Brave New World of Data Science

The rate at which technology is changing boggles the mind.

Machine Learning to Deep Learning.

Chatbots and Block Chain.

Cloud Computing to Big Data Distributed Computing.

New open source libraries in R and Python.

The rate of innovation is increasing.

What is really good, is young people today are unafraid to take risks, to start startups than stick to big companies.

The future belongs to innovation. 

#bigdata #machinelearning #deeplearning #cloudcomputing #python #r #futurism 

Why Budding Data Scientists Should Blog?

One thing I always advise my students and internees. Write a blog and keep a social media presence to distribute the content. Why? Because you are a data scientist only when the world recognizes you as one. Writing also improves your ability to express lucidly complicated topics in a systematic manner. How to stand out in a clogged world of data science posts-

  write  1) simple  2) unique 3) useful blogs.

Having created Decisionstats with +1 million page views over the years- I know it works.  Mazel Tov! #socialmedia #datascience #writing #blogging  

A better Iris Dataset for the current era

The IRIS dataset is the curse of teaching data science. It makes applying algorithms very simple. What is needed is a BIGGER DATASET with missing values and many many variables/features that teaches the whole cycle of data science, not just plain machine learning but also data pre-processing , dimensionality, standardization, as well. Ideally the dataset should be bigger than memory (RAM) to teach efficiency as well. #datascience #machinelearning #algorithms

Why India and China will never be friends and always be neighbors?

The king who is situated anywhere immediately on the circumference of the conqueror’s territory is termed the enemy. The king who is likewise situated close to the enemy, but separated from the conqueror only by the enemy, is termed the friend (of the conqueror). — Kautilya, Arthasastra

Befriend a distant state while attacking a neighbour – Sun_Tzu, Art of War the twenty-third stratagem of thirty-six


What do young and budding data scientists want?

-To be mentored by experienced data scientists on acquiring skills what to study and where to study given the huge number of choices in studying data science (videos, Moocs, classroom,online education) at different price points (from 0$ to 10000$). surprising number of would be data scientists in India dont know basics of git or linux even though it is free at places like codeacademy. surprising number of experienced data scientists in India do not give back to community in meetups or webcasts even though doing so would increase their brands.

-many institutes offer huge number of courses ranging from 400$ to 10000$ programs. budding data scientists need some sort of protection from confusion created by unethical marketing promises

– students need capstone projects, how to do competitions like kaggle, hackathons, open datasets to practice skills above. competitive coding, data structures and algorithms is the next round of knowledge to be acquired to get interviews.

– opportunity to showcase their skills and output in internships. A single data science post on LetsIntern gets ~300 applicants!

Lets all try and give knowledge if we are experienced, and lets all try to humbly work hard to acquire knowledge if we are new. This is next revolution in information technology due to cloud, big data, open source, machine and deep learning, and AI.

Lets make knowledge equitable. Thats the only way it grows.

Finally a credible data scientist certification

Anaconda announced their exhaustive and superb data science certification. While Big Data and other fields have had certifications , data science only had expensive tutorials and training but no certification. This will be a game  changer in the data science training industry.