In a field as new and evolving as information technology (IT), it may seem difficult to choose the best data science courses for your particular interests. However, a better understanding of the complex field will give you the foundational knowledge to pick the best data science courses to increase your overall skill set.
What is data science?
The search for a concise and comprehensive definition of data science will not yield a simple textbook answer. This is because the field is relatively new and its application in today’s professional environment can take many forms. The field of data science begins with a foundational understanding of statistics and IT continues with the development of computer programming skills and ends with an interdisciplinary application of those skills in a variety of fields from medicine to engineering.
What are the best data science courses?
The best data science courses will be either introductory or specialised. An introductory data science course will give you a broad-based understanding of the different aspects of the field of knowledge you will need for your career, like statistics, information management (systems and technology), analytics (e.g. data, business, marketing), and programming (e.g. optimisation, algorithms, software development), while the specialised courses will explore these aspects in-depth.
Stanford University’s (US) M.S. in Statistics: Data Science degree is a good example of a data science programme that provides a well-rounded course structure. Upon the completion of the programme, a student will have demonstrated knowledge in four areas of data science through coursework in the following categories:
- foundational data science (e.g. Numerical Linear Algebra, Discrete Mathematics and Algorithms, and Randomized Algorithms and Probabilistic Analysis),
- elective data science (e.g. Introduction to Statistical Inference, Introduction to Regression Models and Analysis of Variance, Modern Applied Statistics: Data Mining),
- specialised elective data science (e.g. Data Driven Medicine, Machine Learning, Business Intelligence from Big Data),
- advanced scientific programming and high performance computing core (e.g. Advanced Software Development for Scientists and Engineers, Distributed Algorithms and Optimisation, Parallel Computing.
An Internet search will reveal several sites that rank data science schools or degree programmes, such as the “25 Top Schools with Master’s in Business Analytics Programs” or “The 50 Best Masters in Data Science Online”.
What is the difference between an MBA and MS in data science?
Data science degree programmes can offer either a Master of Science (MS) or a Master’s in Business Administration (MBA) with a concentration in data science or, more commonly, business analytics. If you enjoy the more technical side of data science, such as the development of algorithms or complex programming, you will likely prefer an MS degree programme. Alternately, you might enjoy the analytical side of data science, such as making strategic decisions regarding the application of information to a particular business or forecasting future trends in IT, in which case an MBA degree programme might be better suited for you.
Methods of learning
Typical of most degrees, you can find both online and on-campus degree programmes, both full-time and part-time coursework, or some combination of those.
You will find that the majority of data science degree programmes focus in some way on business applications. That is because so many companies need a data science expert that not only can understand the skills of statistics and programming, but can also apply those concepts in various areas of their business, like marketing and software development.
On-campus data science degree programmes
Many data science degree programmes are offered strictly on-site at the B-school or university, either because they value foremost the personal interaction integral to learning or because they have simply not transitioned their educational model to include online courses. The Schulich School of Business at York University (Canada) offers an on-campus Master of Business Analytics that should provide you with the education necessary to “master quantitative and technical expertise, coupled with the skills required to influence key decision makers, inform strategy and improve business performance.”
Online data science degree programmes
The evolution of education appears to be headed towards the availability of online courses and degree programmes, to a point where a student in the US can earn a Masters degree from a European university without ever leaving the country. Online data science degree programmes include a Global Master in Business Analytics and Big Data from the IE School of Human Sciences and Technology (Spain), designed to help you “understand all business functions and get highly specialised analytical skills to play a key role in the upcoming data-driven era,” or a Master of Science in Data Science and Computational Intelligence from Coventry University (UK), designed for students “to pursue a research and development career.”
A massive open online course (MOOC) is a burgeoning method of delivering course material on a variety of topics, including IT fields such as data science. MOOCs are strictly online classes characterised by unlimited participation and open access via the Web. They are offered by both traditional universities and specialised content providers. Although the course content is usually free (typically with website registration), providers can be both non-profit, like Khan Academy, or for-profit, like Coursera.
Some MOOCs are linked to degree programmes, among them the Master of Computer Science in Data Science at the University of Illinois at Urbana-Champaign (US). However, most are stand-alone courses, such as the wide range of courses specific to the teaching of algorithms found on Khan Academy’s website.
A MOOC is a great way to get an introduction to a field like data science to see if the material fits your unique proficiencies and interests or a way to increase your specialised knowledge of that field to further your career. However, knowledge without a degree is a difficult sell to employers searching for a data science expert, even with the somewhat limited supply of such experts in the employment market today.
As blogger Justin Megahan wrote in 2016 regarding the evolution of data science within the financial trading field, “a few decades ago, quants (statisticians working at quantitative analysts) were crunching numbers in windowless rooms and passing on their results for others, often financial traders, to take action on. Today, data scientists [write] the algorithms to ingest real-time data, crunch the numbers, and make trades, all automated, all within fractions of a second.”
The field of data science exists where the knowledge of statistics, analytics, programming and business intersect. Each aspect is crucial to the overall understanding and application of data science in the modern world, and modern data science experts must combine big data with analysis and then apply that knowledge to make business decisions or take action through strategy development or programming. Not a small task, to be sure, but there are many sources for you to gain the knowledge you need to become an expert data scientist.