Answer: There are a number of different ways to pursue a career in data science, including designated master’s degree programs in data science and analytics, private bootcamps and data science training programs, and free online courses and webinars. The process of becoming a data scientist involves learning linear algebra, statistical analysis, and probability theory; cultivating specialized computer programming proficiencies; and developing a familiarity with data mining, data storage systems, and various ways to manage and sort large and complex data sets. Master of Science (MS) in data science degree programs are designed to integrate these elements into a coherent data science curriculum. However, it is not uncommon for data scientists to emerge from bachelor’s and master’s degree programs in mathematics, statistics, and/or computer science/computer engineering/computer programming. There are also numerous web-based resources, including massive open online courses (MOOCs), which can provide data science training to motivated individuals who have a strong background in mathematics and computer programming.
The field of data science in its current form exists at the intersection of information and data collection technologies, computer programming, and sophisticated mathematical and statistical analysis techniques. It is a multidisciplinary field in which knowledge and insights are extracted from raw information through the application of mathematics, statistical modeling, and computer science. This method of scientific data analysis has applications in business and finance, marketing and advertising, healthcare and public policy, scientific and medical research, manufacturing and engineering, and many other areas.
A data scientist is someone who can use the tools of linear algebra, multivariable calculus, and probability theory to collect, organize, and interpret data, draw scientifically based conclusions, and communicate the findings to others. Working in data science requires computer programming capabilities, an understanding of data mining processes and data storage systems, and an ability to use data visualization and presentation techniques. Data scientists typically work with R, Python, and Java programming languages; use SQL and Excel to manage and manipulate data; and are adept at utilizing algorithms to facilitate machine learning for artificial intelligence applications. In addition to cultivating specific technical proficiencies, successful data scientists learn how to frame questions and pose hypotheses that can be effectively addressed through the scientific analysis of data and the application of information technologies.
Training to become a data scientist generally begins with a foundational understanding of probability theory, statistical modeling, and advanced mathematics, including linear algebra and multivariable calculus. These are the basic tools used in data science to extract meaning from raw data and to draw conclusions based on scientific evidence. Much of this work is done using computers and data collection and warehousing systems. The two most common programming languages employed in this work are Python and R. There are other programming languages and software platforms used to create algorithms, process data, and visualize the results, including Excel, Java, Hadoop, Tableau, IBM’s SPSS, and Alteryx Designer. SQL (Structured Query Language) is the tool most commonly used to query database systems, an activity that is central to data science. Finally, becoming a data scientist often involves learning about the problems data science can solve, the questions it can answer, and the mistakes that can be made through the faulty application of data science.
Master’s in data science programs have emerged as one way to learn the various skills and proficiencies that are used in the field. These program are offered online, on-campus, and in hybrid formats that include some online coursework and some on-campus classes. A master’s in data science generally takes one to two years for full-time students to complete and many programs culminate in an applied capstone project that allows students to apply what they have learned in the program to a data science problem of their own choosing.
There are also master’s in computer science programs with a specialization in machine learning that can provide much of the training needed to work in the field of data science, and master’s program in related fields like applied statistics and computer engineering that can be a stepping stone to a data science career. Graduates from non-data science master’s programs and can receive additional preparation for a career in data science by attending a data science certificate program at a college or university that offers this option. Some schools also offer data science certificate programs for students who hold a bachelor’s degree and who have strong quantitative, analytical, and computer programming skills.
While master’s level training in data science may provide an advantage to those pursuing a career in data science, it is not a requirement. Another pathway to developing the skill set needed to work in data science might include some formal academic training at a college or university followed by a data science bootcamp, private training course, or certificate program offered by a private company or a non-degree granting institute. This is a less well-defined pathway that might include attending an associate or a bachelor’s degree program and taking mathematics, statistics, and computer programming courses, working with computers and data in an entry-level position, and attending a two to three month data science bootcamp or taking data science training courses through an organization like the NYC Data Science Academy, K2 Data Science, the SAS Academy for Data Science, Dell EMC, the Microsoft Professional Program for Data Science, IBM’s Big Data University, or Springboard.
It is also possible to cultivate the skills necessary to become a data scientist without going to college or attending a bootcamp or another type of formal training program. Self-motivated learners who have an affinity for computer programming and quantitative analysis can develop proficiencies in mathematical and statistical analysis on their own and learn to work with R and/or Python to run the types of experiments that are common in data science. There are numerous data science blogs and online communities that can offer support to those training to become data scientists, and there are MOOCs (Massive Open Online Courses) offered through services like Coursera, Edx, and Udacity that can provide additional instruction in practical aspects of data science.