2How 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.

So, how to become a Data Scientist?

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. Here are some of them: Data IncubatorInsight Fellowship, Metis Bootcamp.

Here are also some paid resourses. 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.

A cheat sheet of becoming a Data Scientist through your own efforts:

  • 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.

  • 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.

  • Watch these 13 Ted Videos

  • Watch this video of Hans Rosling to understand the power of Visuali...
  • Listen to weekly podcasts by Partially Derivative on Data Sciences

  • University of Washington’s Intro to Data Science and Computing for data analysis will be a good start

  • Check out Measure for America to gain an understanding of how data can make a difference

  • Religiously follow this infographic on how to become a data scientist

  • Read this blog to master your statistics skills

  • Try to complete this open source data science Masters program

  • Do this Machine Learning course at Coursera by the co-founder Andrew Ng of Coursera himself

  • By all means, complete this Data Science Specialization on Coursera, all nine courses, and the capstone

  • 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

  • To understand the deployment side of data science applications Youtube Amazon Web Services and free trainings are a must to do

  • Process Mining

  • Try to read Data Science Central once a day, articles like this can save you a lot of time and discussion in interviews

  • Try to compete in as many data competitions as you can

  • To put a cherry on the cake, these statistics driven courses will help you in differentiation from all other applicants – Inferential StatisticsData Analysis and Statistics

  • Follow the following on Twitter for Predictive Analytics: @DataScienceCtrl, @analyticbridge, @mgualtieri, @doug_laney, @Hypatia_L eslieA, @hyounpark, and @anilbatra

  • Follow the following on Twitter for Big Data and Data Sciences: , Vincent Granvill, Alistair 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 Sadkowsky, William McKnight, Yves Mulkers

source: https://www.datasciencecentral.com


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