Week 1: Introduction to Python and Data Science
Overview of Python programming language
Installing Python and data science libraries (NumPy, Pandas, Matplotlib, etc.)
Basic data types, control structures, and functions in Python
Introduction to Jupyter notebooks and Google Colab
Week 2: Data Cleaning and Preparation
Loading data into Python (CSV, Excel, SQL)
Data cleaning and preprocessing techniques
Handling missing values, duplicates, and outliers
Data normalization and standardization
Week 3: Exploratory Data Analysis (EDA)
Descriptive statistics and data visualization
Univariate and bivariate analysis
Correlation and regression analysis
Hypothesis testing and statistical inference
Week 4: Data Wrangling and Feature Engineering
Data manipulation and transformation techniques
Feature selection and extraction
One-hot encoding and label encoding
Handling date and time data
Week 5: Supervised Learning Algorithms
Regression algorithms (Linear Regression, Ridge Regression, Lasso Regression)
Classification algorithms (Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests)
Model evaluation metrics (R-squared, Mean Squared Error, Confusion Matrix, ROC curve)
Week 6: Unsupervised Learning Algorithms
Clustering algorithms (K-Means, Hierarchical Clustering)
Dimensionality reduction algorithms (PCA, t-SNE)
Model evaluation metrics (Silhouette Score, Elbow Method)
Week 7: Advanced Topics in Data Science
Natural Language Processing (NLP) with Python
Deep Learning with Python (Artificial Neural Networks, Convolutional Neural Networks)
Big Data Analytics with Apache Spark
Week 8-12: Capstone Project
Working on a real-world data science project, from data cleaning to model building and evaluation
Collaborating with a team and presenting findings
Implementing best practices for project management, version control, and documentation.