LPU - Lovely Professional University
What you'll learn

This course equips students with practical skills in Python and SQL for real-world data analytics projects. Through 60 hours of hands-on learning, students will write efficient SQL queries, manipulate and visualize data using Python, and integrate both tools to extract meaningful insights. Students will confidently analyze datasets, interpret results, and communicate data-driven decisions effectively.

  • Installing Python and Jupyter, Variables and Data Types, Input/Output Operations, Writing and Running First Scripts
  • 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

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Dr. Anup Sharma
Associate Professor

Dr. Anup Sharma is an IIM-Ahmedabad Alumni, with Industry and academic experience of over 13 years. He is currently serving as Associate Professor and HoD (MBA Operation, IT and Analytics). He has his expertise in Business Analytics and advance applications of Python in Research and Automation.