Month 1: Foundations of Python and Data Science

Week 1: Introduction to Data Science and Python

● Overview of Data Science
● Introduction to Python programming language
● Importance and applications of Python in Data Science

Week 2: Introduction to Python for Data Science

● Python basics: variables, data types, operators, and control flow
● Functions and modules in Python
● Introduction to Python libraries: NumPy, Pandas, Matplotlib

Week 3: Setting Up Environment and Basic Data Manipulation

● Installing Python and necessary libraries (NumPy, Pandas, Matplotlib)
● Setting up Jupyter Notebooks
● Basic data manipulations with Pandas: reading, writing, and exploring
datasets

Week 4: Advanced Data Manipulation with Pandas

● Data cleaning and preprocessing with Pandas
● Indexing and selecting data with Pandas
● Handling missing data and outliers

Month 2: Data Manipulation and Visualization

Week 5: Advanced Data Manipulation with Pandas

● Grouping and aggregating data with Pandas
● Working with time series data
● Combining and merging datasets with Pandas

Week 6: Data Visualization with Matplotlib and Seaborn

● Introduction to Matplotlib for basic plotting techniques
● Customising plots and adding annotations
● Introduction to Seaborn for statistical data visualisation

Week 7: Advanced Data Visualization with Seaborn

● Statistical plotting with Seaborn
● Pair plots, joint plots, and heatmaps
● Visualising categorical data with Seaborn

Week 8: Capstone Project: Data Visualization

● Project: Exploratory data analysis and visualisation on a real-world
dataset
● Presentation and peer review of projects
Month 3: Statistical Analysis and Machine Learning Fundamentals

Week 9: Descriptive Statistics and Probability

● Measures of central tendency and dispersion
● Probability distributions and random variables

Week 10: Hypothesis Testing and Correlation Analysis

● Introduction to hypothesis testing
● Correlation analysis and significance testing

Week 11: Introduction to Machine Learning

● Overview of Machine Learning
● Types of Machine Learning: Supervised, Unsupervised, Reinforcement
Learning

Week 12: Supervised Learning: Regression Techniques

● Introduction to regression analysis
● Linear regression and polynomial regression
● Model evaluation metrics for regression

Month 4: Supervised Learning: Classification Techniques

Week 13: Logistic Regression and Decision Trees

● Introduction to classification algorithms
● Logistic regression for binary classification
● Decision trees and ensemble methods

Week 14: Support Vector Machines (SVM) and Model Evaluation

● Support Vector Machines (SVM) for classification
● Model evaluation techniques: Confusion matrix, ROC curve,
Precision-Recall curve

Week 15: Supervised Learning: Classification Continued

● Introduction to multi-class classification
● Handling imbalanced datasets
● Advanced classification techniques

Week 16: Capstone Project: Supervised Learning

● Project: Building and evaluating predictive models using supervised
learning techniques
● Presentation and peer review of projects

Month 5: Unsupervised Learning and Deep Learning Fundamentals

Week 17: Introduction to Unsupervised Learning

● Overview of unsupervised learning
● Clustering algorithms: K-means, Hierarchical clustering

Week 18: Dimensionality Reduction Techniques

● Principal Component Analysis (PCA)
● t-Distributed Stochastic Neighbour Embedding (t-SNE)
● Autoencoders for dimensionality reduction

Week 19: Introduction to Deep Learning

● Basics of neural networks
● Building and training neural networks with TensorFlow/Keras

Week 20: Convolutional Neural Networks (CNNs)

● Introduction to CNNs for image classification
● Building and training CNNs with TensorFlow/Keras

Month 6: Advanced Deep Learning and Capstone Project

Week 21: Recurrent Neural Networks (RNNs) and Natural Language

Processing (NLP)
● Introduction to RNNs and their applications
● Introduction to NLP with TensorFlow/Keras

Week 22: Advanced Deep Learning Techniques

● Transfer learning and fine-tuning pre-trained models
● Hyperparameter tuning for deep learning models

Week 23: Capstone Project: Deep Learning

● Project: Applying deep learning techniques to solve a real-world
problem
● Presentation and peer review of projects

Week 24: Course Conclusion and Final Assessment

● Review of key concepts covered throughout the course
● Final assessment and feedbac