Introduction of Analytics for Beginners.

1. Business &
Data Analytics

2. What is Data Analytics?
Analytics is the use of:
data,
information technology,
statistical analysis,
quantitative methods, and
mathematical or computer-based models
to help gain improved insight about their business operations
and make better, fact-based decisions.
Business Analytics (BI) is a subset of Data Analytics
1-2

3. What is Business Analytics?
Business Analytics Applications
Management of customer relationships
Financial and marketing activities
Supply chain management
Human resource planning
Pricing decisions
Sport team game strategies
Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 1-3

4. 4 V’s Of Big Data

5. 4 V’s Of Big Data

6. Types of Data Analytics

7. Types of Data Analytics
◎Descriptive – What is happening now based on
incoming data. To mine the analytics, you typically
use a real-time dashboard and/or email reports.

8. Types of Data Analytics
◎Descriptive – What is happening now based on incoming data. To
mine the analytics, you typically use a real-time dashboard and/or
email reports.
◎Diagnostic – A look at past performance to
determine what happened and why. The result of
the analysis is often an analytic dashboard.

9. Types of Data Analytics
◎Descriptive – What is happening now based on incoming data. To
mine the analytics, you typically use a real-time dashboard and/or
email reports.
◎Diagnostic – A look at past performance to
determine what happened and why. The result of
the analysis is often an analytic dashboard.

10. Types of Data Analytics
◎Descriptive – What is happening now based on incoming data. To
mine the analytics, you typically use a real-time dashboard and/or
email reports.
◎Diagnostic – A look at past performance to determine what
happened and why. The result of the analysis is often an analytic
dashboard.
◎Predictive – An analysis of likely scenarios of what
might happen. The deliverables are usually a
predictive forecast.

11. Types of Data Analytics
◎Descriptive – What is happening now based on incoming data. To
mine the analytics, you typically use a real-time dashboard and/or
email reports.
◎Diagnostic – A look at past performance to determine what
happened and why. The result of the analysis is often an analytic
dashboard.
◎Predictive – An analysis of likely scenarios of what might happen.
The deliverables are usually a predictive forecast.
◎Prescriptive – This type of analysis reveals what
actions should be taken. This is the most valuable
kind of analysis and usually results in rules and
recommendations for next steps.

12. Types of Data Analytics

13. Descriptive Analytics
Descriptive analytics are more about summarizing and
reporting data. This type of data analytics is geared
towards What is currently happening or what has already
happened. A sample data set extracted through
descriptive analytics include “top 10 customer service
representatives in terms of processed requests for the
month of July in Asia.”

14. Diagnostic Analytics
Diagnostic analytics is less focused on what has occurred
but rather focused on Why something happened. In
general, these analytics are looking on the processes and
causes, instead of the result. Here is an example
diagnostic analytics “Revenue is up in the East coast and
the likely reason is the increase in investment on targeted
marketing approach, closure of a major competitor in the
area.”

15. Predictive Analytics
Predictive Analytics is to make sense of why certain things
happened and then build a model to project what could
happen in the future.
The predictions could be:
◎“There’s a 60% probability that our biggest supplier in the South India
will partner with our competitor next year”
◎“Revenue in the South India will likely to increase by 6% to 9% in the
next year.”

16. Prescriptive Analytics
Prescriptive analytics allows users to “prescribe” a number
of different possible actions to and guide them towards a
solution. These analytics are all about providing advice.
Prescriptive analytics attempt to quantify the effect of
future decisions in order to advise on possible outcomes
before the decisions are actually made.

17. Prescriptive Analytics

18. Skills for Data Evangelist

19. Skills

20. Data Analytics Tools

21. Analytics, Data Science, Machine
Learning Software Poll, 2016-2018

22. Carrier Options

23. Carrier Options

24. What do I Get?

25. Applications of Data Analytics and Data Science
◎ Internet Search
◎ Digital Advertisements
(Targeted Advertising and
re-targeting)
◎ Recommender Systems
◎ Image Recognition
◎ Delivery logistics
◎ Speech Recognition
◎ Gaming
◎ Price Comparison Websites
◎ Airline Route Planning
◎ Fraud and Risk Detection

26. “
“Information is the oil of the 21st century,
and analytics is the combustion engine”

27. Thank You!
For further Discussion
Vijay Patel
vkpatel2k@gmail.com