Module 1. Introduction to Python
This module serves as a foundation to familiarize participants with the essentials of the Python
programming language. Learners will explore Python’s syntax and basic programming concepts,
including variables, data types, operators, and control structures. Participants will also engage
in hands-on practical exercises.
By the end of this module, participants will have a solid grasp of Python essentials.

Module2. The Pandas & NumPy Library
The module is designed to introduce participants to two of the most essential libraries in
Python for data manipulation and analysis. Learners will explore NumPy, focusing on its how to
enable efficient numerical computations. It will delve into the Pandas library, highlighting its
capabilities for data handling and analysis with DataFrame and Series.
By the end of this module, participants will have a solid understanding of how to leverage
Pandas and NumPy to analyze datasets effectively.

Module3. Data Visualization with matplotlib
The module is designed to equip participants with the skills necessary to create compelling visual
representations of data using the Matplotlib library in Python. In this module, learners will explore the
fundamental concepts of data visualization. Participants will dive into the core functionalities of
Matplotlib, learning how to create various types of plots, including line graphs, bar charts, histograms,
and scatter plots.

Module 4: Statistics and Averages
The module is designed to provide participants with a comprehensive understanding of key
statistical concepts and their implementation in Python. This module begins by exploring the
concept of averages, specifically mean, median, and mode, and their significance in data
analysis.
By the end of this module, participants will have a solid foundation in descriptive statistics,
equipping them with the skills to analyze and interpret data effectively using Python.

Module 5: Introduction to Machine Learning
The module provides an overview of the fundamental concepts and practical applications of machine
learning. It begins by exploring the various uses and potential abuses of machine learning, highlighting
both its transformative impact and ethical considerations. It will delve into the mechanisms behind how
machines learn, covering essential algorithms and processes that enable data-driven decision-making.
Learners will also gain insights into machine learning in practice, discussing real-world applications and
case studies that illustrate its effectiveness and challenges.

Module6. Forecasting Numeric Data – Regression Methods
The module is designed to introduce participants to the essential techniques for predicting numerical
outcomes using regression analysis. Participants will delve into various types of regression techniques,
such as linear regression, multiple regression, and polynomial regression, while understanding the
underlying assumptions and appropriate use cases for each method. The module emphasizes practical
applications, demonstrating how to implement regression models using Python.

Module7. Logistic Regression
The module focuses on one of the fundamental techniques in statistical modeling and machine learning
used for binary classification problems. Participants will begin by understanding the principles of logistic
regression, including its mathematical foundation and how it differs from linear regression.
By the end of this module, learners will be equipped with the knowledge and skills necessary to apply
logistic regression in real-world scenarios, enhancing their ability to tackle binary classification
challenges in various domains.

Module8. Time series models with Python
The module is designed to provide participants with a thorough understanding of time series analysis
and its applications using Python. Learners will explore the fundamental concepts of time series data,
including components such as trend, seasonality, and noise. They will study various time series models,
including Autoregressive Integrated Moving Average (ARIMA), Seasonal Decomposition of Time Series
(STL), and Exponential Smoothing State Space Models (ETS).
By the end of this module, participants will be equipped with the tools and techniques necessary to
analyze and forecast time-dependent data effectively, enabling them to apply these skills in finance,
economics, sales forecasting, and other relevant fields.

Module9. Cluster Analysis
The module is designed to introduce participants to the fundamental concepts and techniques of
clustering in data analysis and machine learning. Clustering is an unsupervised learning method that
groups similar data points together based on features, allowing for pattern recognition and data
segmentation. In this module, learners will explore various clustering algorithms, including K-Means,
Hierarchical Clustering, and DBSCAN, understanding the strengths and weaknesses of each method.

Module10. Decision Tree & Random Forest
The module provides participants with a comprehensive understanding of two powerful machine
learning algorithms used for classification and regression tasks. This module begins with an introduction
to decision trees, explaining their structure, how they make decisions based on feature splits, and the
advantages and disadvantages of using this method. Participants will learn how to interpret decision
trees and the importance of parameters like depth and splitting criteria.
Building on this foundation, the module will then delve into random forests, an ensemble learning
method that employs multiple decision trees to enhance model accuracy and robustness. Learners will
explore how random forests reduce overfitting, improve predictions, and provide insights into feature
importance.