Category: Analytics

Dear Future Data Scientists

Be a data scientist in 6 months. Learn R or SAS or Python in 6 weeks. Learn Data science by doing one capstone project on one dataset.

Sorry mate, there are no short cuts to success.

Your real data science journey begins AFTER you learn the statistics AFTER you learn the techniques AFTER you learn the tools like R/SAS/Python.

A couple of datasets like Iris / Boston / German Credit / Scraping Tweets wont do it. A few weeks on kaggle wont do it.

You probably need to spend a few more months on Kaggle and a few more months on competitive programming like www.hackerrank.com will bring your data science dreams closer.

Disclaimer-I have interviewed potential data scientists and I have taught on some of these kind of courses. #datascience #python #programming #r #statistics  #datasets

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Internships are like magic for budding data scientists

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Internships are the magic that a student to convert to data scientist. Not everyone can solve Kaggle contests while still in engineering or other schools. Not everyone has the money to pay for private institute’s training which basically teach R, Python or SAS but not analytical thinking when confronted with a messy real life dataset.

A project in such training is worth much less than the experience of internships.

Companies who have existing data science teams should also try and give internships to create a  steadier supply of data scientists for their operations besides building a bigger brand in data science recruitment.

Meetups and LinkedIn groups( Facebook groups) are good places to offer internships and students should make attending Meetups as a pseudo proxy part of curriculum with the goal of landing internships in each year of their summer/winter break. #analytics #recruiting #python #r #meetup  #engineers #internships #datascience

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 

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WHAT DO YOUNG BUDDING DATA SCIENTISTS WANT?

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.

https://www.anaconda.com/anaconda-data-science-certification/?mkt_tok=allo