UNIQUE SERIES AKTU Syllabus
BBA551

Analytics and AI Lab–1

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

BBA · Sem 5 (Lab)

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List of Experiments / Activities

  1. 1Explore Generative AI tools for business applications and prepare a report on their use in marketing, finance, HR, operations, and customer service.
  2. 2Create charts and dashboards using Excel from business datasets and interpret key performance indicators for managerial decision-making.
  3. 3Perform descriptive analytics using Excel by analysing sales, customer, or financial datasets and preparing summary reports.
  4. 4Conduct correlation and trend analysis in Excel to identify relationships and patterns in business data.
  5. 5Design and evaluate prompts for Generative AI tools for content creation, report writing, and business communication.
  6. 6Generate marketing content using Generative AI tools, including product descriptions, promotional messages, and social media posts.
  7. 7Prepare HR documents using Generative AI tools, such as job descriptions, interview questions, and employee communication materials.
  8. 8Generate business and financial summaries using Generative AI tools and evaluate the quality and relevance of the outputs.
  9. 9Analyse customer reviews using Generative AI tools and identify customer sentiments, concerns, and improvement opportunities.
  10. 10Analyse business problems such as customer churn, declining sales, inventory issues, or recruitment challenges and propose AI-enabled solutions.
  11. 11Evaluate AI-generated outputs for bias, fairness, transparency, hallucinations, and privacy concerns using practical examples.
  12. 12Introduction to Business Analytics Using R – Install R and RStudio, import a simple business dataset, and generate basic statistical summaries and visualisations.
  13. 13Data Visualisation in R – Create bar charts, pie charts, and line graphs using a business dataset and interpret the managerial insights obtained from the visualisations.
  14. 14Introduction to Business Analytics Using Python – Set up the Python environment, import a business dataset using Pandas, and generate basic descriptive statistics.
  15. 15Data Visualisation in Python – Create bar charts, histograms, and line charts using Matplotlib and interpret business trends and patterns from the visual outputs.
Course Outcomes (COs)6 outcomes

After completion of the course student will be able to:

  1. CO1Explain the fundamental concepts of artificial intelligence, machine learning, deep learning, data science, and business analytics in business contexts. (Understand)K2
  2. CO2Apply descriptive, diagnostic, predictive, and prescriptive analytics techniques using business data and analytical tools to support decision-making. (Apply)K3
  3. CO3Apply visualisation techniques, dashboards, KPIs, correlation analysis, and forecasting methods on business datasets to derive meaningful insights. (Apply)K3
  4. CO4Prepare and use effective prompts for Generative AI applications to solve business problems in marketing, finance, human resources, and reporting. (Apply)K3
  5. CO5Illustrate the applications of AI across various business functions and their impact on organisational performance and digital transformation. (Understand)K2
  6. CO6Explain the ethical, legal, governance, and strategic issues associated with AI adoption and responsible AI practices for organisations. (Understand) MARKETINGK2

Suggested Readings (Latest Editions)

  1. Kumar, U. D. . Business Analytics: The Science of Data-Driven Decision Making. Wiley India.
  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. Russell, S., & Norvig, P. . Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
  4. Mollick, E. . Co-Intelligence: Living and Working with AI. Portfolio/Penguin Random House.
  5. Albright, S. C., & Winston, W. L. . Business Analytics: Data Analysis and Decision Making (8th ed.). Cengage Learning.
  6. Acharya, S., & Chellappan, S. . Business Analytics. Wiley India.
  7. Prasad, R. N., & Acharya, S. . Fundamentals of Business Analytics. Wiley India.
  8. Goyal, D., & Srivastava, A. Business Analytics Using Excel and Power BI. BPB Publications.
  9. Hodeghatta, U. R., & Nayak, U. Practical Business Analytics Using R and Python. Pearson India.
  10. Bhatia, P. S. (2024). Artificial Intelligence and Machine Learning for Business Applications. BPB Publications.

Official AKTU syllabus

This subject is on page 15, 16, 17, 18 of the original AKTU PDF.

Official AKTU syllabus PDF ↗