COURSE OUTLINE

 Day 1: Python for Data Science Foundations

Focus: Building core Python skills for data handling

• Introduction to Python for data science 
• Python syntax, variables, and data structures
• Functions, loops, and conditional statements 
• Introduction to NumPy (arrays, operations, broadcasting) 
• Introduction to Pandas (Series, DataFrames, indexing) 
• Data loading and handling (CSV, Excel, JSON files) 
• Hands-on: Basic data manipulation using Pandas

Day 2: Data Cleaning and Visualization

Focus: Preparing and visualizing datasets

• Understanding data quality issues (missing values, duplicates, outliers) 
• Data cleaning techniques using Pandas 
• Data transformation and preprocessing 
• Exploratory Data Analysis (EDA) techniques 
• Data visualization using Matplotlib and Seaborn 
• Identifying patterns and trends in data 
• Hands-on: Cleaning and visualizing a real-world dataset

Day 3: Statistics and Data Analytics

Focus: Understanding data through statistical methods

• Introduction to descriptive and inferential statistics 
• Measures of central tendency (mean, median, mode) 
• Measures of dispersion (variance, standard deviation) 
• Probability fundamentals for data science 
• Correlation and covariance analysis 
• Hypothesis testing basics 
• Hands-on: Statistical analysis using Python libraries



Day 4: Machine Learning with Python

Focus: Building predictive models

• Introduction to Machine Learning concepts
• Supervised vs Unsupervised learning 
• Regression models (Linear Regression, Multiple Regression) 
• Classification models (Logistic Regression, Decision Trees, KNN) 
• Clustering techniques (K-Means clustering) 
• Model training and evaluation metrics 
• Hands-on: Building basic ML models using Scikit-learn

Day 5: Advanced Data Science Techniques

Focus: Improving and optimizing ML models

• Feature engineering and feature selection techniques 
• Handling categorical and numerical features 
• Model evaluation and performance tuning 
• Overfitting and underfitting concepts 
• Cross-validation techniques 
• Introduction to model deployment concepts 
• Hands-on: Improving model accuracy through optimization

Day 6: Final Project and Assessment

Focus: End-to-end implementation and presentation

• End-to-end data science workflow (problem to solution) 
• Dataset selection and problem framing 
• Data preprocessing and model building 
• Model evaluation and interpretation 
• Project deployment overview (basic level) 
• Final project presentation 
• Assessment and feedback session