This document discusses data analysis and provides details on the following: - It defines data analysis and provides examples of its use. - It describes the four main types of data analysis: descriptive, diagnostic, predictive, and prescriptive. - It outlines the six step data analysis process: data requirement gathering, data collection, data cleaning, analyzing data, data interpretation, and data visualization. - It provides examples to illustrate each type and step of the analysis process. - It also lists some commonly used data analysis tools.

1. DATA ANALYSIS
Prepared and Presented By:
 AMMAR ABBAS SHAH (2020-ME-73)
 HARIS RIAZ (2020-ME-61)
 AHSAN HASEEB (2020-ME-75)
 AWAIS AHMED (2020-ME-58)

2. DATA ANALYSIS
• What is Data Analysis?
• What are the types of Data Analysis?
• How Data Analysis process is carried out?
• Data Analysis Tools

3. What is Data Analysis?
• Data Analysis is a process of inspecting, cleansing,
transforming and modeling data with the goal of
discovering useful information, informing conclusions
and supporting decision making.
 Example:
• Retailers use data analysis to understand their customer
needs and buying habits to predict trends and boost
their business.
• Healthcare industries analyze patient data to provide
lifesaving diagnoses and treatment options. They also
deal with healthcare plans, insurance information to
derive key insights.

4. Types of Data
Analysis

5. Types of Data Analysis
There are 4 main types of Data Analysis.
Data Analysis
Diagnostic
Data Analysis
Predictive
Data Analysis
Prescriptive
Data Analysis
Descriptive
Data Analysis

6. Descriptive Data Analysis
Descriptive data analysis looks at past data and tells what happened. This is often used when tracking Key
Performance Indicators(KPIs), revenue, sales leads and more.
Example:
1) CGPA of a student.
2) Batting Average of a Batsman.
3) Previous sales record of a specific product.

7. Diagnostic Data Analysis
Diagnostic Analysis shows ”WHY did it happen?” by finding
the cause from the insight found in Descriptive Analysis.
This analysis is useful to identify behavior patterns of data.
If a new problem arrives in your business process, you can
look into this Analysis to find similar patterns of that
problem. And it may have chances to use similar
prescriptions for the new problems.
 Examples:
 A freight company investigating the cause of slow
shipments in a certain region.
 A company drilling down to determine which
marketing activities increased trials.

8. Predictive Data Analysis
Predictive Analysis shows “What is likely to happen?” by
using previous data. This analysis makes predictions
about future outcomes based on current or past data.
Forecasting is just an estimate. Its accuracy is based on
how much detailed information you have and how much
you dig in it.
Example:
 Risk Assessment
 Sales Forecasting
 Customer Success Teams

9. Prescriptive Data Analysis
• Prescriptive Data Analysis
combines the insight from all
the previous analysis to
determine which action to take
in a current problem or decision.
Example:
• Artificial Intelligence (AI) is a
perfect example of prescriptive
data analysis.

10. Data Analysis
Process
 Following are the steps which are
carried out to complete data analysis:
1. Data Requirement Gathering
2. Data Collection
3. Data Cleaning
4. Analyzing Data
5. Data Interpretation
6. Data Visualization

11. Data Analysis Process
Following are the steps which are carried out to complete data
analysis:
1. Data Requirement Gathering
2. Data Collection
3. Data Cleaning
4. Analyzing Data
5. Data Interpretation
6. Data Visualization

12. Data Requirement Gathering
• In this process, you decide these things:
Aim of analysis
Type of data analysis you want
What to analyze?
How to measure?

13. Data Collection
• In this process, you collect your data based on your requirements.
As you collected data from various sources, you must have to
keep a log with a collection date and source of the data. Data may
be collected from sensors in the environment, including traffic
cameras, satellites, recording devices, etc. It may also be obtained
through interviews, downloads from online sources, or reading
documentation.

14. Data Cleaning
• In this process, you clean your data by removing not be useful or
irrelevant to your aim of analysis. The data which is collected may
contain duplicate records, white spaces or errors. So it must be
cleaned and made error-free before analyzing data.

15. Analyzing Data
• In this process, you manipulate your data. During this phase, you
can use data analysis tools and software which will help you to
understand, interpret, and derive conclusions based on the
requirements.

16. Data Interpretation
• In this section, you interpret your results. You can choose the way
to express or communicate your data analysis either you can use
simply in words or maybe a table or chart. Then use the results of
your data analysis process to decide your best course of action.

17. Data Visualization
• In this process, you use a graphical method to communicate your
conclusions, results and findings to make it easier for human
brain to understand. It can be in the form of charts and graphs
etc. By comparing datasets, you can find a way to find out
meaningful information.

18. Data Analysis
Tools

19. Data Analysis Tools
Following are one of the best, widely
use data analysis tools:
• Microsoft Power BI
• SKYTREE
• ROOT
• Julia
• PAW
• ELKI