In a world that’s increasingly reliant on technology, data scientists are pioneers of technological change across a huge array of industries. They solve modern problems, analyze trends, and extract meaning from large and complex sets of data. As a growing number of companies commit to data-driven development, and as they collect and compile more information via the internet, there’s ample opportunity for data scientists to flex their analytical skills and computer science abilities.
In this position, individuals integrate advanced information technology (IT) knowledge with other proficiencies — from communication and data visualization skills to algebra and programming capabilities — to interpret various sets of data. They then apply the information they glean to the development of creative, evidence-based solutions with wide applications. You’ll find data scientists working in fields as diverse as biotechnology, energy development, transportation, and telecommunications.
What data scientists do varies depending on their specific training, interests, data science skills, and where they focus their careers. Regardless of their field, these professional data wranglers are vital in today’s technology-driven, solutions-oriented world. Those interested in learning how to become a data scientist need to take certain steps to prepare to pursue a role in this dynamic field, such as investing in an advanced data scientist degree.
Data Scientist Job Description and Responsibilities
Data scientists must consider the nuances of structured and unstructured data, while also applying skills gained through the study of statistics and machine learning. As they clean, combine, and consider the numbers, data scientists strive to eliminate any biases in the data, with the ultimate goal of extracting meaningful information, which they can then put to use.
This field requires persistence, curiosity, and a desire to uncover the hidden meaning in a wealth of raw data. What data scientists do is complex and often varies by position. That being said, there are a few key tasks common to most data scientist job descriptions.
This is the jumping-off point for any data science project. By identifying a specific and measurable problem about the business, it provides a specific question for the data to answer. For example, a company may want to know why its fourth-quarter sales did not meet projections for a certain software product in a specific region of the country. Once the specific problem is identified, the data scientist may then consider how the solution can be found using data science.
Data Collection and Organization
Data collection skills are essential to becoming a data scientist. Once data scientists understand the variables and what information they’re looking for, they can devise the best approach to collecting the data, which can come from one or multiple sources. They might formulate machine learning algorithms, create model experiments, or otherwise conduct research to get to the heart of the problem and provide a solution. Cleaning and validating the data for accuracy is another critical part of this process.
During this phase, the data scientist (or data analyst) manipulates the data using a variety of techniques and analytics tools. The goal is to find meaningful insights via the correlations, patterns, and trends in the data. Many data scientists use data visualization tools so that they can see different graphical and visual representations of the data they are analyzing.
The goal of data interpretation is to answer the specific question posed during the problem identification phase. Data interpretation identifies the solutions and opportunities that the data scientist can present to their organization’s leadership and stakeholders, which they can then use to make vital business decisions and strategy adjustments.
Steps in How to Become a Data Scientist
When considering how to become a data scientist, be aware that you’ll need to take several steps before seeking work in this growing, information-driven field. Most people who pursue the career path to become a data scientist start engaging with the concepts through both formal education and hands-on, independent practice. It’s helpful for aspiring data scientists to possess an interest in programming, mathematics, and complex computer systems from the outset. In addition, most professionals in this field complete the following steps before landing a position as a data scientist:
1. Complete an Undergraduate Degree
Many people get their start on this career path by earning an undergraduate degree in data science. This foundational data scientist degree generally provides a background in information technology, mathematics, computer science, and statistics. Each of these disciplines speaks to some of the skills required for the data scientist role.
2. Gain Experience with an Entry-Level Job
Getting on-the-job experience is important in most professions, and data science is no exception. Most people find that it’s useful to spend a few years post-graduation developing their programming and analytical skills. As a junior data scientist, you’ll have the chance to get specific, hands-on training, and you’ll gain a deeper understanding of what it takes to work at a high level in the field. The main areas to develop skills in as a junior data scientist include:
- Big data
- Data manipulation and analysis
- Data visualization
- Deep learning
- Machine learning
- Software engineering
- Statistical methods
3. Pursue a Master’s Degree
The data science field tends to afford more opportunities to those with advanced degrees. Continuing your education at the graduate level allows you to learn more about important topics that are valuable in an upper-level position, such as predictive analytics and model building. Gaining advanced knowledge by earning a data scientist degree such as an online master’s in data science can help you advance your career.
4. Never Stop Learning
Technologies change fast, and so do the needs of organizations that employ data scientists. If you want to become a data scientist, you need to cultivate a desire to continue your education in the field. In this ever-evolving sector, the drive to always learn and grow will help you stay competitive in the job market and keep you engaged with the dynamic world of data. Ways to stay up to date include:
- Subscribing to publications and newsletters that cover data science and related topics
- Listening to podcasts about data science
- Attending conferences pertaining to the world of data and artificial intelligence (AI)
- Reading research papers that are relevant to the data science profession
- Following leaders in the field of data science on LinkedIn
Data Scientist Salary
Data scientists are in high demand for their ability to design creative, data-driven approaches to real-world problems. This work’s value is reflected in the average salary of a high-level data scientist. The median annual salary for computer and information research scientists was $131,490 in 2021, the U.S. Bureau of Labor Statistics (BLS) reports. In March 2023, the median annual data scientist salary was about $99,000, according to Payscale, with top earners in the profession making more than $137,000 per year.
While data science is generally a lucrative profession, data scientist salaries can vary. Pay often depends on the industry, level of experience, level of education, workplace environment, and region. It’s also worth noting that those pursuing a role as a data scientist are entering an in-demand field. Computer and information research scientist jobs, such as data scientist jobs, are projected to increase 21% between 2021 and 2031, according to the BLS. This represents a much higher rate of growth than the average 5% increase the BLS projects for all professions.
Data Scientist Degree and Curriculum
The Master of Science in Data Science degree curriculum at Maryville University teaches students the advanced concepts and skills necessary to become a data scientist. Developed in collaboration with industry leaders, this 36-credit-hour degree program covers everything from machine learning to predictive modeling. The courses in the program include:
- Math Modeling
- R Programming
- SAS Programming
- Text Mining
- Big Data Analytics
When researching how to become a data scientist, you’ll likely discover that it’s worthwhile to invest the extra time and effort in earning an advanced data scientist degree. Although entry-level work can be secured with an undergraduate degree, most employers prefer candidates who have received a formal education on the advanced concepts and methods of data science that are featured in master’s programs.
Learn More on How to Become a Data Scientist
If you’re an aspiring data scientist or just looking to advance or pivot your career, consider an advanced degree in the field of data science. Earning an online master’s degree in data science from Maryville University can equip you to help companies and organizations leverage data and technology to make critical decisions. Additionally, data scientists work in one of the fastest-growing professions in the country. Those who make the commitment now can begin to prepare to join the thriving and high-paying field of big data.
Learn more about the program so you can begin pursuing your professional goal of becoming a data scientist who helps organizations make critical decisions.