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2018 plan for getting expertise in Machine Learning and Deep Learning

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Machine Learning and deep learning are next frontier in the world of innovation. These skill sets are high in demand and demand is going to increase further as we are moving towards a world with connected systems. Although I am experienced professional with SAS having multiple SAS certifications and  using SAS for programming, predictive model building, optimization and data visualization for more than seven years, I will be foolish I do not recognize how important it is to adopt open source platforms for innovation.  2018 plan to acquire expertise in these are are 1. Choose a programming language and I have chose python as my language of preference  2. Do hands on practice to be efficient in Pandas and Numpy. These two libraries are useful and very important for data exploration,wrangling and data cleaning. I find Kernals in Kaggle and below blogs and books to be helpful tutorial for  Pandas  a. https://www.dataquest.io/blog/pandas-tutorial-python-1/  b.https://www.d
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Interdisciplinary Research

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I had heard during my  childhood period that science has no boundaries now I am experiencing it. One of my friend from IITK finished his BE and M Tech in Mechanical and now pursuing PhD from Biology department in one of the best University of USA. Another friend completed his graduation in Electronics and now pursuing PhD in pharmacology. By this blog I would like to share some information on studies and research in Interdisciplinary field being carried out in India especially in IITs. IITs offers many PhD and M Tech programs in  interdisciplinary field. What is interdisciplinary Program. - Interdisciplinary Program can be defined in two ways 1. Any program which is offered jointly by two or more engineering/science departments. It means faculties from different field collaborate and undertake research jointly with common objective. For example in IOER program at IIT Bombay faculties from Electrical, Mechanical,Mathematics and Business School jointly undertake research. MSP, NET
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Predicting Life stage of a customer using the retail data

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It is desirable for a retailer to have information of the life cycle stage of  a customer. This helps greatly to a retailer in offer optimizations. Targeting right customer with rights offers is greatest challenge for a retailer. They run hundreds of promotions and optimizing it saves huge cost and helps in retaining customers.  In the era of super markets, large retailers issue loyalty cards and maintain customer purchase data. But most of the demographic data is not available with them as customers normally do not fill these fields and leave them blank. Although customer demographic data are not available using purchase data certain life stages of customer can be predicted using rule base techniques or predictive modeling. Life stages bring changes in purchasing pattern. 1.  A single male will mostly buy his needs and daily eatables.  Eating habits gets changed if he is married etc.  A man can occasionally buy woman's product but it will be regular if he is married or in
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Game Theory and Team Performance

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I had came across this word Game Theory when I was taking course of Operations Research at IIT Kanpur but at that point of time I only gave attention from academic point of view and after having a basic understanding of it I moved on. Almost half a year later I watched Beautiful Mind and got flavor of importance Game Theory in economics and mathematics. Recently I downloaded two lecture notes from University of Penn and MIT and then started going through it. Suddenly I realized that Game Theory can also be applied in team performance when number players may be from 2 to N and number of Games can also be N where N is any positive integer. Any team is good as the members of the team. Performance of individuals, definitely has impact on overall performance of the team. It is given that all the members in a team may not be at same level of experience, knowledge. Their personality may be different.  But, all the members are tied together with a common team goal and the out come is func
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Data Science: Promising career option for bright future

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Data science   is an interdisciplinary field to extract   knowledge   or insights from   data in structured or unstructured form leading to substantive decision making. This being interdisciplinary area employs knowledge and theory from multiple fields such as statistics, mathematics, computer science and programming. Data science finds applications in diverse fields from healthcare management to politics, cyber security to genetic engineering and marketing to operations etc. Data science is playing pivotal role in academic and applied research in different domains. Leading banks and retailers of the world have been using data driven analytics for marketing promotions and risk assessments since the time when it was called analytics. In last decade data, have been used in multiple fields of business and science leading to emergence of Data Science as stream. It would not be exaggeration to state that data science is a promising career option for bright future. Harvard Busi
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Why I love working at CoreCompete

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I joined   CoreCompete   on  Feb 18, 2013 Monday. It has been a great learning and exciting experience. When I walked into Banjara Hills, Hyderabad office of Core Compete (now shifted to Gachibowli, 3rd office) only thought in my mind was did I take wise decision by choosing Core Compete over a global giant.  Today, I can say that it was the best professional decision I have taken. I feel proud and amazed when I think about our growth from 4 to 200+ in period of 6 years in the niche business of advance analytics and big data technology and recognized as fastest growing consulting organisation in North America ( http://www.consultingmag.com/fastest-growing-firms-2016/   ) for last three consecutive years. Yes, we are growing fast, but that’s not the only reason that I love working at Core Compete.  In last 6 years I worked for multiple clients across multiple domain using different advance analytical techniques and technology platform.I got opportunity to work in different doma
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Enterprise Miner for the performance evaluation of the propensity models using actual campaign results

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Product propensity models are developed to identify   the  customers or observations those  have high likelihood of responding to any cross-sell campaign or any event of interest.  Model d evelopment which includes has multiple steps requires extensive efforts. 1. Identification of business objective or business problem. 2. Identifying analytical objective  3. Identifying data scope and time window.  4. Data preparation and exploration 5. Data treatment and variable reduction 6. Model training and validation 7. Model selection and interpretation  8. Business approval  Steps to automate the model scoring for its periodical usage. 9. Model deployment  10. Periodical Model Scoring  Once the models are scored and used in the campaign for targeting customers to cross sell the product of interest, effectiveness of model must be measured. I am going to discuss the different ways to evaluate the model's robustness. This should not be
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