Aritra Kumar Sinha

Hi,

First of all thanks for the question. Data Science as a subject has multiple layers. A great way to get started would be to brush up basic statistical concepts. Fundamental concepts of probability, distributions and Hypothesis Testing can get you started working on certain Data Science  problems. For example, if a marketing campaign has taken place during Diwali and some customers are given some offers , what is the best way to measure the impact of the campaign? How can we say that the impact was statistically significant ? 

Problems of supervised and unsupervised learning however requires deeper understanding. Multiple online available sources can help you get clarity on concepts. The key thing is to know what topics to search for. Here is a starting  list of topics you would want to research on with regards to unsupervised learning

   Principal Component and Factor         Analysis

   Cluster Analysis

    Conjoint Analysis

    Multi Dimensional Scaling

Topics on Supervised Learning could be and not limited to 

   Regression (OLS and GLM) 

   Non Parametric methods such as

      Decision Trees

      Random Forest

       SVM

       Boosting Techniques

Once you get the gist of the concepts on these topics , it will be much easier for you to grasp the discussions in any training program if you join one. In terms of programming Python is agreat platform .A few years ago, SAS was almost always important but things have changed now. SkLearn offers great set of packages in Python to play with. I'd encourage you to try and apply algorithmic approaches to create your own functions in Python. In conclusion, in my experience Data Science has been a journey and not a cluster of skills. Over time it will become a part of your thought process and will guide you every step of the way. Keep learning and keep practicing. 

If you need to discuss anything further - reach out to me.

Aritra