Data Science

This post is the part of blog post series 'Data Literacy for Professionals', kindly refer the pilot post to get the table of content & links to related posts topic-wise.

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What is Data Science?

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

Data is everywhere, and is found in huge and exponentially increasing quantities. Data science as a whole reflects the ways in which data is discovered, conditioned, extracted, compiled, processed, analyzed, interpreted, modeled, visualized, reported on, and presented regardless of the size of the data being processed.

Why Data Science is important?

Have you ever looked for shoes online and found related advertisements on facebook and other websites continuously for a week? Or say, you’re chatting with your best friend and the keyboard suggests you the exact words that you want to use in your sentence? How does YouTube show all your favourite videos on your homescreen?

Well, these are all the applications of Data Science. Over the last few years Data Science has really changed our concept of technology. Data Science has really pulled the ends between fiction and technology, right from LinkedIn to Tinder, data science is being used everywhere.

Data Science is successfully adding value to all the business models by using statistics and deep learning to make better decisions and improve hiring. It is also being used to crunch the previous data and predict possible situations and risk so that we can work on avoiding them. Moreover analysis of this data can really help set a workflow.

How to do Data Science?

So, how to start doing data science in your department, I would suggest you to start small with use cases.

• Short Term:

-Use Case Identification & Assessment: Involve business analysts & business stakeholders to identify & assess the use cases for data science.

-Data Acquisition & Understanding: Acquire & explore data and understand the attributes in relation to the objective set for the use case.

-Modelling & Evaluation: Apply machine learning models and evaluate the performance of the models.

-Product/Solution Integration: Integrate your models to existing relevant products or solutions.

-User Acceptance & Deployment: Involve business users to evaluate the business value of models & deploy the models in production.

• Long Term:

-Data Science Framework: Build DS framework to streamline the people, process & technology around data science.

Case Study: Rathi Pizza Inc

Lets get back to our case study, how we can apply data science to our pizza business. First, identify the use cases where data science can be applied. Suppose we want to increase our sales, we need to assess our use case if its worth of applying data science. We can start with collecting & analyzing historical sales figures, what are the data attributes affects sales, how important is a data attribute in terms of affecting sales, is it causation or a mere correlation. Once we identified the useful patterns, its worthwhile to build models to predict sales figures for upcoming month or quarter. We can also analyse what changes we can make to our business which can result into increase in sales. We evaluate built models to find a sweet spot between specialization and generalization to avoid over-fitting or under-fitting. The next step is to integrate our model to existing solution or product if the model is not a solution or product in itself. We involve business users to get acceptance and deploy the model in production.

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