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Introduction and Motivation

Hello All -

When I look around, I am fascinated by the way the world has transformed over the past decade with the digital revolution and data democratization. Gadgets have become ubiquitous and business decisions are driven by the data obtained from numerous data points available across platforms, impacting a plethora of fields like education, healthcare, retail, banking, sports and so on. Data Science and analytics being the core of this transformation, I am one among those who are fascinated about the field and aspiring to be at the forefront of this revolution.

I am an engineer at heart which makes me the guy with a WHY wherever I go. Over the years I have also realized that learning and sharing knowledge is really important in a technical field like data science. I strongly believe that knowledge sharing is one of the most important asset for any individual and in that regard, this series of blog posts are a set of insights that I have gained with respect to Data Science and Analytics through my education, work experience and by working on hobby projects.

The posts would cover a variety of topics starting from what is data analytics to how are the various statistical concepts used to solve real world business problems. The posts would also present conceptual materials on machine learning and Deep Learning that I have learned over the years by taking up courses and working on projects.

Data Science, as described by many industry leaders is an art of problem solving and decision making. The core to Data Science are three major domains : Math, Business and Technology.

Math : This involves the various Statistical and Probabilistic concepts used in the field of data science for generating insights including distributions and hypothesis testing.
Business : The business problem we are trying to solve and the domain integrity.
Technology: The tools that are used to perform these statistical analysis on data and to generate  actionable insights.

Most of the topics covered will have all these three ingredients in it. I will be explaining a statistical concept by taking up a business problem, framing a hypothesis and then implementing the analysis using tools like R, Python, SAS etc. to generate insights. I would also make use of the state of the art visualization tools like Tableau to make executive dashboards and presentations.

I am personally excited about the topics to be covered in this series of posts. Looking forward to sharing and learning a lot in the process. Happy Learning! 

Cheers!
Renga



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