Course Archives    Theoretical Statistics and Mathematics Unit

Course: Business Analytics I Level: Postgraduate Time: Currently not offered

Syllabus Past Exams Syllabus 1. Introduction to Analytics (7) Introduction, reasons for the popularity of business analytics and/or data science, examples of analytics problems, components of an analytics/data science project, important analytics tasks, hypothesis testing, predictive modeling, discovering dimensions, profiling, association, and concurrence grouping, link prediction, constraint optimization, etc. 2. Introduction to R and R Studio (4) Getting started, installation of R and R Studio, comments, variables, data types, numbers, arithmetic & assignment operators, logical or comparison operators, R math functions, arrays, conditional statements - if, else if, if else, nested if statements, loops - while loop & for loop, R functions, getting help, R packages, vectors, matrix, data frames, importing and exporting data, data summarization, visualization. 3. Introduction to Python (8) Getting started, installation of Python, Anaconda Navigator, & Jupyter Notebook, variables, comments, data types, Python keywords, arithmetic operators, comparison operators, Python math functions, lists, arrays, array functions, conditional statements - if else, loops - while loop & for loop, Python functions, data frames, importing and exporting data, data summarization & Visualization. 4. Data Preprocessing (6) Data sorting, filtering, file merging, feature generation, file appending, missing value handling - checking for missing values, identifying fields with missing values, deleting missing value records, missing value imputation techniques, identification of zeros, duplicate records, unique records, etc. in datasets and anomaly detection. 5. Data Visualization using R & Python (6) Correlation, scatter plot, data aggregation, box plot, interval plot, density plot, conditional density plot, cross tabulation & contingency tables, bar chart, pie chart, mosaic plot. 6. Descriptive Analytics (6) Summarization of large volumes of data into formats such as tables, charts, graphs, or dashboards, Applications of descriptive analytics - business performance analysis, market research, customer segmentation, inventory management, quality control, logistics & transportation, maintenance & repair, employee performance, etc., descriptive analytics examples in sales and supply chain management. 7. Introduction to Database Management (5) Relational Database Management System (RDBMS), database tables, MySQL, MySQL statements - Select, where, and, or, not, order by, insert into, update, delete, joins, functions, etc. 8. RFM Analysis & Customer Segmentation (4) Cosine similarity, collaborative filtering: user-based, item-based, content-based filtering, and hybrid methods. 9. Association and Co-occurrence grouping (4) Market Basket Analysis - Frequent itemset generation and association rule mining. Referemce Texts: 1. Jones, O., Maillardet, R., & Robinson, A. (2009). Introduction to scientific programming and simulation using R. Chapman & Hall/CRC. 2. Matloff, N. (2011). The art of R programming: A tour of statistical software design. No Starch Press. 3. Crawley, M. J. (2012). The R book (2nd ed.). John Wiley & Sons. 4. Chambers, J. M. (2008). Software for data analysis: Programming with R. Springer. 5. Unpingco, J. (2016). Python for probability, statistics, and machine learning. Springer International Publishing. 6. Kumar, A. (2019). Mastering pandas: A complete guide to pandas, from installation to advanced data analysis techniques. Packt Publishing Ltd. 7. Lambert, K. A. (2018). Fundamentals of Python: First programs (2nd ed.). Cengage Learning. 8. Haslwanter, T. (2016). An introduction to statistics with Python: With applications in the life sciences. Springer International Publishing. 9. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: With applications in R. Springer Top of the page Past Exams

Top of the page

[ Semester Schedule ][ Statmath Unit ] [Indian Statistical Institute]