📘 Data Science Course Content

📅 Module 1: Introduction to Data Science

• What is Data Science?

• Importance and applications

• Data Science programs workflow

• Roles in Data Science: Analyst, Scientist, Engineer, ML Engineer

📅 Module 2: Python for Data Science

• Python for Data Science: variables, loops, functions

• Data structures (lists, dictionaries, sets, tuples)

• NumPy for numerical computing

• Pandas for data manipulation

• Data loading/exporting (CSV, Excel, JSON)

• Working with missing data and outliers

📅 Module 3: Data Visualization

• Visualizations using Matplotlib and Seaborn

• Optional: Plotly

• Creating effective visual stories

• Dashboards and presentation-ready plots

• Tableau for interactive reporting

📅 Module 4: Statistics & Probability

• Descriptive statistics: mean, median, mode, standard deviation

• Data distributions

• Probability theory, Bayes' Theorem

• Hypothesis testing, confidence intervals

• t-tests, z-tests, chi-square tests

📅 Module 5: SQL for Data Science

• Introduction to SQL for Data Science

• Writing SQL queries: SELECT, JOIN, GROUP BY, HAVING

• Subqueries & nested queries

• Window functions

• Real-world case studies with databases

📅 Module 6: Exploratory Data Analysis (EDA)

• Understanding data features

• Univariate & bivariate analysis

• Correlation analysis

• Handling categorical variables

• Feature scaling (standardization/normalization)

📅 Module 7: Machine Learning (ML)

• ML overview: supervised vs unsupervised

• Model development process

• Data splitting (train/test)

• Model evaluation metrics

🔹 Supervised Learning

• Linear Regression, Logistic Regression

• Decision Trees, Random Forest

• K-Nearest Neighbors (KNN), Naive Bayes

🔹 Unsupervised Learning

• K-Means Clustering, Hierarchical Clustering

• Dimensionality Reduction (PCA)

📅 Module 8: Advanced Machine Learning

• Hyperparameter tuning (Grid Search, Random Search)

• Cross-validation

• Ensemble models: Bagging, Boosting

• Introduction to XGBoost, LightGBM

• Model deployment basics using Flask API

📅 Module 9: Deep Learning (Optional/Advanced)

• Basics of Neural Networks

• TensorFlow / Keras introduction

• CNNs for image processing

• RNNs for sequence data

• Transfer learning overview

📅 Module 10: Capstone Project & Portfolio Building

• Real-world dataset project

• From EDA to model deployment

• GitHub portfolio building

• Final presentation

Extras (Optional Topics)

• Web scraping using BeautifulSoup and Selenium

• Time series forecasting

• Natural Language Processing (NLP) and Sentiment Analysis

• Dashboards using Power BI / Tableau