Essential Skills You Need to Know for Data Science

22nd October, 2020

Data Science is such a broad discipline that includes several divisions like data structure and data research, artificial intelligence, data visualization and presentation, predictive analytics, and machine learning, etc. But what exactly are the essential skills you need to know to become one?

The field of data science has endured for at least a decade in its current pattern. 

There is a long list of academic, technical, and soft skills that may or may not require to be a Data Scientist. But Core data science skills fall into a few buckets: math/statisticsprogramming/coding, and business/domain skills


1. Mathematics and Statistics Skills

Data science is all about applying mathematical theories to the real world. Topics like Linear algebra is the most crucial math skill in machine learning. This algebra is helpful in data pre-processing, data alteration, and model evaluation. Similarly, familiarity with multivariable calculus is vital for building a machine learning model. Several top-rated online portals are providing you the best skillsets and classes, such as AI for everyone on Coursera, or Cognitive Class from IBM.

2. Statistics/Probability Skills

The grounds of data science involve detailed and presumed statistical methods and probability. Knowledge in these areas provides fundamental techniques to use while working with data. Statistics is the process of operating and analyzing a data set to distinguish unique mathematical characteristics (for example, mean or variance). These optimized characteristics then allow Data Scientists or Data professionals to make decisions based on those data characteristics.

Here are the topics you need to be familiar with to be a data scientist: Probability distributions, Hypothesis testing, Bayesian concepts, Statistical significance and Regression.

3. Programming Skills.

Programming skills are inherent in data science. Since Python and R are considered the two most popular programming languages in data science, one must adhere to specific knowledge in both languages.

A very few companies may require in either R or python, not both, but you must learn these both.Data science with python or coding both permits a Data Scientist to convert theoretical knowledge into real-life applications.

This learning provides a substantial understanding of data structure, which is always used in algorithms reading.

4. Business/Domain Skills.

There is a common saying that history must not repeat. After overcoming many failures of data sciences projects, companies are now requiring business acumen in their data scientists, like strategic management of large projects, planning the work in context to business goals. These organizations strongly consider data professional or students, who has some industry or project management exposure.

Check out some of the best free Coursera courses on data science here.

Read our previous blog; How graduates can land their first IT job, for more info!


Apart from this, there are various other highly specific skills such as Machine Learning, Data Wrangling, Preprocessing, Deep learning and Cloud software you can specialise in.  You can practice all of these skills and more by competing in a data science hackathon! Datasets, parameters, and challenge criteria will be available for all competitors to practice their programming, statistics, and business skills. Check out the next hackathon being run here at 

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