B.Sc. (Data Science) Retail Marketing Analytics Practical Syllabus - Mumbai University

This is the Fourth Year BSc Data Science Honours syllabus under NEP 2020, phased in one year at a time, from the academic year 2024-25. The University still sets the earlier Choice Based papers alongside it for ATKT candidates, so check which scheme your exam form names before you revise.

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Syllabus for Retail Marketing Analytics Practical

Major elective · Semester 7 · Fourth Year BSc Data Science Honours · 2 credits · 50 marks

Module I

1. Learn how to tabulate and summarize marketing data using R.
Clean and preprocess the marketing data.
Generate a simple histogram plot to visualize data distribution.
Use tabulation and summary functions to gain insights from the data.
Interpret the findings and discuss the implications for marketing analysis. 2. Gain proficiency in visualizing marketing data using R.
Understand the key elements of data visualization.
Create various visualizations such as histograms, scatter plots, line plots, and bar charts using the ggplot() function in R.
Apply appropriate visualization techniques to effectively communicate marketing insights. 3. Design and conduct experiments for marketing campaigns.
Learn about experimental design and its application in marketing.
Design experiments using examples from marketing scenarios.
Implement randomization and sample splitting techniques.
Conduct the experiments and collect relevant data for analysis. 4. Understand the concept of hypothesis testing and its role in assessing experiment outcomes.
Explore the purpose of hypothesis testing in analyzing experiment results.
Familiarize with key terminologies related to hypothesis testing.
Learn the process of hypothesis testing and power calculation.
Conduct hypothesis testing using R to evaluate experiment outcomes. 5. Calculate and predict Customer Lifetime Value (CLV).
Calculate CLV using different approaches and frameworks.
Explore predictive modeling techniques such as linear regression and logistic regression for CLV prediction.
Assess the accuracy and reliability of CLV predictions.

Module II

1. Apply CLV analysis and cohort analysis in marketing analytics.
Analyze CLV data and identify patterns and trends.
Perform cohort analysis to segment customers based on their behavior or characteristics.
Interpret the results of CLV analysis and cohort analysis to derive actionable insights for marketing strategies. 2. Extract data from social media platforms and perform analysis to gain insights into customer behavior and preferences.
Utilize Python libraries like BeautifulSoup and requests to scrape data from social media platforms.
Clean and preprocess the scraped data.
Analyze the data to identify trends, sentiment analysis, or customer engagement metrics.
Visualize the findings using appropriate charts or graphs. 3. Analyze customer purchasing patterns and build a recommender system based on market basket analysis.
Use transactional data to identify frequently occurring item sets using association rule mining algorithms.
Calculate support, confidence, and lift for the identified item sets.
Build a recommendation engine using collaborative filtering techniques.
Evaluate the performance of the recommender system and make recommendations based on customer preferences. 4. Segment customers based on their recency, frequency, and monetary value (RFM) to better target marketing efforts.
Analyze customer transaction data to calculate RFM scores.
Segment customers into different groups using clustering algorithms such as k-means or hierarchical clustering.
Perform descriptive analysis on each customer segment to understand their characteristics.
Develop targeted marketing strategies for each segment based on their RFM profiles. 5. Conduct A/B testing to evaluate the impact of different marketing strategies and make data-driven decisions.
Design and implement A/B tests for marketing campaigns using randomized assignment.
Collect relevant data and perform statistical analysis to compare the performance of different strategies.
Calculate key metrics such as conversion rates, click-through rates, or revenue.
Interpret the results and provide recommendations for optimizing marketing campaigns based on the findings. 10 Text Books 1. Hands-on Data Science for Marketing by Yoon Hyup Hwang, Packt Publishing, 2019 2. Retail Analytics: The Secret Weapon by Emmett Cox,1st edition , Weily , 2011 11 Reference Books 1. Cutting Edge Marketing Analytics: Real World Cases and Data Sets for Hands on Learning by Venkatesan Rajkumar, Farris Paul and Ronald Wilcox, Pearson FT Press, 2014 2. Marketing Analytics: A Practical Guide to Real Marketing Science by Grigsby Mike,Kogan Page, 2015 12 Semester End Examination: 100%

Text Books

1 Hands-on Data Science for Marketing by Yoon Hyup Hwang, Packt Publishing, 2019
2 Retail Analytics: The Secret Weapon by Emmett Cox,1st edition , Weily , 2011
1 Cutting Edge Marketing Analytics: Real World Cases and Data Sets for Hands on Learning by Venkatesan Rajkumar, Farris Paul and Ronald Wilcox, Pearson FT Press, 2014
2 Marketing Analytics: A Practical Guide to Real Marketing Science by Grigsby Mike,Kogan Page, 2015

Reproduced from the University of Mumbai syllabus for B.Sc. (Data Science) under NEP 2020. Wording is as printed in that syllabus. Module numbering is as printed there too.

The complete syllabus

This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.

PDF 2024 25 DS SEM I & II NEP NEP 2020 syllabus, in force from 2024-25 Read full PDF Read

PDF 2023 24 BSc Data Science Sem V & VI Earlier Choice Based syllabus, still set for ATKT candidates Read full PDF Read

PDF 2021 22 BSc Data Science Sem III & IV Earlier Choice Based syllabus, still set for ATKT candidates Read full PDF Read

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