skilldevelopment.cpe@lpu.co.in

What you'll learn

Course Description This hands-on workshop is designed to equip students with essential skills in Business Analytics using Python. Blending technical proficiency with business insight, the course introduces students to data handling, exploratory data analysis, statistical testing, and predictive modeling through real-world business datasets. Participants will learn to extract actionable insights from data and use analytics to support data-driven decision-making. Using industry-standard tools such as pandas, NumPy, matplotlib, seaborn, and scikit-learn, students will work on case studies in marketing, HR, sales, and finance. The workshop culminates in a capstone project where learners apply their knowledge to solve a business problem using an end-to-end analytics pipeline. Prior programming knowledge is not mandatory, but familiarity with basic Python or statistics is beneficial. Course Outcomes: 1. Build and evaluate business models with real-world datasets 2. Derive actionable insights from data to support decisions 3. Visualize and communicate results effectively to business stakeholders 4. Be proficient in using Python for business data analysis

  • "What is Business Analytics? Real-world use cases Introduction to Python (syntax, variables, data types) Data structures: Lists, Tuples, Dictionaries Writing basic Python scripts Assignment: Simple Python exercises + business scenario-based logic problems"
  • "Reading data from CSV/Excel DataFrames and Series objects Filtering, sorting, grouping, merging Business Case: Analyzing sales data Assignment: Clean and explore a sales dataset"
  • "Handling missing values, duplicates Data transformation and feature engineering Label encoding, one-hot encoding Business Case: HR attrition dataset Assignment: Preprocess HR dataset and create new variables"
  • "Descriptive statistics Data visualization using Matplotlib & Seaborn Correlation, boxplots, heatmaps, histograms Business Case: Market segmentation insights Assignment: EDA report generation for a customer dataset"
  • "Probability distributions Hypothesis testing (t-test, chi-square test) Confidence intervals and p-values Business Case: A/B testing of marketing campaigns Assignment: Analyze a test campaign result and draw conclusions"
  • "Linear regression with statsmodels and sklearn Interpreting coefficients, R², residuals Business Case: Revenue forecasting Assignment: Build and evaluate a revenue prediction model"
  • "Logistic regression, Decision Trees Confusion matrix, precision, recall, F1-score Business Case: Predicting loan default or churn Assignment: Apply logistic regression to classify customer churn"
  • "K-Means Clustering Choosing optimal clusters (elbow method) Customer segmentation case study Visualizing clusters Assignment: Perform clustering on customer purchase data"
  • "Time series components ARIMA basics using statsmodels Business Case: Sales or stock price forecasting Assignment: Analyze time series trends from sales dataset"
  • "Choose one business problem, apply end-to-end analytics (EDA + modeling + insights) Introduction to Python Dash/Streamlit Presenting insights in dashboards Dashboard presentation"

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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.