Data Science Updates

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

Being a B.E. Computers it was natural to get a job in an IT organization. Got to work on several technologies since last 14 years and understood the Insurance and Finance (Investment Banking) domains. To sum it all, the applications we created were more or less CRUD operations on data, a bit of data massaging and data presentation on Dasboards. Somewhere around 2014, while working for an IB, got introduced to concepts of Variations/Deviations, Monte Carlo simulations, Anova etc which are normal to Quantitative analysis for banks. These were statistical concepts overall to understand the variations amongst data. It helped me overall to draw conclusions on historical bank data or features and find correlations. However, what was missing was explaining the behavior in different scenarios or in presence or absence of some features (or data varibles to be simple). Given some statistical background  i could dive into the easiest of Machine Learning techniques like Regressions (Linear, Logistics). All the efforts were to perform the  exploratory data analysis. Around in a year since, I decided to get my hands dirty into predictive analysis, in essence to analyze the trend and forecast an event or numbers given historical trend. Exposing myself to decision trees, SVM , clustering algoritgms like KNN, apriori and FP for rules mining, I got to understand the world of ML. Initially, I could create models but improvng the accuracy and tuning them furthermore was a challenge. That's when I started taking the concepts seriously and tried to grab some material to grasp mathematics behind those models, which took around 6-8 months but was worth it. Now, having explored the world of as AI and ML, with sound industry experience, I have been training data science aspirants through reputed learning centres and few private tutoring for professionals. Recently, I have also began work on predictive analysis on my stock portfolio, in attempt to run a start-up.

The journey from IT peprogramm to a data scientist wasn't as easy as picking a book and reading about, but required good amount of efforts to practice on variety of data.

For a non IT professional, additional challenge would be to grasp a programming language, like R or Python, which is must to try you hands on the concepts.

Best of luck!!