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Data Science Education in the Present

Data Science Education in the Present

With internet, Internet of Things (IoT), social media booming and majority of online transactions, we are living in an ocean of data. It is natural to ask that what value this data has? If it has some value then how do we extract that? More importantly how can one interpret and use the results for the benefit of the organization/business and of the consumer.

In came, Data Science – a field that gives an organization the muscle power to process, analyze and derive insight to take effective decisions.

Data Science became very popular after 2012 when Harvard Business Review called it to be the most sought-after job of 21st century. Jim Gray, an American computer scientist consider Data Science as a new paradigm of science. Data Science provides students an opportunity to learn and meet the rising demands of the industry for this particular subject.

A successful Data Scientist needs a unique set of analytical skills, technical and business understanding to effectively work on massive data sets for actionable business insights. In this blog, we are going to look at the present state of the Data Science education. Data Science is an interdisciplinary field, primarily consists of Mathematics, Computer Science, and Domain Expertise. Each of these disciplines is important in Data Science work and optimal use of each of these has its own contribution for an optimal and evolved Data Science product.

Data Science Education


Mathematics for Data Science mainly comprise of Probability, Statistics, Multivariate Calculus, Linear Algebra, and Discrete Mathematics. This knowledge of mathematics is the basis for Data Science stages, e.g. analysis, statistical testing, visualization, and Machine Learning, etc. Various skills are used as job search terms at four main job portals in 2018 ( see Figure 1).

Figure 1. Data Science Job Search Terms 2018 at four main job portals.

Programming Languages

Formal education of the programming languages as part of the Computer Science discipline started in the 1960s. Data Science uses different computer programming languages, some of these are more

preferred than the others due to various reasons. Some of the programming languages that we use in today’s time are R programming, Python, SAS, JAVA, MATLAB®, SQL, NoSQL, Julia, Scala, C, and F#. Most in demand programming languages for Data Scientists in 2018 from four main professional job portals ( see Figure 2).

Domain Expertise

Domain Expertise is the third and last aspect of Data Science. Experts claim that it is more important than any best-sophisticated Machine Learning algorithm. Data Scientist must understand the information that they are processing. Domain Expertise plays a crucial part in a good Feature Engineering and the model quality is a function of features that we use to train a Machine Learning algorithm. Building models without domain knowledge is a risky task and result in a sub-optimal output of limited applicability.

Figure 2. In demand programming languages for Data Science in 2018.

Some of the domains are Finance, Education, Telecom, Healthcare, Retail, Bio-Informatics, and Manufacturing Engineering. Data is coming from various operations of the business. Various sources of data are customer transactions, click streams, sensors, social media, log files, and GPS plots, etc. Data Science skill-sets should be flexible and transferable among domains. Reading literature and attending presentation can boost one’s domain knowledge. Talking and asking questions to the domain experts is another important task a Data Scientist can do to gain domain knowledge and do a good Data Science job.

Some experts claim that the formal university education alone is not sufficient to be a good Data Scientist. One must get some hands-on experience in various industry related projects.

So there are many institutes all over the world that have started offering programmes in data science. At Regenesys we focus on the quality education with a strong focus on the domain expertise, Feature Engineering, programming skills, mathematical background and ample practice on the real-life data sets from various businesses. Additionally, we are also emphasizing on the advanced topics in the Data Science.