Data Science

Module 1: Introduction to Data Science

• What is Data Science?

• Applications and use cases

• Data Scientist roles and skills

• Tools of the trade (Python, Jupyter, Git, etc.)

Module 2: Python for Data Science

• Python basics (variables, loops, functions)

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

• Libraries:

  • NumPy for numerical operations

  • Pandas for data manipulation

  • Matplotlib & Seaborn for visualization

Module 3: Data Wrangling & Cleaning

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

• Handling missing values

• Data type conversions

• String operations & date-time formatting

• Outliers and duplicates

Module 4: Data Visualization

• Matplotlib, Seaborn, Plotly basics

• Histograms, scatter plots, box plots, pairplots

• Customizing plots (labels, legends, themes)

• Dashboards (intro to Streamlit or Tableau)

Module 5: Exploratory Data Analysis (EDA)

• Descriptive statistics

• Grouping & aggregation

• Correlation and covariance

• Feature engineering basics

Module 6: Probability & Statistics

• Probability theory basics

• Random variables and distributions (normal, binomial, Poisson)

• Hypothesis testing (t-test, chi-square test)

• Confidence intervals

• Central limit theorem

Module 7: Machine Learning (ML)

• Supervised Learning

  • Linear regression

  • Logistic regression

  • Decision Trees & Random Forests

  • k-Nearest Neighbors

  • Support Vector Machines (SVM)

• Unsupervised Learning

  • K-means clustering

  • Hierarchical clustering

  • PCA (Principal Component Analysis)

Module 8: Model Evaluation & Tuning

• Train/test split

• Cross-validation

• Accuracy, precision, recall, F1-score

• ROC curves and AUC

• Hyperparameter tuning (GridSearch, RandomSearch)

Module 9: Advanced Topics (Optional)

• Natural Language Processing (NLP)

• Time Series Forecasting

• Deep Learning intro with TensorFlow or PyTorch

• Recommendation Systems

Module 10: Working with Real-World Data

• Web scraping with BeautifulSoup or Scrapy

• Working with APIs (e.g., Twitter, OpenWeather)

• SQL for Data Science

• Big data tools overview (Hadoop, Spark)

Module 11: Capstone Project

• Choose a real dataset (Kaggle, UCI, etc.)

• Complete EDA, modeling, evaluation

• Document in Jupyter or create a dashboard

• Present your findings