Lists and Tuples, Dictionaries and Sets, Indexing and Slicing, Nested Structures

if / elif / else, Comparison and Logical Operators, Nested Conditions, Real-world Decision Problems

for Loops, while Loops, break / continue / pass, Looping over Data Structures

Defining and Calling Functions, Arguments and Return Values, Lambda Functions, Scope and Reusability

Reading CSV and Excel Files, Writing Output Files, os and glob for File Management, Error Handling with try/except

Arrays and Array Operations, Reshaping and Indexing, Mathematical Functions, Random Number Generation

DataFrames and Series, Filtering and Sorting, groupby and Aggregation, Merging and Reshaping Data

Handling Missing Values, Removing Duplicates, Data Type Conversion, Outlier Detection and Treatment

Measures of Central Tendency, Measures of Spread and Variability, Frequency Distribution, Correlation Analysis

Line, Bar and Pie Charts, Histograms and Box Plots, Subplots and Layouts, Customizing Titles, Labels and Colors

Heatmaps and Pairplots, Violin and Swarm Plots, Regression Plots, Styling and Themes

Plotly Express Charts, Hover and Zoom Features, Interactive Dashboards with Dash, Exporting Interactive Visuals

Predictive vs Descriptive Analytics, Train/Test Split, Model Evaluation Metrics, Overfitting and Underfitting

Concept of Regression, Building and Fitting the Model, Interpreting Coefficients, Residual Analysis

Multiple Predictors, Multicollinearity and VIF, Feature Selection, Model Evaluation (R², RMSE)

Binary Classification, Multinomial Logistic Regression, Confusion Matrix and Accuracy, ROC-AUC Curve

Tree Structure and Splitting Criteria, Gini Impurity and Entropy, Pruning and Depth Control, Visualizing the Tree

Ensemble and Bagging Concept, Building Random Forest, Feature Importance, Tuning n_estimators and max_depth

Boosting vs Bagging, XGBoost Model Building, Hyperparameter Tuning, Comparing with Random Forest

Unsupervised Learning Concept, Choosing K with Elbow Method, Fitting and Labeling Clusters, Visualizing Clusters

Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naive Bayes Classifier, Choosing the Right Algorithm

Optimization Concepts, Linear Programming with SciPy, Scenario and Sensitivity Analysis, Business Decision Simulation

Cross-Validation (k-fold), Bias-Variance Tradeoff, Comparing Models with Metrics, Building a Model Report

Relational Database Concepts, Installing MySQL/PostgreSQL, Creating Databases and Tables, Data Types in SQL

SELECT, WHERE, ORDER BY, DISTINCT, Aggregate Functions (SUM, AVG, COUNT), GROUP BY and HAVING, Aliases

INNER, LEFT, RIGHT and FULL JOIN, Primary and Foreign Keys, Joining Multiple Tables, Real-world Join Scenarios

Subqueries and Nested Queries, Window Functions (RANK, ROW_NUMBER), CTEs (WITH clause), Query Optimization

Connecting MySQL/PostgreSQL via SQLAlchemy, Running SQL Queries from Python, Loading SQL Results into Pandas, Writing DataFrames back to Database

Problem Definition and Dataset Selection, SQL Querying + Python Analysis, ML Model Building and Visualization, Insight Presentation and Storytelling