Section 1
##### Getting Started with the most popular Data Science Library: Pandas

Section 2
##### Working with Pandas

1

Imputing missing values

2

Pivot tables

3

Crosstabs

4

Merge DataFrames

5

Sorting data frames

6

Plotting with Pandas

7

Analysis broken down into various steps

8

Exploratory Analysis

9

Basic Descriptive Statistic Analysis

10

Distribution Analysis

11

Categorical Variable Analysis

12

Data Munging

13

Treating Missing Values

Section 3
##### Building Predictive Models

1

Linear Regression Theory

2

Understanding Regression

3

Training and Validation

4

Goodness of Fit

5

Practical Application

6

Exploratory Analysis

7

Case study

8

Logistic Regression Theory

9

Linear Probability Model

10

Concept of Classification

11

Comparison with Linear Regression

12

Odds Ratio

13

Classification Table

14

Practical Application

15

Getting the data

16

Reading the data

17

Algorithms

18

Decision Tree

19

Random Forest

Section 4
##### Time Series Analysis

## Questions

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