Days?

The daily routine of the typical data scientist combines foundational work with analysis. Anaconda’s 2020 State of Data Science report asked 2,360 data science professionals about their most common tasks. This survey provides a snapshot of how data scientists spend their time during an average workday, including: 

  • Data cleansing (26%)
  • Data visualization (21%)
  • Data loading (19%)
  • Model training and scoring (12%)
  • Deploying models (11%)
  • Model selection (11%)

Daily tasks for data scientists reveal this career’s blend of mathematical and programming knowledge. The key to becoming a data scientist is to master both areas and find new opportunities for making data work for others. Former Google and Slack engineer Josh Wills has underlined this point by saying:

“A data scientist is someone who is better at statistics than any software engineer and better at software engineering than any statistician.”

The good news for anyone in the process of becoming a data scientist is the profession’s growing presence across different industries. Anaconda’s survey determined that 77% of its respondents were employed across 15 industries. Automakers, government agencies, and department stores alike share the need for data practitioners.

Becoming a Data Scientist to Fill Talent Shortages

Broader hiring of data scientists has led to a shortage of qualified practitioners for available jobs. Chief data scientists surveyed by Wing place hiring as their second biggest challenge after stakeholder collaboration. This concern stems from the recurring issues presented by a surveyed executive:

“Who are people we can attract, who are people we can retrain internally, and how do we set them up for success?”

QuantHub found that there were three times more data science job postings than job searches in 2020. Applicants are learning how to become data scientists but not fast enough to keep up with demand. Several trends are leading to demand outpacing supply including:

  • A 74% annual growth in hiring for artificial intelligence from 2015 to 2019
  • 83% of surveyed companies investing in big data projects by 2020
  • A two-year average turnover for data scientists due to promotions and new opportunities

The Bureau of Labor Statistics (BLS) estimates a 31% growth in data science openings from 2019 to 2029. A driving force to becoming a data scientist is laying the groundwork for future innovation. Data science career paths are open to qualified practitioners ready for global challenges.

Skills Needed to Meet Data Science Qualifications

An important step in becoming a data scientist is building a portfolio of in-demand skills. Graduate programs in computer science such as Baylor University’s online Masters in Computer Science Data Science track can help aspiring practitioners make the leap from the classroom to the workplace. At this point, the question is, “What skills will meet data science qualifications in the future?”

SAS offers a good primer on the essential skills needed for data science jobs. Every successful data scientist excels in the following areas:

  • Database management
  • Data visualization and reporting
  • Python, R, and other coding languages
  • Machine learning
  • Statistics
  • Hadoop and MapReduce

Newcomers to the field should add skills beyond the essentials to keep up with their colleagues. IBM and Burning Technologies go further by answering, “What does it take to be a data scientist in the future?” Their report on talent shortages found the following skills led to salary premiums for data scientists:

  • Pattern recognition
  • Object-oriented analysis and design
  • Quantitative analysis

Data science’s expansion into new fields has led to an emphasis on interpersonal and management skills. Harvard Business Review identified the gap between creating insights and reaching audiences as a significant challenge in the profession. Fast risers in data science know how to communicate their findings to target audiences while collaborating across departments.

Choosing the Best Data Science Career Path for You

Employer demand for skilled applicants creates an ideal situation for data science professionals. Glassdoor’s evaluation of more than 16,000 data scientist salaries found an average base salary of $116,050. A high-end salary of $165,000 shows the long-term return on investment from becoming a data scientist.

The best way to achieve data science qualifications that meet employer needs is a master’s degree. Kaggle’s 2020 survey of data science professionals found 51.1% of respondents held at least a master’s degree in a related field. Graduate degree programs combine skill development and real-world exercises that guide students toward thought leadership.

The data scientist job title will likely evolve in the near future as specialization becomes necessary. PepsiCo’s Steven Finkelstein offers several reasons why future data scientist are heading toward different job titles:

  1. Undergraduate machine learning courses will trickle up to graduate programs
  2. Automation will take data management tasks off practitioner hands
  3. Rapid technological growth will make specialization essential to efficiency

Finkelstein sees data science splitting into software engineering and decision scientist paths. In this vision, software engineers develop large-scale systems while decision scientists specialize in answering questions with incomplete data. These data science career paths will split further and create specialty positions for future practitioners. 

Data science will infuse non-technical areas of the public and private sectors as data-driven decisions become commonplace. Julien Kervizic, the Head of Engineering at iptiQ, divides the future of data science jobs into four axes:

  • Business
  • Data
  • Engineering
  • Product

Early-career data scientists looking for out-of-the-box challenges can aim for the Business and Product axes. Marketing and supply chain managers are increasingly reliant on data science, creating new data science career paths. Data practitioners who excel at design, product development, and communications find opportunities as product managers.

Opening Data Science Career Paths at Baylor University

A forward-thinking graduate degree program meets future data science qualifications. Baylor University’s online Masters in Computer Science combines thought-leading faculty with innovative course design. Degree candidates work with emerging leaders in the data science field without setting foot in a classroom.

Your time at Baylor University starts with core courses on topics like machine learning and software engineering. The online Masters in Computer Science includes a data science track focused on this ever-growing field. This track includes the following classes:

  • Applied Data Science
  • Cloud Computing
  • Data Mining and Analysis
  • Data Visualization

Baylor University’s national reputation places computer science graduates in a great position for data science jobs. U.S. News & World Report ranked the university No. 25 among Most Innovative Schools in 2021. The popular publication also ranked Baylor at #76, placing the university in the top 5% of Best National Universities.

If you have questions about becoming a data scientist, the required qualifications, or career paths open to you, Baylor's program guide has answers. 

Contact Baylor University today to learn how to become a data scientist through its innovative degree program. Our enrollment advisors are always at hand to help.