Data Science – Part 1

Chirag Sanghavi

Chirag Sanghavi

Data Science enthusiast and learner with 9+ years of experience in software industry with technologies like SQL Server, ETL, SSIS, SSRS.

Pursuing Post graduate program in Data Science, Big Data and Business Analytics.

Willing to contribute, write to admin@sharecareinspire.com
Chirag Sanghavi

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>> Data Science – Part 2

Introduction

It gives me immense pleasure to write this article on Data Science, a topic, which in today’s date has been considered as one of the most important science which is not only limited to growth of an organization or business but also can help in better governance of societies and to develop a great data driven product.  To support my words, following is the statement I have personally recorded during panel discussion on “Using Data for Better Governance and Society”, while attending Data Science Congress 2017, a giga event where all the great minds and data scientists from around the world gathered to give accurate insights as to what the data science is.

The term data science generally gets associated with academia, research, education and its implementations at high end technology companies like Google, Amazon, Netflix, LinkedIn etc. In reality, that is only one of the many facets of data science, and its applications can touch every aspect of human life. One such area is for better governance and societal reforms.

What is Data Science?

According to Wikipedia, the definition of data science is “Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining.

It is a concept to unify statistics, data analysis and their related methods in order to understand and analyze actual business value of data. It uses techniques and theories from broad areas of mathematics,  statistics,  information science,  and computer science, machine learning,  classification,  cluster analysis,  data mining,  databases, and visualization.

Following is the Venn diagram for data science created by Drew Conway who started his career in counter-terrorism as a computational social scientist in the U.S. intelligence community.

Data Science
Data Science

Store of valuable raw information is at the core. Streaming the data data warehouses, which acts like a treasure,  from which much is to be learned by mining that data and build advanced capabilities with it.

Data science is about using data in creative ways to generate business value.

Data Science
Data Science

Aspects of Data science

Data insight

Uncovering findings from data is Data Insight. It is about getting into granular level of data to understand behaviors, trends, and inferences. It helps to surface insight that help enable companies to make business decisions.

The outcome is to provide advice to an executive to make a smarter business decision.

To optimally plan for production levels and understand future demand , Proctor & Gamble utilizes time series models.

To understand what drives user interest, Netflix mines movie viewing patterns, and uses that to make decisions for series to produce.

Data product

A data product is a technical implementation that utilizes data as input, and processes that data to return results which are generated by algorithm. Example of a data product is a recommendation engine, which ingests user data, and makes personalized recommendations based on that data.

It encapsulates an algorithm, and is designed to integrate directly into core applications.

Here are some examples of data products:

Shopping portals like Amazon uses its recommendation algorithms engine to suggest items for you to buy.

Email service providers like Gmail uses its own algorithm behind the scenes and processes incoming mail and determines if a message is junk or not.

Role of Data scientists

Data scientists play a central role in developing data product. This involves building out algorithms, as well as testing, refinement, and technical deployment into production systems. In this sense, data scientists serve as technical developers, building assets that can be leveraged at wide scale.

I will be describing the roles and responsibilities of a data scientist in another article which will be published soon.

Conclusion

Thus to conclude, in Data Science one can have multiplicative returns on investment, both from guidance through data insight, and development of data product.

I hope my article gives clear picture of what data science actually is and how it can be used for discovery of data insight and development of data product. I hope whoever reads this article finds it useful and shares the knowledge appropriately.


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