Introduction on Data Science

1. Impact of Data Science
D.Shunmuga Kumari, M.Sc.,M.Phil.,
Assistant Professor,
Department of Information Technology,
V.V.Vanniaperumal College for Women,
Virudhunagar.

2. Contents
 Need for Data Science
 What is Data Science?
 Data Science Vs Business Intelligence
 Prerequisites for learning Data
Science
 What does Data Scientist do?
 Data Science Life cycle
 Demand for Data Scientist.

3. Need for Data Science
Data Science
•Autonomous Car
Minimize the Accidents
•Data Science Decision
Speed up/Turn/Apply
Break
Airlines
•Route Planning
•Predictive Analysis
•Promotional Offers
•Different Classes of
Planes

4. Need for Data Science
Better Decision making
whether Amazon or Flip cart?
Predictive Analysis
What will happen next?
Pattern Discovery
Sales will Inc/Dec
Finding the hidden information in
the data

5. What is Data Science?
Decision Tree Is Shopping Online
Ratings 4 or 5 Close Web site
Discount >20Close Web
site
Close Web
site Purchase Product

6. Asking Questions on Data
Science
Which
Route may
capable
faster?
Which
viewers
like TV
shows?
Will this
Refrigerator
fall in next
3 years?
Who will
win the
Election?
Cab Booking
NetFlix Sell to Advertise
Yes/No for Planning
Capturing Votes/Voters

7. What is Data Science?
Asking the Right
questions and
Exploring the Data
Modeling the data
using various
algorithms
Communicating and
visualizing the
Results

8. Business Intelligence Vs Data
Science
Criterion Business Intelligence Data Science
Data Source Structured Data e.g.,SQL Un Structured Data
e.g.,Web Logs
Method Analytical Scientific
Skill Statistics, Visualizations Statistics,Visualizatio
ns,Machine learning
Focus Past and Present Data Present data and
Future Predictions

9. Prerequisites for Data Science
Basics
Curiosity
Common Sense
Communication Skills
Asking question, will have better
understanding of the problem
 Identify new ways to solve a
problem and to detect priority
problems
A data Scientist needs to communicate
their finding to business teams to act
upon the insights

10. Prerequisites for Data Science
Machine Learning
Backbone – DS uses to find solutions to a
problem
Mathematical Modeling
Fast calculations and Predictions
Statistics
Extract knowledge and obtain results from
data
Programming
Preferably Python or R for Data Modeling
Databases
Discipline of querying databases

11. What does a Data Scientist
do?
Real World-
Raw data-
Process and Analysis-
Meaningful Data-
Useful
Insights

12. Machine Learning Algorithms
Regression analysis is a predictive modeling technique which
investigate relationship between dependent and independent
variable
Clustering is a technique is to divide the groups
in to collection of objects
A decision tree is a largely used in
machine learning technique for
Regression and Classification
problems
Classification Methods

13. Life Cycle
 Concept Study
 Understanding the problem statement ,through
study of business Model is required
 Use case-specification-budget-goal (What is?)
 Data Preparation
 Data Integration
 Resolving data conflicts/data redundancy
 Data Transformation
 Involves Normalizations & aggregations.
 Data Reduction
 using strategy to reducing size of data
 Data Cleaning
 Correcting missing data-null data-improper data

15. Life Cycle
 Model Planning
 Deeper analysis of Dataset to better
understand the data
 Involves Exploratory data Analysis(EDA)
 Goals:
 Data types
 Data distributions
 Identify the patterns

16. Model Planning
Techniques

17. Model Planning

18. Tools used in Model Planning
R-
Studio
Python
MatLab
SAS

19. Model Building
 Definition:
 Analyzing the data and
observe that the output is progressing
Linearly.
 Using Linear Regression Algorithm
 Linear Regression Model – predict
sales

20. Model Building
 Linear regression describes the relationship
between 2 variable i.e X and Y
 X-independent Y-dependent var
 Regression Line Y=mX+c
 M Slope
 C= Y intercept

21. Communication
 A good Data scientist should be able
to communicate his finding with team,
in their execution phase

22. Demand for Data Scientist

23. Data Science