Introduction to SQL

• What is SQL and why it matters in AI & data workflows
• Understanding relational databases and schemas
• Setting up a SQL environment (e.g., MySQL, SQLite, PostgreSQL)
• Writing basic queries with SELECT
• Filtering data using WHERE, BETWEEN, LIKE, IN
• Sorting and limiting results (ORDER BY, LIMIT)
• Combining data using JOIN (INNER, LEFT, RIGHT, FULL)
• Aggregation with GROUP BY, HAVING, and built-in functions (SUM, COUNT, AVG, etc.)
• Working with subqueries and nested statements
• Data types and constraints (NULL, PRIMARY KEY, FOREIGN KEY)
• Creating, updating, and deleting records (INSERT, UPDATE, DELETE)
• Best practices for structuring and querying databases

Exploratory Data Analysis (EDA)

• What is EDA and why it’s important
• Workflow of an EDA process
• Descriptive statistics and summary metrics
• Data profiling and quality assessment
• Detecting and handling missing values
• Identifying outliers and anomalies
• Visualizing distributions (histograms, box plots, KDE)
• Exploring relationships (scatter plots, heatmaps, pair plots)
• Correlation analysis and multicollinearity checks
• Categorical vs numerical data exploration
• Using Seaborn and Matplotlib for custom visualizations
• Creating data stories and dashboards with insights
• Documenting EDA findings for modeling phases

Data Visualization in Python

• Importance of data visualization in AI and data science
• Introduction to Python visualization libraries: Matplotlib, Seaborn, Plotly
• Creating line, bar, and scatter plots with Matplotlib
• Styling and customizing plots (labels, legends, titles, colors)
• Building advanced visualizations: histograms, box plots, pie charts
• Heatmaps and correlation matrices
• Plotting time series and trends
• Using Seaborn for statistical visualizations (violin plots, pair plots, swarm plots)
• Introduction to interactive visualizations with Plotly
• Creating subplots and multi-panel charts
• Saving and exporting high-quality visualizations
• Best practices for visual storytelling and design clarity

Essential Mathematics for Data Science

• Linear Algebra: Scalars, Vectors, and Matrices, Basic Vector Operations, Dot Product
• Intro to Calculus: Matrix Transposition, Basic Matrix Operations, Matrix Multiplication
• Calculus Intuition: What is a Function?, The Concept of a Derivative, Finding Minima and Maxima
• Calculus for Optimization & Introduction to Probability: Partial Derivatives, The Gradient, Gradient Descent Algorithm
• Fundamentals of Probability: Basic Terminology, Probability Axioms & Basic Rules, Conditional Probability, Independence of Events
• Essential Probability & Statistics: Bayes' Theorem, Descriptive Statistics, Random Variables & Probability Distributions, Tying it All Together & Next Steps

Machine Learning Fundamentals

• What is Machine Learning and how it differs from traditional programming

• Categories of ML: Supervised, Unsupervised, and Reinforcement Learning

• Understanding datasets: features, labels, and data splitting

• Overview of the ML pipeline: preprocessing to deployment

• Introduction to Scikit-learn and its ecosystem

• Data preprocessing techniques
• Model selection and training
• Evaluating models
• Cross-validation and overfitting vs underfitting
• Hyperparameter tuning basics
• Real-world use cases of ML in business and tech
• Ethical considerations and fairness in machine learning