Introduction to data analytics

Introduction to Data Analytics
Umasree Raghunath
&
Kumaraswamy T V
IBM
Presentation curated by
Sudheer Vetcha

Agenda
What is Data
What is
Analytics
What is Data
Analytics
Why Data
Analytics
Applications
of Data
Analytics
Activities in
Data Analytics
Roles and jobs
in Data
Analytics
Use cases
Tools
available for
Data Analytics

What is Data ?
 Data is simply facts or figures — bits of information.
 When data is organized, processed, interpreted, structured to make
it meaningful or useful, it is called complete information.
 Data can be images, sounds, videos etc.
 Data is Measured, Reported, and Analyzed
 Can be structured or unstructured
 Can be discrete or continuous

What is Analytics ?
It is the method of understanding or
identifying or interpreting the hidden
pattern in the data for effective
decision making.

It is the process of inspecting,
cleaning, transforming and
modelling data by using statistics
What is Data Analytics ?

Types of Analytics
Descriptive Analytics
Prescriptive Analytics
Descriptive Analytics

Descriptive Analytics
We use Descriptive statistics
Identifying what has happened
Example: What is the average test score of the class

Prescriptive Analytics
We use inferential statistics
Interpreting what should we do
Example: Whether to reject or accept a batch of sops
manufactured by a machine

Predictive Analytics
 We use predictive statistics
 Predicting what could happen
 Example: Predicting the sales of Walmart

 Effective decision making
 Enhanced customer
service
 Improves Business/sales
 Identify anomalies
Why Data Analytics ?

Applications of Data Analytics
Self Driving Cars Google Maps

Recommendation Engine Games

Decision Making

Activities in Data Analytics
 Data Extraction – Fetch data from database
 Data Analysis – Observe the data
 Data Manipulation – Manipulate the data
 Data Modelling – Fit a model to the data
 Data Visualization – Visualize the data

Career in Data Analytics
 Data Engineer
 Data Analyst
 Data Modeler
 Data Scientist
 Full stack developer

Use Case - YouTube
 YouTube uses recommendation
systems to bring videos to a user.
 Increases the time user spends in
the site
 Makes money by advertising
 Entertains the user
 Ultimately became popular

Use Case - BMW
Sensors are installed in the car to
monitor the condition of the
vehicle
BMW gets the data continuously,
analyses it to understand the
condition of the car
Customer is contacted when the
car needs service

Use Case - Other
Identifying fraudulent banking transactions
Determining a defaulter before issuing a credit card
Advertising a brand on T-Shirts – FIFA
Customer churn - Telecommunication

Tools available in Data Analytics – Data Extraction
 Data Stage
 Informatica
 R
 Python
 SAS

Tools available in Data Analytics – Data Analysis & Modelling
 Python
 R
 SAS
 SPSS
 IBM WATSON
 MATLAB
 Statistica
 WEKA
 MS Excel

Tools available in Data Analytics – Data Visualization
 COGNOS
 Qlikview
 Tableau
 Excel
 R
 Python
 Dueen
 Domo
 BIRT
 JaperSoft
 SpagoBI
 Pentaho

Introduction to data analytics

Introduction to data analytics

In this document