Currently, on the Web there exists the ability to complete a Masters curriculum in Data Science using Free/Open Source classes, ebooks, workspaces, software.  DataScienceMasters.org provides a full curriculum and links to each resource.  



Will it be an official Masters? No, but an official Masters is not always what is needed, it’s knowledge and experience working with the tools and techniques necessary to actually do Data Science.&nbsp; For some, this free curriculum will allow business-line leaders, Analysts and Programmers from other fields to fill in the education gaps and get better at their job, as well as, one step closer to being an actual Data Scientist.&nbsp;



<h2 class="wp-block-heading">The Open-Source Data Science Masters</h2>



The open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to make data useful.



<h3 class="wp-block-heading">The Internet is Your Oyster</h3>



With Coursera, ebooks, Stack Overflow, and GitHub — all free and open — how can you afford not to take advantage of an open source education?



<h3 class="wp-block-heading">The Motivation</h3>



We need more Data Scientists.



…by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.



— <a href="http://bit.ly/datascienceshortage">McKinsey Report Highlights the Impending Data Scientist Shortage</a> 23 July 2013



There are little to no Data Scientists with 5 years experience, because the job simply did not exist.



— David Hardtke <a href="http://bit.ly/howtohireadatascientist">How To Hire A Data Scientist</a> 13 Nov 2012



<h3 class="wp-block-heading">An Academic Shortfall</h3>



Classic academic conduits aren’t providing Data Scientists — this talent gap will be closed differently.



Academic credentials are important but not necessary for high-quality data science. The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.



We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science,bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available. Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.



And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.

Guide | How to Obtain a Free “open Source” Masters in Data Science

If there were a competition for breathless hype in technology, big data would be the current champion — there’s even a Brooklyn-based band by the name.&nbsp; And though the phrase is ubiquitous in boardrooms and IT departments across categories of companies, the insurance industry is in many ways taking the lead in getting real business value from the volume, velocity, and variety of massive datasets.



Why are insurers taking this challenge on at the same time they are grappling with core-systems transformation, evolving customer expectation and regulatory upheaval? Well, says Pawan Divakarla, big data business leader at Progressive Insurance, “Big data actually does work.” And the results can be dramatic.



According to recent research from Accenture, a third of insurers now are using data from wearable technologies, such as FitBits and Jawbones, to collect lifestyle data from insureds. Insurance telematics is now mainstream — and subject matter experts say the latest big data technologies and techniques offer insurance companies an opportunity to circumvent exhaustive data cleanup efforts that previously have stymied reporting and analytics efforts.



But big data is still difficult, Divakarla admits, and while the technology itself offers great promise, people are a critical element for success. Insurers must develop data workers, not just data scientists, who need to understand programming, where to find data, and how it’s structured, as well as the business issues they are tasked with solving, he explains.



<h2 class="wp-block-heading">10 Big Data Case Studies</h2>



Insurance Networking News, our sister brand, identified 10 insurance companies, across lines of business, that demonstrate true leadership in big data and analytics excellence by developing cross-enterprise strategy, delivering results from the corporate investment, and perhaps most importantly, identifying and recruiting key staff members with the right expertise. The strategies may vary, but the commitment is real.



Here are the case studies:



<ol>
<li><a href="http://www.insurancenetworking.com/news/data-analytics/big-datas-big-guns-progressive-insurance-35951-1.html">Progressive Insurance</a></li>



<li><a href="http://www.insurancenetworking.com/news/data-analytics/big-datas-big-guns-massmutual-35952-1.html">MassMutual</a></li>



<li><a href="http://www.insurancenetworking.com/news/data-analytics/big-datas-big-guns-american-family-insurance-35953-1.html">American Family Insurance</a></li>



<li><a href="http://www.insurancenetworking.com/news/data-analytics/big-datas-big-guns-john-hancock-35954-1.html">John Hancock</a></li>



<li><a href="http://www.insurancenetworking.com/news/data-analytics/big-datas-big-guns-cna-35959-1.html">CNA</a></li>



<li><a href="http://www.insurancenetworking.com/news/data-analytics/big-datas-big-guns-nationwide-35958-1.html">Nationwide</a></li>



<li><a href="http://www.insurancenetworking.com/news/data-analytics/big-datas-big-guns-swiss-re-35957-1.html">Swiss Re</a></li>



<li><a href="http://www.insurancenetworking.com/news/data-analytics/big-datas-big-guns-allstate-35960-1.html">Allstate</a></li>



<li><a href="http://www.insurancenetworking.com/news/data-analytics/big-datas-big-guns-zurich-35961-1.html">Zurich</a></li>



<li><a href="http://www.insurancenetworking.com/news/data-analytics/big-datas-big-guns-qbe-north-america-35962-1.html">QBE North America</a></li>



<li></li>
</ol>

Case Studies | 10 Big Data Projects in the Insurance Industry

You are excited. You have got that much awaited interview call for that dream analytics job. You are confident you will be perfect for the job. Now all that remains is convincing the interviewer. Don’t you wish you knew what kind of questions they are going to be ask?



As co-founder and one of the chief trainers at Jigsaw Academy, an online analytics training institute, I regularly get calls from our students days before their scheduled interview asking me just this. I am going to share with you just what I share with them. Here you go. Below are a few of the more popular questions you could get asked and the corresponding answers in a nutshell.&nbsp;



Question 1. Can you outline the various steps in an analytics project?



Broadly speaking these are the steps. Of course these may vary slightly depending on the type of problem, data, tools available etc.



1. Problem definition – The first step is to of course understand the business problem. What is the problem you are trying to solve – what is the business context? Very often however your client may also just give you a whole lot of data and ask you to do something with it. In such a case you would need to take a more exploratory look at the data. Nevertheless if the client has a specific problem that needs to be tackled, then then first step is to clearly define and understand the problem. You will then need to convert the business problem into an analytics problem. I other words you need to understand exactly what you are going to predict with the model you build. There is no point in building a fabulous model, only to realise later that what it is predicting is not exactly what the business needs.



2. Data Exploration – Once you have the problem defined, the next step is to explore the data and become more familiar with it. This is especially important when dealing with a completely new data set.



3. Data Preparation – Now that you have a good understanding of the data, you will need to prepare it for modelling. You will identify and treat missing values, detect outliers, transform variables, create binary variables if required and so on. This stage is very influenced by the modelling technique you will use at the next stage.&nbsp; For example, regression involves a fair amount of data preparation, but decision trees may need less prep whereas clustering requires a whole different kind of prep as compared to other techniques.



4. Modelling – Once the data is prepared, you can begin modelling. This is usually an iterative process where you run a model, evaluate the results, tweak your approach, run another model, evaluate the results, re-tweak and so on….. You go on doing this until you come up with a model you are satisfied with or what you feel is the best possible result with the given data.



5. Validation – The final model (or maybe the best 2-3 models) should then be put through the validation process. In this process, you test the model using completely new data set i.e. data that was not used to build the model. This process ensures that your model is a good model in general and not just a very good model for the specific data earlier used (Technically, this is called avoiding over fitting)



6. Implementation and tracking – The final model is chosen after the validation. Then you start implementing the model and tracking the results. You need to track results to see the performance of the model over time. In general, the accuracy of a model goes down over time. How much time will really depend on the variables – how dynamic or static they are, and the general environment – how static or dynamic that is.



Question 2. &nbsp; What do you do in data exploration?



Data exploration is done to become familiar with the data. This step is especially important when dealing with new data. There are a number of things you will want to do in this step –



a.&nbsp; &nbsp; &nbsp; &nbsp; What is there in the data – look at the list of all the variables in the data set. Understand the meaning of each variable using the data dictionary. Go back to the business for more information in case of any confusion.



b.&nbsp; &nbsp; &nbsp; &nbsp; How much data is there – look at the volume of the data (how many records), look at the time frame of the data (last 3 months, last 6 months etc.)



c. &nbsp; &nbsp; &nbsp; &nbsp; Quality of the data – how much missing information, quality of data in each variable. Are all fields usable? If a field has data for only 10% of the observations, then maybe that field is not usable etc.



d.&nbsp; &nbsp; &nbsp; &nbsp; You will also identify some important variables and may do a deeper investigation of these. Like looking at averages, min and max values, maybe 10th and 90th percentile as well…



e.&nbsp; &nbsp; &nbsp; &nbsp; You may also identify fields that you need to transform in the data prep stage.



&nbsp;Question 3: What do you do in data preparation?



In data preparation, you will prepare the data for the next stage i.e. the modelling stage. What you do here is influenced by the choice of technique you use in the next stage.



But some things are done in most cases – example identifying missing values and treating them, identifying outlier values (unusual values) and treating them, transforming variables, creating binary variables if required etc,



This is the stage where you will partition the data as well. i.e create training data (to do modelling) and validation (to do validation).



&nbsp;Question 4: How will you treat missing values?



The first step is to identify variables with missing values. Assess the extent of missing values. Is there a pattern in missing values? If yes, try and identify the pattern. It may lead to interesting insights.



If no pattern, then we can either ignore missing values (SAS will not use any observation with missing data) or impute the missing values.



Simple imputation – substitute with mean or median values



OR



Case wise imputation –for example, if we have missing values in the income field.



Question 5: How will you treat outlier values?



You can identify outliers using graphical analysis and univariate analysis. If there are only a few outliers, you can assess them individually. If there are many, you may want to substitute the outlier values with the 1stpercentile or the 99th percentile values.



If there is a lot of data, you may decide to ignore records with outliers.



Not all extreme values are outliers. Not all outliers are extreme values.



Question 6: How do you assess the results of a logistic regression analysis?



You can use different methods to assess how good a logistic model is.



a. Concordance – This tells you about the ability of the model to discriminate between the event happening and not happening.



b. Lift – It helps you assess how much better the model is compared to random selection.



c. Classification matrix – helps you look at the false positives and true negatives.



Some other general questions you will most likely be asked:



<ul>
<li>What have you done to improve your data analytics knowledge in the past year?</li>



<li>What are your career goals?</li>



<li>Why do you want a career in data analytics?</li>
</ul>



The answers to these questions will have to be unique to the person answering it. The key is to show confidence and give well thought out answers that demonstrate you are knowledgeable about the industry and have the conviction to work hard and excel as a data analyst.



About Sarita Digumarti: Sarita has over 10 years of extensive analytics and consulting experience across diverse domains including retail, health-care and financial services. She has worked in both India and the US, helping clients tackle complex business problems by applying analytical techniques. She has a Master’s degree in Quantitative Economics, from Tufts University, Boston, and a PG Diploma in Management from T.A. Pai Management Institute, Manipal. Sarita’s <a href="http://in.linkedin.com/in/saritadigumarti">Linkedin profile</a>

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