• Learn how to use Python and libraries like Pandas and NumPy to work with data.
• Discover how to create charts and graphs to understand and present data better.
• Understand how to use statistics and tests to make sense of your data.
• Get familiar with machine learning techniques for making predictions and analyzing patterns.
• Learn to use methods like decision trees and random forests for more complex data analysis.

1. Basic Python Programming Skills: Participants should have a fundamental understanding of Python programming. You can also take <Programming Essentials> using Python before enrolling into this course.
2. Understanding of Basic Mathematics and Statistics: A basic understanding of mathematical concepts and Statistics will be helpful.
3. Basic Knowledge of Data Visualization (optional):

Familiarity with data visualization concepts and tools can enhance learning, although not strictly required.

This training will equip you for the following job roles and career paths:

• Data Scientist
• Machine Learning Engineer
• Data Analyst
• Business Intelligence Analyst
• Data Engineer

Module 1. Introduction to Python Programming Language  

• Python—The Programming Language

• Installing Python

• Anaconda

• Spyder

• Jupyter notebook

• IDLE (Integrated Development Environment)

• Implement the Code Using an IDE

• Interact with Python

• Writing Python Code

• Make Calculations

• Import New Libraries and Functions

• Import additional libraries using pip install

• Import msgpack to satisfy basic requirement

• NumPy

• Pandas

• Matplotlib

Module 2. The Pandas & NumPy Library  

• Pandas Data Structures

• Introduction

• Creating Your Own Data

• Types of Data

• The dtype Option

• The Series

• The list

• The tupple

• Difference between list & tupple

• The DataFrame

• Making Changes to Series and DataFrames

• Exporting and Importing Data

• CSV

• Excel

• Jason

• Aggregate Functions

• Indexing, Slicing, and Iterating

• Indexing

• Slicing

• Iterating an Array

• Conditions and Boolean Arrays

• Shape Manipulation

• The Index Objects

• Other Functionalities on Indexes

• Reindexing

• Dropping

• Arithmetic and Data Alignment

• Operations between Data Structures

• Flexible Arithmetic Methods

• Operations between DataFrame and Series

• Function Application and Mapping

• Functions by Element

• Functions by Row or Column

• Statistics Functions

• Sorting and Ranking

• “Not a Number” Data

• Assigning a NaN Value

• Working With Missing Data

• Filtering Out NaN Values

• Filling in NaN Occurrences

• Hierarchical Indexing and Leveling

• Reordering and Sorting Levels

• Summary Statistic by Level

• AB Testing

 Module 3. Pandas in Depth: Data Manipulation  

• Data Preparation

• Merging

• Merging Multiple Data Sets

• Concatenating

• Combining

• Pivoting

• Removing

• Data Transformation

• Tidy Data

• Removing Duplicates

• Mapping

• Discretization and Binning

• Detecting and Filtering Outliers

• Permutation

• String Manipulation

• More String Methods

• Built-in Methods for Manipulation of Strings

• Regular Expressions

• Data Aggregation

• Group By

• A Practical Example

• Hierarchical Grouping

• Group Iteration

• Chain of Transformations

• Functions on Groups

• Advanced Data Aggregation

Module 4. Data Visualization with matplotlib  

• The matplotlib Library

• Installation

• matplotlib Architecture

• Backend Layer

• Artist Layer

• Scripting Layer (pyplot)

• pyplot

• Line chart

• Scatter plot

• Annotations: Add Text

• Annotations: Properties

• A Simple Interactive Chart

• Set the Properties of the Plot

• Working with Multiple Figures and Axes

• Adding Further Elements to the Chart

• Adding Text

• Adding a Legend

• Legends: Properties

• Saving Your Charts

• Saving the Code

• Saving Your Chart Directly as an Image

• Line Chart

• Line Charts with pandas

• Histogram

Module 5. Statistics and Averages

• Means: The Lure of Averages

• The Average in Python: mean()

• Medians: Caught in the Middle

• The Median in Python: median()

• Statistics à la Mode

• The Mode in Python

• Deviating from the Average

• Measuring Variation

• Back to the Roots: Standard Deviation

• Standard Deviation in Python

• Conditions, Conditions, Conditions …

• Meeting Standards and Standings

• Catching Some Z’s

Module 6. One-Sample Hypothesis Testing  

• Hypotheses, Tests, and Errors

• Hypothesis Tests and Sampling Distributions

• Catching Some Z’s Again

• Z Testing in Python

• t for One

• t Testing in Python

• Working with t-Distributions

• Visualizing t-Distributions

• Testing a Variance

• Working with Chi-Square Distributions

• Visualizing Chi-Square Distributions

Module 7. Two-Sample Hypothesis Testing  

• Hypotheses Built for Two

• Sampling Distributions Revisited

• t for Two

• Like Peas in a Pod: Equal Variances

• t-Testing in Python

• A Matched Set: Hypothesis Testing for Paired Samples

• Paired Sample t-testing in Python

• Testing Two Variances

Module 8. Testing More than Two Samples  

• Testing More Than Two

• ANOVA in Python

• Another Kind of Hypothesis, Another Kind of Test

• Getting Trendy

• Trend Analysis in Python

Module 9. More Complicated Testing  

• Cracking the Combinations, Two-way ANOVA in Python,

• Two kinds of Variables…at once

• After the Analysis

Module 10. Introducing Machine Learning  

• Uses and abuses of machine learning

• Machine learning successes

• How machines learn

• Machine learning in practice

• Machine learning with Python

Module 11. Forecasting Numeric Data – Regression Methods  

• Understanding regression

• Simple linear regression

• Ordinary least squares estimation

• Multiple Linear Regression

• Regression: What a Line!

• Linear Regression in Python

• Juggling Many Relationships at Once: Multiple Regression

• exploring and preparing the data

• ANOVA: Another Look

• Formulae and Linear Models

• Model Building

• training a model on the data

• evaluating model performance

• improving model performance

• Goodness of Fit with Data—The Perils of Overfitting

• Root-Mean-Square Error

• Model Simplicity and Goodness of Fit

• Assumption checking

• Assumption checking using packages

• Case studies of Linear Regression

• Estimation the quality of wines

• Price prediction of real estate

• Movie popularity prediction

• Retail sales prediction

Module 12. When the Response Falls into Two Categories – Logistic Regression  

• Understanding logistic regression

• The logit model

• Generalized Linear Model

• Simple logistic regression

• Multiple logistic regression

• Customer satisfaction analysis with the multiple logistic regression

• Multiple logistic regression with categorical data

• The Dataset and the Data Dictionary

• Data Import in Python

• EDD in Python

• Outlier Treatment in Python

• Missing Value treatment in Python

• Variable transformation and Deletion in Python

• Dummy variable creation in Python

• Automatic dummy variable creation

• Formulae and Logistic Models

• Model Building

• training a model on the data

• evaluating model performance

• improving model performance

• Goodness of Fit with Data—The Perils of Overfitting

• Confusion Matrix

• Creating Confusion Matrix in Python

Module 13. Time series models with Python  

• Introduction to Time Series Data

• Notation for Time Series Data

• Peculiarities of Time Series Data

• Setting the Frequency

• Treatment of missing values

• White Noise

• Stationarity

• Seasonality

• Correlation Between Past and Present Values

• The Autocorrelation Function (ACF)

• The Partial Autocorrelation Function (PACF)

• Picking the Correct Model

• The Autoregressive (AR) Model

• ARMA

• ARIMA

• Automatic ARIMA

Module 14. Cluster Analysis  

• Unsupervised Learning & Clustering: theory

• K-Means Clustering: Theory

• Example K-Means Clustering in Python

• Visualize K-Means Results in Python

• Model-based Unsupervised Clustering in Python

• How to assess a Clustering Tendency of the dataset

• Selecting the number of clusters for unsupervised Clustering methods (K-Means)

• Assessing the performance of unsupervised learning (clustering) algorithms

• How to compare the performance of different unsupervised clustering algorithms?

Module 15. Decision Tree & Random Forest  

• A Simple Tree Model

• Deciding How to Split Trees

• The stopping criteria for controlling tree growth

• Tree Entropy and Information Gain

• Pros and Cons of Decision Trees

• Tree Overfitting

• Pruning Trees

• Decision Trees for Classification

• Conditional Inference Trees

• Conditional Inference Tree Classification

• Building a decision tree in Python

• Model Validation

• Model Improvement

• Model Interpretation

• Ensemble technique

• Random Forest Classification

• Splitting Data into Test and Train Set in Python

• Choose the number of trees

• Model Validation

• Model Improvement

• Model Interpretation

• Accuracy of the model

• Decision Vs Random Forest