Data Science

🔹 Module 1: Introduction to Data Science

• What is Data Science?

• Data Science lifecycle: From data collection to deployment

• Key roles: Analyst, Scientist, Engineer

• Tools & technologies used in the industry

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🔹 Module 2: Python for Data Science

• Python setup (Jupyter, Anaconda, Colab)

• Variables, data types, functions, loops, conditionals

• Working with lists, dictionaries, and libraries (NumPy, Pandas)

• Mini-project: Analyze simple CSV datasets

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🔹 Module 3: Data Wrangling with Pandas

• Importing data (CSV, Excel, SQL, Web APIs)

• Handling missing data, duplicates, and outliers

• Grouping, aggregating, and merging datasets

• Real-world data cleaning project (e.g., e-commerce sales)

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🔹 Module 4: Data Visualization

• Data storytelling principles

• Plotting with Matplotlib and Seaborn

• Creating charts: bar, line, scatter, box, heatmap

• Project: Visualize trends in COVID, finance, or survey data

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🔹 Module 5: Exploratory Data Analysis (EDA)

• EDA process & frameworks

• Univariate and bivariate analysis

• Feature engineering and selection

• Case study: Customer churn or HR analytics

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🔹 Module 6: Statistics for Data Science

• Descriptive statistics

• Probability basics and distributions

• Hypothesis testing and confidence intervals

• Use case: A/B testing in marketing

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🔹 Module 7: Machine Learning Fundamentals

• Supervised vs unsupervised learning

• Train/test split, cross-validation

• Algorithms: Linear Regression, Logistic Regression, Decision Trees

• Metrics: Accuracy, MSE, confusion matrix, ROC

• Project: Predict housing prices or loan default

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🔹 Module 8: Advanced Machine Learning

• Random Forests, SVM, KNN

• Introduction to XGBoost

• Model tuning with GridSearchCV

• Ensemble methods

• Project: Credit scoring or product recommendation

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🔹 Module 9: Unsupervised Learning & Clustering

• KMeans clustering

• Hierarchical clustering

• Dimensionality reduction with PCA

• Use case: Customer segmentation

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🔹 Module 10: Working with Real-World Data

• Connecting to APIs (Twitter, OpenWeather)

• Introduction to web scraping with BeautifulSoup

• Handling big datasets (optional Spark module)

• Ethical considerations & data privacy

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🔹 Module 11: Capstone Project

Choose one:

• Build a sales forecasting model

• Create a dashboard and ML model for HR/finance

• NLP project: Sentiment analysis from customer reviews
  Deliverables: Clean code, visuals, model, GitHub portfolio

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🔹 Module 12: Career & Portfolio Building

• Creating a data science resume and GitHub profile

• Interview questions and answers

• How to write project case studies

• Freelancing and job board strategies