skilldevelopment.cpe@lpu.co.in

What you'll learn

A fully practical, hands-on program that trains students to work with real data using SQL, Python, PowerBI and AI tools. The course focuses on doing rather than theory, enabling learners to clean data, run analysis, build dashboards, apply AI, and generate research-style insights. Objectives: • enable students to clean, prepare and manage datasets practically • develop competency in SQL for real business data operations • build practical skills in Python for analysis and basic AI modelling • train students to create interactive dashboards using PowerBI • apply AI tools to automate analytics and generate insights

  • loading csv and excel files; merging datasets; filtering data; sorting data; handling missing values; removing duplicates; formatting columns; date-time conversions; quick numeric summaries; exporting cleaned datasets for SQL and PowerBI.
  • creating tables from cleaned datasets; inserting records; retrieving records using select; applying where conditions; joining multiple tables; grouping and aggregating real business data; creating calculated fields; applying window functions on sales and HR data; extracting segments; generating summary tables for PowerBI import.
  • using pandas for data slicing; reshaping and pivoting; detecting and treating outliers; generating visual analytics using matplotlib; performing correlation checks; training a basic regression model; forecasting numeric values using regression; performing basic classification; checking accuracy; running sentiment analysis on text; exporting python outputs for PowerBI integration.
  • importing SQL and python outputs; transforming data using power query; creating calculated columns; writing dax measures; building sales dashboards; building HR dashboards; creating drill-throughs; adding slicers and filters; formatting visuals; creating KPI cards; using conditional formatting; exporting dashboard for sharing.
  • running predictive models using python; applying logistic regression on classification tasks; generating confusion matrix and interpretation; running sentiment analysis using textblob or nltk; preparing correlation matrix; performing simple hypothesis checks using python; interpreting regression output in python; generating insight tables; converting findings into structured reports.
  • using AI to generate python code for analytics; using AI to write SQL queries; using AI for EDA generation; AI-assisted data cleaning instructions; AI-based feature engineering suggestions; using AI for business question generation; AI-driven insight extraction from datasets; AI-assisted visualization recommendations; using AI to automate report writing; combining AI + PowerBI for insights; exporting AI-generated narratives.

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Dr. Anup Sharma
Associate Professor

Dr. Anup Sharma is an IIM-Ahmedabad Alumni, with Industry and academic experience of over 10 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.


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Dr. Pritpal Singh
Professor

B.Tech, MBA, UGC-NET, Ph.D. (Healthcare Analytics); Professor; Mittal School of Business; Lovely Professional University, Phagwara, Punjab, India; Dr. Pritpal Singh is currently working as a Professor at the Mittal School of Business, Lovely Professional University, Phagwara, Punjab (India). With an academic career spanning over 15 years, he has been actively involved in teaching and mentoring students in various domains. His core teaching areas include Databases for Managers, Data Visualization using Tableau, Data Analysis using Python, Data Mining and Data Warehousing, and SQL Commands. He has contributed significantly to academics and applied research, particularly in the field of healthcare analytics, aligning both his doctoral work and teaching expertise with emerging industry needs.