- Introduction to Data Science
- Data Collection
- Data Preprocessing
- Exploratory Data Analysis (EDA)
- Feature Engineering
- Machine Learning
- Model Evaluation
- Visualization
- Resources
- What is Data Science?
- Data Science Process
- Importance of Domain Knowledge
- Types of Data (Structured, Unstructured, Semi-Structured)
- Data Sources (Databases, APIs, Web Scraping)
- Data Quality and Cleaning
- Handling Missing Values
- Data Transformation (Scaling, Normalization)
- Encoding Categorical Variables
- Outlier Detection and Treatment
- Summary Statistics (Mean, Median, Variance)
- Data Visualization (Histograms, Box Plots, Scatter Plots)
- Correlation Analysis
- Distribution Analysis
- Importance of Feature Engineering
- Feature Extraction (Dimensionality Reduction, PCA)
- Feature Selection (Correlation, Importance)
- Creating Interaction Features
- Supervised vs. Unsupervised Learning
- Types of Algorithms (Regression, Classification, Clustering)
- Model Training and Testing
- Cross-Validation
- Evaluation Metrics (Accuracy, Precision, Recall, F1-Score, RMSE)
- Confusion Matrix
- Overfitting and Underfitting
- Bias-Variance Tradeoff
- Matplotlib Basics
- Seaborn for Statistical Visualization
- Interactive Visualization (Plotly, Bokeh)
- Data Dashboards (Tableau, Power BI)
- Useful Libraries (numpy, pandas, scikit-learn)
- Online Courses and Tutorials
- Blogs and Books for Data Science
- Kaggle for Practice
Note: This cheat sheet provides a basic overview of data science concepts. Expand each section with more detailed information based on your needs.