Introduction to Data Science & Need of Data Science

Introduction

Syllabus:

Need for data science – benefits and uses – facets of data – data science process – setting the research goal – retrieving data – cleansing, integrating, and transforming data – exploratory data analysis – build the models  – presenting and building applications.

Material Reference:

David Cielen, Arno D. B. Meysman, and Mohamed Ali, “Introducing Data Science”, Manning Publications, 2016. (first two chapters for Unit I)

Page Number: 1 to 56

Data Science:

Data science is an interdisciplinary field which is  focused on extracting knowledge from Big Data, which are typically large, and applying the knowledge and actionable insights from data to solve problems in a wide range of application domains.

Need for Data Science

• Big data is a huge collection of data with wide variety of different data set and in different formats. It is hard for the conventional management techniques to extract the data of different format and process them.
• Data science involves using methods to analyse massive amounts of data and extract the knowledge it contains.

The example is like a relationship between crude oil and oil refinery.

Characteristics of Big data

• Volume - How much data is there?
• Variety - How diverse are different types of data?
• Velocity - At what speed is new data generated?
• Veracity - How accurate is the data?

Benefits & uses of Data Science & Big Data

• Data science and big data are used almost everywhere in both commercial and non-commercial settings.

Example

• Google AdSense, which collects data from internet users so relevant commercial messages can be matched to the person browsing the internet.
• Human resource professionals use people analytics and text mining to screen candidates, monitor the mood of employees, and study informal networks among coworkers.
• Financial institutions use data science to predict stock markets, determine the risk of lending money, and earn how to attract new clients for their services.
• Many governmental organisations not only rely on internal data scientists to discover valuable information, but also share their data with the public.
• Nongovernmental organisations ( NGO s) are also no strangers to using data. They use it to raise money and defend their causes.