List of Experiments / Activities
- 1Clean and preprocess a business dataset by handling missing values, duplicates, and outliers using Excel.
- 2Create bar charts, line charts, pie charts, and scatter plots using Excel and interpret business insights from the visualisations.
- 3Generate histograms and box plots to analyse data distribution and identify patterns in business datasets.
- 4Design an interactive dashboard with KPIs, filters, and slicers using Excel for business performance monitoring.
- 5Develop a data story using visualisations and business insights and present recommendations for managerial decision-making.
- 6Create basic business visualisations in R using sales, customer, or financial datasets and interpret the findings.
- 7Create data visualisations in Python using Matplotlib or Seaborn and analyse trends and relationships in business data.
- 8Analyse social media performance metrics such as reach, impressions, engagement rate, and conversions using sample datasets.
- 9Prepare a social media analytics report and recommend strategies to improve audience engagement and campaign effectiveness.
- 10Perform sentiment analysis on customer reviews using AI tools and classify feedback into positive, negative, and neutral categories.
- 11Evaluate digital marketing campaign performance by calculating CTR, CPC, CPA, and conversion rates from campaign data.
- 12Analyse website traffic reports and interpret key web analytics metrics such as page views, sessions, bounce rate, and conversion rate.
- 13Perform exploratory data analysis (EDA) on business datasets using Excel, R, or Python to identify trends, patterns, and correlations.
- 14Conduct correlation, regression, and forecasting analysis on business data and interpret predictive insights for decision-making.
- 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)
After completion of the course student will be able to:
- CO1Apply data cleaning, preprocessing, and visualization techniques to prepare and present business data effectively. (Apply)K3
- CO2Prepare charts, dashboards, KPI reports, and data stories to communicate business insights and support managerial decision-making. (Apply)K3
- CO3Apply analytical techniques on social media, web, and business datasets to identify trends, patterns, customer behaviour, and campaign performance. (Apply)K3
- CO4Apply exploratory, statistical, and predictive analytics techniques using appropriate analytical tools to generate business forecasts and insights. (Apply)K3
- CO5Interpret the outcomes of predictive models, digital marketing campaigns, and analytics-driven business solutions for organisational effectiveness. (Understand)K2
Suggested Readings (Latest Editions)
- Knaflic, C. N. Storytelling with Data: A Data Visualization Guide for Business Professionals. Wiley.
- 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.
- Albright, S. C., & Winston, W. L. Business Analytics: Data Analysis and Decision Making (8th ed.). Cengage Learning.
- Kaushik, A. Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity. Sybex.
- Few, S. Information Dashboard Design: Displaying Data for At-a-Glance Monitoring (3rd ed.). Analytics Press.
- Goyal, D., & Srivastava, A. Business Analytics Using Excel and Power BI. BPB Publications.
- 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 97, 98 of the original AKTU PDF.