Programme Content
Programme Contents
Module 1 Introduction to Analytics
Introduction to Analytics and CRISP DM
Data Collection and Biases
Module 2 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 3 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 4 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 5 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
Intro to Machine Learning
Tree Models Regression Trees and Classification Trees
Feature Importance
Purity Measures GINI
Purity Measures Entropy MSE
Building and Pruning Trees
Ensemble Methods Bagged
Ensembles Ensemble Methods Random Forests
Boosting
Clustering K Means and Hierarchical Models
Business Case study Machine Learning algorithms
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
Modeling 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)
Please Note: Modules topics are indicative only, and the suggested time and sequence may be dropped modifiedadapted to fit the participant profile amp; programme hours.
The curriculum of this 10 months online Future Leaders Program covers technical and business aspects of the application of Analytics Data Science. It starts by laying a strong foundation of essential tools and techniques, including Descriptive and Inferential Statistics, Data Extraction and Manipulation with SQL, Data Manipulation and processing with Python R, and Data Visualization with Tableau, Big Data and ML with Spark
Module 1: Analytics Intro and Descriptive Statistics
Module 2: R
Module 3: Inferential Statistics with Excel
Module 4: SQL(My SQL)
Module 5: Feature Engineering (R)
Module 6: GLM Predictive Statistical Modelling in R
Module 7: Python
Module 8: Machine Learning with Python
Module 9: Text Mining Introduction to NLP
Module 10: Big Data and Machine Learning with Spark
Module 11: Tableau Generating Business Value with Storytelling and Insights with data visualization
Module 12: Projects
Course Highlights
Duration: 10 Months
150 Hours divided across 12 Modules
Delivery mode: Online Live Classes
Assessments: 10 module-level quizzes assignments
Bring Your Own Project: 1
Bring Your Own Project (BYOP)
The BYOP feature will aid learners understand the application of tools and concepts taught during this 10-month Business Analytics program. They will get to work in groups, identify, shortlist, and finalize a project idea that theyll work on. Learners will be mentored throughout the various stages of BYOP by an industry SME. This ensures that participants get to apply the Analytics and Big Data techniques in their projects, including Machine Learning and Predictive Analytics so that they can easily tackle real-life business problems and provide effective solutions in their professional careers.
For further information, please contact: 91-9019987000 or ipbaiimidr.ac.in.
Please note that IIM Indore reserves the right to change the programme design, format, number of sessions, certificate format, terms in the programme or can incorporate any such change deemed necessary by the institute without prior intimation.
Duration Number of Session Hours
Duration: 10 Months
Number of Hours: Approx. 160 hours
Number of Sessions (75 Minutes each): 128 sessions
Online: sessions: 116 sessions
On Campus: 12 Sessions
On-campus orientation module of three days duration:
3 residential days at the IIM Indores Indore Campus
One or two sessions from some of the courses will become part of the on-campus orientation module. In case the on-campus module is not conducted due to Covid situation, the same will be included in the total number of sessions.
The programme duration may be slightly extended due to unavoidable situations.
