UNIQUE SERIES AKTU Syllabus
BMBAI652

Analytics and AI Lab–2

L-T-P0-0-2 Credits1 Session2026-27 (W.E.F 2024-25) TypeLab / Practical
Appears in 1 semester

MBA Integrated · Sem 6 (Lab)

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List of Experiments / Activities

  1. Course Contents
  2. 1Clean and preprocess a business dataset by handling missing values, duplicates, and outliers using Excel.
  3. 2Create bar charts, line charts, pie charts, and scatter plots using Excel and interpret business insights from the visualisations.
  4. 3Generate histograms and box plots to analyse data distribution and identify patterns in business datasets.
  5. 4Design an interactive dashboard with KPIs, filters, and slicers using Excel for business performance monitoring.
  6. 5Develop a data story using visualisations and business insights and present recommendations for managerial decision-making.
  7. 6Create basic business visualisations in R using sales, customer, or financial datasets and interpret the findings.
  8. 7Create data visualisations in Python using Matplotlib or Seaborn and analyse trends and relationships in business data.
  9. 8Analyse social media performance metrics such as reach, impressions, engagement rate, and conversions using sample datasets.
  10. 9Prepare a social media analytics report and recommend strategies to improve audience engagement and campaign effectiveness.
  11. 10Perform sentiment analysis on customer reviews using AI tools and classify feedback into positive, negative, and neutral categories.
  12. 11Evaluate digital marketing campaign performance by calculating CTR, CPC, CPA, and conversion rates from campaign data.
  13. 12Analyse website traffic reports and interpret key web analytics metrics such as page views, sessions, bounce rate, and conversion rate.
  14. 13Perform exploratory data analysis (EDA) on business datasets using Excel, R, or Python to identify trends, patterns, and correlations.
  15. 14Conduct correlation, regression, and forecasting analysis on business data and interpret predictive insights for decision-making.
  16. 15Develop a mini-project on predictive analytics by analysing a business problem, creating visualisations, generating forecasts, and presenting managerial recommendations through a report and presentation.
Course Outcomes (COs)5 outcomes

After completion of the course student will be able to:

  1. CO1Apply data cleaning, preprocessing and visualisation techniques to prepare business data for analysis.K3 (Apply)
  2. CO2Prepare charts, dashboards, KPI reports and data stories to communicate business insights.K3 (Apply)
  3. CO3Use analytical tools to examine social media, web and business datasets for trends and campaign performance.K3 (Apply)
  4. CO4Apply exploratory, statistical and predictive analytics techniques to generate business forecasts.K3 (Apply)
  5. CO5Interpret the outputs of predictive models and analytics-driven solutions for managerial decision-making.K2 (Understand)

Suggested Readings (Latest Edition)

  1. Knaflic, C. N. Storytelling with Data: A Data Visualization Guide for Business Professionals. Wiley.
  2. Provost, F., & Fawcett, T. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking (2nd ed.). O’Reilly Media.
  3. Albright, S. C., & Winston, W. L. Business Analytics: Data Analysis and Decision Making (8th ed.). Cengage Learning.
  4. Kaushik, A. Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity. Sybex.
  5. Few, S. Information Dashboard Design: Displaying Data for At-a-Glance Monitoring (3rd ed.). Analytics Press.
  6. Goyal, D., & Srivastava, A. Business Analytics Using Excel and Power BI. BPB Publications.
  7. Gupta, D., Gupta, M., & Gupta, P. Social Media and Web Analytics: Turning Insights into Action in a Digital World. BPB Publications.

Official AKTU syllabus

This subject is on page 26, 27 of the original AKTU PDF.

Official AKTU syllabus PDF ↗