Understand what data science is and explore the data science workflow and methodology.

• What is data science
• Data science vs statistics vs analytics
• Data science workflow and methodology
• Types of data and data sources
• Data science roles and career paths
• Business value of data science
• Ethics in data science
• Tools and technologies overview

Master Python programming fundamentals and essential libraries for data science.

• Python basics and syntax
• Data structures (lists, dictionaries, sets)
• Control flow and functions
• Object-oriented programming basics
• File handling and I/O operations
• Error handling and debugging
• Virtual environments and package management
• Python development environment setup

Learn array operations, mathematical functions, and numerical computing with NumPy.

• NumPy arrays and data types
• Array creation and indexing
• Array operations and broadcasting
• Mathematical and statistical functions
• Linear algebra operations
• Random number generation
• Array reshaping and manipulation
• Performance optimization with NumPy

Master data manipulation, cleaning, and analysis using the Pandas library.

• Series and DataFrame fundamentals
• Data loading and saving (CSV, JSON, Excel)
• Data selection and filtering
• Data cleaning and preprocessing
• Handling missing data
• Data transformation and aggregation
• Merging and joining datasets
• GroupBy operations and pivot tables

Create compelling visualizations using Matplotlib, Seaborn, and other visualization tools.

• Principles of effective data visualization
• Matplotlib basics and customization
• Seaborn for statistical visualizations
• Plot types (scatter, line, bar, histogram)
• Subplots and figure management
• Interactive visualizations with Plotly
• Geographical data visualization
• Dashboard creation basics

Learn systematic approaches to explore and understand datasets through analysis and visualization.

• EDA methodology and best practices
• Descriptive statistics and summaries
• Distribution analysis
• Correlation and relationship discovery
• Outlier detection and treatment
• Data profiling techniques
• Hypothesis generation
• EDA report creation

Build foundational statistical knowledge essential for data science applications.

• Descriptive vs inferential statistics
• Probability distributions
• Central limit theorem
• Hypothesis testing
• Confidence intervals
• P-values and statistical significance
• Correlation vs causation
• Bayesian vs frequentist approaches

Master techniques for cleaning messy data and preparing it for analysis.

• Data quality assessment
• Handling missing values
• Outlier detection and treatment
• Data type conversions
• Text data cleaning
• Data validation techniques
• Feature scaling and normalization
• Data transformation pipelines

Learn machine learning fundamentals and basic algorithms for predictive modeling.

• Machine learning concepts and types
• Supervised vs unsupervised learning
• Training, validation, and test sets
• Cross-validation techniques
• Overfitting and underfitting
• Bias-variance tradeoff
• Model evaluation metrics
• Scikit-learn library basics

Understand linear and logistic regression for predicting continuous and categorical outcomes.

• Simple linear regression
• Multiple linear regression
• Regression assumptions and diagnostics
• Feature selection and engineering
• Regularization (Ridge, Lasso)
• Logistic regression
• Model interpretation
• Regression evaluation metrics

Learn popular classification algorithms for predicting categorical outcomes.

• Decision trees and random forests
• Support vector machines
• Naive Bayes classifier
• K-nearest neighbors
• Ensemble methods
• Classification evaluation metrics
• Confusion matrices and ROC curves
• Handling imbalanced datasets

Explore unsupervised learning techniques for pattern discovery and data segmentation.

• K-means clustering
• Hierarchical clustering
• DBSCAN clustering
• Principal Component Analysis (PCA)
• Dimensionality reduction techniques
• Clustering evaluation metrics
• Market segmentation applications
• Anomaly detection basics

Analyze temporal data patterns and build forecasting models for time-dependent data.

• Time series components
• Trend and seasonality analysis
• Time series decomposition
• Moving averages and smoothing
• ARIMA models
• Forecasting techniques
• Time series visualization
• Real-world time series applications

Learn to execute complete data science projects from problem definition to deployment.

• Problem definition and scoping
• Data collection strategies
• Project planning and management
• Version control with Git
• Reproducible research practices
• Model deployment basics
• Project documentation
• Presenting results to stakeholders