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Home Courses IIM Indore | Integrated Programme in Business Analytics (IPBA Batch - 18)

IIM Indore | Integrated Programme in Business Analytics (IPBA Batch - 18)

To understand advanced analytics tools and techniques.

Course Snapshot

  • FeeINR 3,20,000 + GST
  • Work Experience2 - 30 Years
  • Duration10 Months
  • Delivery MethodBlended- Online

Course Detail

Programme Overview:

The Integrated Programme in Business Analytics (IPBA) offers participants a dynamic learning experience focused on essential business skills. With modules in strategic planning, financial management, team leadership, and effective marketing, the programme equips individuals with the tools to succeed.

  • Provides participants with diverse business skills.
  • Focuses on strategic planning, financial management, and team leadership.
  • Emphasizes effective marketing and sales strategies.

Programme Takeaways:

Upon finishing the programme, students gain proficiency in advanced analytics tools, strategic data application, industry insights, effective communication of findings, and practical experience through projects and case studies.

  • Proficiency in advanced analytics tools and techniques.
  • Strategic application of data for business decision-making.
  • Industry collaboration provides valuable insights for a globally relevant skill set.
  • Ability to communicate complex analytics findings effectively.
  • Practical experience through hands-on projects and real-world case studies.
  • A holistic curriculum covering all aspects of business analytics.
  • Ethical considerations are integrated into decision-making processes.

Desired Candidate Profile

  • 2+ years work experience and 50% marks in UG/PG.

Course Modules

Module 01 - Introduction to Analytics:

  • Introduction to Analytics and CRISP DM
  • Data Collection and Biases

Module 02 - R:

  • Intro to R
  • Generating and Using Summary Statistics
  • Distributions and Histograms with R
  • Empirical Distributions
  • R data manipulation
  • Business Case Study - R data manipulation

Module 03 - Inferential Statistics:

  • Concepts of Probability
  • Discrete & Continuous distributions S
  • Sampling theory
  • Parameter estimation via confidence interval
  • Basics of hypothesis testing, 1-sample tests (mu, p), one-sided, two-sided, via CI, p-value
  • 2-sample (paired & independent) tests (means), Equality of variance test
  • Nonparametric tests (sign test, WSRT, Mann-Whittney test), test for normality
  • k-sample test for mean: ANOVA, Kruskal-Wallis test
  • Chi-square tests for goodness of fit, independence, homogeneity
  • Business Case study- Descriptive + Inferential Statistics

Module 04 - SQL (MySQL server):

  • SQL Servers as Data Sources
  • Data Normalization and Consequence
  • Basic SQL DML Queries
  • SQL Joins
  • Business Case study - SQL DML commands
Module 05 - Feature Engineering with R:
  • Data Exploration and Visualization in R + Data Sanity checks and treatment.
  • Using GitHub & Kaggle to build an analytics profile.

Module 6 - GLM:

  • Linear Regression
  • Business Case Study - Linear Regression
  • Logistic Regression
  • Business Case Study -Logistics Regression

Module 7 - Time Series:

  • Time Series Forecasting
  • Business Case study - Time Series Forecasting

Module 8 - Python:

  • Introduction to Python- Basic Data Structures
  • Python Basic Data Structures & Data Manipulation
  • Python - Data Exploration - Sanity Checks
  • Preparing Data Quality Reports
  • Python- Data Preparation -Outliers and Missing Value Treatments
  • Variable Profiling Using Information Value
  • Business Case study (EDA) - Python

Module 9 - Machine Learning:

  • Introduction to Python- Basic Data Structures
  • Python Basic Data Structures & Data Manipulation
  • Python - Data Exploration - Sanity Checks
  • Preparing Data Quality Reports
  • Python- Data Preparation -Outliers and Missing Value Treatments
  • Variable Profiling Using Information Value
  • Business Case study (EDA) - Python

Module 10 - Text Mining & Introduction to NLP:

  • Text Handling - Reading Text Files at Scale
  • Using Regular Expressions to Clean Text
  • Handling Text Encoding Issues
  • Tokenization, stemming and lemmatization
  • POS Tagging
  • Parsing Grammatical Trees
  • Named Entity Recognition
  • Modelling - Text Representation, TFIDF, Count Vector
  • Cosine Similarity of Text Corpus
  • Using TFIDF features to build sentiment classifiers
  • Handling Image data
  • Business Case study - Text Mining

Module 11 - Deep Learning:

  • Neural Network
  • Business Case study -Neural Network

Module 12 - Tableau:

  • Tableau for Data Visualization
  • Models to Value
  • Pitfalls of Predictive Models in Business
  • Storytelling with Data

Module 13 - Big Data:

  • Intro to Big Data Ecosystem - Hadoop and HDFS
  • Querying with Hive
  • Intro to Spark and PySpark SQL
  • Business Case Study Data Engineering
  • Business Case Study - ML with PySpark

Module 14 - BYOP:

  • Project Presentation (BYOP)