Statistics is the science of data. It involves collecting, presenting, and characterizing data. It plays an important role in fields like industry, commerce, economics, and more by analyzing data. There are two main types of statistics: descriptive statistics which describes data through measures like average, and inferential statistics which makes decisions about population characteristics through estimation and hypothesis testing. Statistics helps answer questions about whether certain factors increase outcomes and helps predict things like election results. It is an essential tool in data science.

1. Role of statistics in data science
Group 5
Sec: B
ISTTM Business School

2. ISTTM Business School
What Is Statistics?
Statistics is the science of data.
Why?1. Collecting Data
e.g., Survey
2. Presenting Data
e.g., Charts & Tables
3. Characterizing Data
e.g., Average
Data
Analysis
Decision-
Making
© 1984-1994 T/Maker Co.
© 1984-1994 T/Maker Co.

3. Importance of statistics
• Statistics has important role in determining the existing position of
per capita income, unemployment, population growth rate, housing,
schooling medical facilities etc…in a country.
• Now statistics holds a central position in almost every field like
Industry, Commerce, Trade, Physics, Chemistry, Economics,
Mathematics, Biology, Botany, Psychology, Astronomy, Information
Technology etc…, so application of statistics is very wide.
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4. ISTTM Business School
Statistical Methods
Statistical
Methods
Descriptive
Statistics
Inferential
Statistics

5. ISTTM Business School
Descriptive Statistics
1. Involves
• Collecting Data
• Presenting Data
• Characterizing Data
2. Purpose
• Describe Data
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25
50
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6. ISTTM Business School
1. Involves
• Estimation
• Hypothesis
Testing
2. Purpose
• Make decisions about
population characteristics
Inferential Statistics
Population?

7. ISTTM Business School
Four Elements of Descriptive
Statistical Problems
1. The population or sample of interest
2. One or more variables (characteristics of the
population or sample units) that are to be
investigated
3. Tables, graphs, or numerical summary tools
4. Identification of patterns in the data

8. ISTTM Business School
Five Elements of Inferential
Statistical Problems
1. The population of interest
2. One or more variables (characteristics of the
population units) that are to be investigated
3. The sample of population units
4. The inference about the population based on
information contained in the sample
5. A measure of reliability for the inference

9. ISTTM Business School
Types of Data
Types of
Data
Quantitative
Data
Qualitative
Data
1. Quantitative (numerical in nature)
2. Qualitative (categorical in nature)

10. ISTTM Business School
Quantitative Data
Measured on a numeric
scale.
• Number of defective
items in a lot.
• Salaries of CEOs of
oil companies.
• Ages of employees at
a company.
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52
71
4
8
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11. ISTTM Business School
Qualitative Data
Classified into categories.
• College major of each
student in a class.
• Gender of each employee
at a company.
• Method of payment
(cash, check, credit card).
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12. Statistics also played a key role
helping to answer questions like:
• Does fertilizer increase crop yields?
• Does Streptomycin cure Tuberculosis?
• Does smoking cause lung-cancer?:
• Who will win the election?
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13. Supervised vs Unsupervised learning
• Supervised: Both inputs (features) and outputs (labels) in training set
• Unsupervised: No output values available, just inputs.
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14. Chaining Tools for Data Science
Data
Preparation
Exploratory
Analysis
Inference /
Prediction
Solution
Implementation
Results
Communication
ExcelHadoop
RDBMS
/ SQL
PythonExcel
R
Python
Custom
Code
R
• Use the right toolset in different stages