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How to Become a Data Scientist - On your own

Big Data, Data Sciences, and Predictive Analytics are the talk of the town and it doesn’t matter which town you are referring to, it’s everywhere, from the White House hiring DJ Patil as the first chief data scientist to the United Nations using predictive analytics to forecast bombings on schools. There are dozens of Startups springing out every month stretching human imagination of how the underlying technologies can be used to improve our lives and everything we do. Data science is in demand and its growth is on steroids. According to Linkedin, “Statistical Analysis” and “Data Mining” are two top-most skills to get hired this year. Gartner says there are 4.4 million jobs for data scientists (and related titles) worldwide in 2015, 1.9 million in the US alone.  One data science job creates another three non-IT jobs, so we are talking about some 13 million jobs altogether. The question is what YOU can do to secure a job and make your dreams come true, and how YOU can become someone that would qualify for these 4.4 million jobs worldwide.

There are at least 50 data science degree programs by universities worldwide offering diplomas in this discipline, it costs from 50,000 to 270,000 US$ and takes 1 to 4 years of your life. It might be a good option if you are looking to join college soon, and it has its own benefits over other programs in similar or not-to-so similar disciplines. I find these programs very expensive for the people from developing countries or working professionals to commit X years of their lives.

Then there are few very good summer programs, fellowships and boot camps that promise you to make a data scientists in very short span of time, some of them are free but almost impossible to get in, while other requires a PhD or advanced degree, and some would cost between 15,000 to 25,000 US$ for 2 months or so. While these are very good options for recent Ph.D. graduates to gain some real industry experience, we have yet to see their quality and performance against a veteran industry analyst. Few of the ones that I really like are Data Incubator, Insight Fellowship,  Metis Bootcamp, Data Science for Social Goods and the famous Zipfian Academy programs.

Let me also mention few paid resources that I am a fan of before I tell you how to do all that for free. First one is the Explore Data Science program by Booz Allen, it costs 1,250 $ but worth a single penny. Second one is recorded lectures by Tim Chartier on DVD, called Big Data: How Data Analytics is transforming the world, it costs 80 bucks and worth your investment. The next in the list are two courses by MIT, Tackling the Big Data Challenges, that costs 500$ and provides you a very solid theoretical foundation on big data, and The Analytics Edge, that costs only 100 bucks and gives a superb introduction on how the analytics can be used to solve day-to-day business problems. If you can spare few hours a day then Udacity offers a perfect Nanodegree for Data Analysts that costs 200$/month can be completed in 6 months or so, they offer this in partnership with Facebook, Zipfian Academy, and MongoDB. ThinkFul has a wonderful program for 500$/month to connect you live with a mentor to guide you to become a data scientist.

Ok, so what one can do to become a data scientist if he/she cannot afford or get selected in the aforementioned competitive and expensive programs. What someone from a developing country can do to improve his/her chances of getting hired in this very important field or even try to use these advanced skills to improve their own surroundings, communities and countries.

Here is my cheat sheet of becoming a Data Scientist through your own efforts:

 

  1. Understand Data: Data is useless and can (and should) be misleading without the context. Data needs a story to tell a story. Data is like a color that needs a surface to even prove its existence, as color red for example, can’t prove its existence without a surface, we see a red car, or red scarf, red tie, red shoes or red something, similarly data needs to be associated with its surroundings, context, methods, ways and the whole life cycle where it is born, generated, used, modified, executed and terminated. I have yet to find a “data scientist” who can talk to me about the “data” without mentioning technologies like Hadoop, NoSQL, Tableau or other sophisticated vendors and buzzwords. You need to have an intimate relationship with your data; you need to know it inside out. Asking someone else about anomalies in “your” data is equal to asking your wife how she gets pregnant. One of the distinct edge we had for our relationship with the UN and the software to secure schools form bombings is our command over the underlying data, while the world talks about it using statistical charts and figures, we are the ones back home who experience it, live it in our daily lives, the importance, details, and the appreciation of this data that we have cannot be find anywhere else. We are doing the same with our other projects and clients.
  2. Understand Data Scientist: Unfortunately, one of the most confused and misused word in data sciences filed is the “data scientist” itself. Someone relate it to a mystic oracle who would know everything under the sun, while others would reduce it down to statistical expert, for few its someone familiar with Hadoop and NoSQL, and for others it is someone who can perform A/B testing and can use so much mathematics and statistical terms that would be hard to understand in executive meetings. For some, it is visualization dashboards and for others it’s a never ending ETL processes. For me, a Data Scientist is someone who understands less about the science than the ones who creates it and little less about the data than the ones who generates it, but exactly knows how these two works together.  A good data scientist is the one who knows what is available “outside the box” and who he needs to connect with, hire, or the technologies he needs to deploy to get the job done, one who can link business objectives with data marts, and who can simply connect the dots from business gains to human behaviors and from data generation to dollars spent.
  3. Watch these 13 Ted Videos
  4. Watch this video of Hans Rosling to understand the power of Visuali...
  5. Listen to weekly podcasts by Partially Derivative on Data Sciences and explore their Resources page
  6. University of Washington’s Intro to Data Science  and Computing for data analysis will be a good start
  7. Check out Measure for America to gain an understanding of how data can make a difference
  8. Read the free book - Field Guide to Data Sciences
  9. Religiously follow this infographic on how to become a data scientist
  10. Read this blog to master your statistics skills
  11. Read this wonderful practical intro to data sciences by Zipfian Academy
  12. Try to complete this open source data science Masters program
  13. Do this Machine Learning course at Coursera by the co-founder Andrew Ng of Coursera himself
  14. By all means, complete this Data Science Specialization on Coursera, all nine courses, and the capstone
  15. If you lack computer science background or want to go towards programming side of the data sciences, try to complete this Data Mining Specialization from the Coursera
  16. Optional: depends on the industry you like to work with, you may want to check out these industry specific courses/links on data sciences, healthcare analytics – intro and specialization, education, performance optimization and  general academic research
  17. To understand the deployment side of data science applications, this cloud computing specialization from the Coursera and Youtube Amazon Web Services and free trainings are a must to do
  18. Do these second-to-none courses on Mining Massive Datasets  and Process Mining
  19. This link will lead you to 27 best data mining books for free
  20. Try to read Data Science Central once a day, articles like this can save you a lot of time and discussion in interviews
  21. Try to compete in as many data competitions as you can
  22. To put a cherry on the cake, these statistics driven courses will help you in differentiation from all other applicants – Inferential Statistics, Descriptive Statistics, Data Analysis and Statistics, Passion drive stats, and Making Sense of Data
  23. Follow the following on Twitter for Predictive Analytics: @DataScienceCtrl, @analyticbridge@mgualtieri, @doug_laney, @Hypatia_LeslieA, @hyounpark, and @anilbatra
  24. Follow the following on Twitter for Big Data and Data Sciences: , Vincent GranvillAlistair Croll, Alex Popescu, @rethinkdb, Amy Heineike, Anthony Goldbloom, Ben Lorica, @oreillymedia., Bill Hewitt, Carla Gentry CSPO, David Smith, David Feinleib, Derrick Harris, DJ Patil, Doug Laney - Edd Dumbill, Eric Kavanagh, Fern Halper, Gil Press, Hilary Mason, Jake Porway, James Gingerich, James Kobielus, Jeff Hammerbacher, Jeff Kelly, Jim Harris, Justin Lovell, Kevin Weil, Krish Krishnan, Manish Bhatt, Merv Adrian, Michael Driscoll, Monica Rogati, Neil Raden, Paul Philp, Peter Skomoroch, Philip (Flip) Kromer, Philip Russom, Paul Zikopoulos, Russell Jurney, Sid Probstein, Stewart Townsend, Todd Lipcon, Troy SadkowskyWilliam McKnight, Yves Mulkers

 

The whole list will take 3 to 12 months to complete and will cost you absolutely nothing, and I can guarantee you that with this skills set you really have to try very hard to remain jobless. Even if you complete half of it, send me a note and I will have something ready for you.

Ball is in your court, it doesn’t matter where you are and how much you can afford, if you want to make at least four times higher the average income of your countrymen, this is the way to do it, at least for next 10 years (where we will be generating 20 TBs of data per year per person versus 1 TB of data per year per person in the last 10 years.)

 

I will write separate articles on Data Science Books (I’ve read 127 of those in last six months) and MOOCs (I am celebrating my 25th MOOC certification today).

For everyone else data sciences is an opportunity, for me it’s a passion

I tweet at @ZeeshanUsmani

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Comment by Murari on April 13, 2015 at 11:20pm

Hi Zeeshan,

Many Thanks for sharing the details. The links are comprehensive and rich in content. I am a big data hadoop developer and keen to pursue data science career . All these details in a single place will help to go for my dream.

Comment by Rajendra Kulkarni on April 12, 2015 at 8:41am

Very nice links and information.. dont have enough words to thank you. Also Bigdatauniversity has  free courses which are nice as well. This will be good enough for any beginner to start and master the details of datascience taking baby steps with links you provided. Adding you on twitter!!!

Comment by Omoyele Shodeinde on April 4, 2015 at 9:03am

Hi Zeeshan Usmani,

You mentioned that one should send you a note after completing up to half of your list. 

Comment by Zunqiu Chen on April 2, 2015 at 7:55am

Thanks for all these sharing. I am a bio-statistician with 6+ year experiences but really keen to dive into this "big data" area. I am going to follow this list to see how far I can complete. Thanks!

Comment by Michael Laux on April 2, 2015 at 12:42am

I am reading news in this area regularly and condense them. The resulting collections are listed here:

http://advanceddataanalytics.net/what-is/

http://advanceddataanalytics.net/ebooks/

http://advanceddataanalytics.net/books/

The reason for the "What is" collection e.g. is, that I often wanted to quickly find out a meaning of an abbreviation in the context of "Data Science" and it turned out to be not always easy.

The "eBooks" collection is just another one to already published ones and contains every (online) book I found in the Internet.

The "Books" collection contains books on my bookshelf and books I am interested in.

Comment by Mitchell A. Sanders on April 1, 2015 at 10:51am

Nice list of links and article. Must say, I'd be pretty impressed anybody could go through these items in 3-12 months (and comprehend anything), but it is a good compilation of resources. Thanks from all the community, I'm sure. I know I learned some things (e.g. the University of Illinois doing a Coursera specialization was news to me.)

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