Are you an Aspiring Data Scientist ? Here is what you need to know


Most of us are now quite familiar with terms like Big Data , Analytics, Data Science and Data Scientist . The United States White House recently announced Mr. D. J. Patil (@dpatil) as the first U.S Chief Data Scientist (white house blog). According an article in Harvard Business Review, Data Scientist has been considered as the Sexiest Job of the 21st Century(HBR). Most of the tech savvy companies including Google, LinkedIn , Facebook and Twitter have developed their internal data science teams which are headed by Chief Data Scientists or Chief Data Officers and many other non-tech companies from different sectors including retail, healthcare, aviation, logistics and human resources are in process of developing their internal data science and analytics teams to have the power of data driven decision making. So if you are looking for a career in data analytics and you are an aspiring Data Scientist here are top 10 things you may find useful.

1] Join a Masters Program : With the rise of Big Data the demand for data analytics professionals is rising everyday and many top universities have started specialized programs in data analytics and data science field to cater this demand. Most of these Masters programs can be completed in 1 year, which focus not only on the technical skills required to be ready for analytics profession but the programs also equip students with business fundamentals and project management and leadership skills which are equally essential in this profession. Few select university programs are as below and readers can find many more similar programs from web search.

1. Master of Science in Business Analytics by University of Minnesota 2. Master of Science in Business Analytics by University of Texas Austin 3. Master of Science in Analytics by Georgia Tech University 4.Master of Science in Analytics byNorthwestern University

For those of you who are working full time at present and are interested in joining a Masters program there are few universities who offer online programs in Analytics.For example: Northwestern Predictive Analytics. Here is a consolidated list of most of the Analytics programs in United States complied by NCSU ( List )

2] Start Learning R and Python : R and Python are the two most widely used programming languages used by lot of analytics professionals. If you are an undergraduate student thinking of pursuing a Masters,a college kid or an experienced professional, having these languages in your armory is going to be a huge advantage for you. There are lot of Online courses available on Coursera , Edx and many other MOOCs. Code School is one more source where you can learn bunch of programming languages. Here is a introduction to R

3]Learn From Peers : Lot of professionals in data analytics field are posting their work on their blogs, or sharing the links of their git-hub repositories. You can find many useful posts on blogs like R-Bloggers , FiveThirtyEight , Revolutions and websites like Kdnuggets.

4]Join Data Science Competitions : There are lots of different forums like Kaggle, CrowdAnalyticx , DrivenData etc. where you can participate in data science competition and even win some cash prize.You can learn a lot by applying your knowledge on real data sets on there forums and also comparing yourself against your peers. There are lot of useful forums and discussion boards on these platforms where you can learn new techniques and approaches to a problem shared by the peers .

5] Read Case Studies and Articles in Analytics : “In theory, theory and practice are the same. In practice, they are not.”-Albert Einstein. Knowing the techniques and theory about applying the models and tweaking the parameters and everything is one thing but when it comes to application of it from grass root level, a person needs to understand the scenario from business perspective and this is where learning from what others have done becomes useful. Having the domain knowledge of the industry, know small details about the practices used in that industry can be very useful when solving a industry specific problem.Reading case studies and articles on how analytics was implemented can give reader the sense of application of techniques learnt in theory. There are lot of case studies available on HBR as well as those shared by top consulting firms including Mu Sigma, McKinsey and Accenture etc.

6]Follow the Leaders in your Field : There is no better way than learning from experts to stay updated about latest trends and happening in your industry. Follow the influencers and experts in analytics, on social platforms like twitter. Here is a list of top 10 influencers in predictive analytics shared by dataeconomy.

7] Network, Meet People, Attend Conferences : Networking with people in analytics profession, joining local groups on websites like to find like minded people can be a great source of knowledge. If possible and affordable it is recommended to attend analytics and data science conferences to stay abreast of latest happenings. Starta , HP Vertica are few of the top conferences that happen every year.

8]Develop the Ability of Story Telling : Data Scientist is someone who is not just required to understand the technical and business aspects of the problem, but also needs to explain the end results to top management and CXOs in the form of a story that will enable them to take right business decisions. So start practicing explaining complex business problem and its solution to a person who don’t know anything about it.

9]Write Blogs , Share Articles : Writing blogs about your work and sharing with it with others is a great way to showcase your work as well as to help others to learn from you. Writing a solution to a complex analytics problem you solved and explaining it in the form of a story can be a great practice to your story telling skills as well.

10]Learn to Ask Why: This is the most important skill that all the leading data scientist possess. They want to know why something is happening the way it is happening , whether it is increased sales, increased response to some advertising campaign or sudden drop in clicks. As a Data Scientist one needs to have that curiosity of understanding why it is happening this way and using the data to find the answer to that WHY.

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*Image credit: Data Scientist graphic. For Bersin by Deloitte, Deloitte Consulting LLP. 2014,


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