Executive Program in Business Analytics
Witness the different overview of market and analyse the strategic business decision with IMI Delhi
Learn with Experts
Analyse your learning with industry experts
Learn Anything
On strategic and business operation activities
Flexible Learning
Access to your analysed learning, with your satisfactory schedule
Industrial Standard
Get a standardized framework for your tools
Program Overview
Comprehensive Understanding
Gain a thorough understanding of how data analytics contributes to corporate growth.
Learning
Gain managerial knowledge of structured and unstructured data mining, machine learning tools, and processes.
Department
Determine which organisational departments stand to benefit financially from the innovative use of business analytics.
Discover
Discover how to create and apply data-driven solutions that are in line with organisational objectives by learning strategic approaches.
Gap Analysis
Discover how to spot abnormalities and gaps in results from machine learning models and provide tailored business advice.
Communicate
Acquire the ability to communicate data analysis insights using narrative approaches.
Testimonials
Course Curriculum
Introduction to Business Analytics (4 hrs)
- Introduction to Business Analytics – Concepts and Tools
- Business Analytics – The need
- Why invest in Business Analytics
- Type and Scope of Analytics
- Emerging Trends in Analytics
Understanding Data and Information (2 hrs)
- Data Types and Big Data
- Marketing Analytics
- Risk Estimation Analytics
- Social Media Analytics
Introduction to Microsoft Excel for Data Analysis (2 hrs)
- Examining Data for Errors using formulas and functions
- Using graphs and charts to identify data problems using Excel
Readings
- PPTs and reading materials
- Online links
Evaluation: Quiz
Basics of Statistics and Probability (4 hrs)
- Descriptive Statistics: Central Tendency, Dispersion, Skewness, Kurtosis, Correlation Coefficients
- Probability: Overview of Sets, Types of events, Venn Diagrams
- Important laws of Probability
- Hands-on tasks of the above in Excel using a business problem
Case study
- Harvard Study: Central Parking Services Private Limited (IMB451), Abhishek Srivastav, Tanmay Gupta, Unnikrishnan Dinesh Kumar, (2013, IMB)
Readings
- Handouts, Online links & PPTs
Evaluation: Quiz
Data Cleaning and Preparation Techniques (2 hrs)
- Checking for missing values and outliers
- Handling blank cells, duplicates, highlight errors
Descriptive Analytics (2 hrs)
- Understanding descriptive analytics?
- Steps – Collection, Preparation, Exploration and Visualization
- Data Mining and Aggregation Techniques
- Techniques and Real-world use cases
Readings
- Handouts, Online links & PPTs
Evaluation: Quiz
Introduction to Python Programming: (4 hours)
- Introduction to Python
- Reading Code, Printing Comments
- User Input
- Using Interactive Help
- Variables and Naming, Numbers and functions, Strings and Text
- Basic operators
- Compound Boolean Expressions
- Conditional Statements (if/else Statement)
- Nested Conditionals
- Multi-way Decision Statements
- Multi-way Versus Sequential Conditionals
- Iteration
- While Statement
- For Statement
- Nested Loops
- Functions and Modules Basics
- The Built-in Functions
- Parameter Passing
Python Lab Assignment: Worksheets will be given and will be solved in the class. Please note Google Colab (https://colab.google/) will be used for the lab.
Readings: PPT & link of online references will be shared with the participants.
Evaluation: One Multiple Choice Quiz will be conducted to assess the understanding of Python basic concepts.
Introduction to NumPy for Numerical Computing (2 hours)
- Importing the NumPy module
- Arrays
- Creating Numpy Arrays
- Numpy Data Objects, dtype
- Array indexing and looping.
- Array mathematics.
- Universal Functions: abs( ), exp( ), sqrt( ) etc.
- Array Methods: min( ), max( ), sum( ), sort( ) std, etc
- Array item selection and manipulation.
- Concatenating, Flattening and Adding Dimensions in Arrays
- Statistical features of arrays
- Vector and matrix mathematics.
Python Lab Assignment: There will be One Assignment based on the topic discussed in this Section. Please note Google Colab (https://colab.google/) will be used for the lab.
Readings: PPT & link of online references will be shared with the participants.
Evaluation: One Multiple Choice Quiz will be conducted to assess the understanding of NumPy for Numerical Computing concepts.
Data Structures and Manipulation with Pandas (2 hrs)
- Introduction to Pandas: What is Pandas library?
- Install and import Pandas
- Definition of data structure, Overview of data structures in Pandas
- Series — 1D
- DataFrame — 2D
- Panel — 3D
- The Pandas Series
- Series creation
- Selecting elements from a Series
- Assigning values to the elements
- Operations and mathematical functions on series
- The Pandas DataFrame
- DataFrame creation
- Reading data from CSV, JSON, or SQL and converting back to a CSV, JSON, or SQL
- Selecting elements from a DataFrame
- Assigning values to the elements
- DataFrame operations
- Viewing your data
- Getting info about your data
- Understanding your variables
- Column cleanup
- Handling Missing Values
- Handling duplicates
- DataFrame slicing, selecting, extracting.
- The Pandas Panel
- Panel creation
- Selecting elements from a Panel
Python Lab Assignment: There will be One Assignment based on the topic discussed in this Section. Please note Google Colab (https://colab.google/) will be used for the lab.
Readings: PPT & link of online references will be shared with the participants.
Evaluation: One Multiple Choice Quiz will be conducted to assess the understanding of Data Structures and manipulation with Python concepts.
Advanced Data Manipulation Techniques (2 hrs)
- Filtering Data
- Merging & Joining data
- Aggregating Data
- Pivoting Data
- Creating Crosstab
- Handling Missing Data
- Transforming Data
Python Lab Assignment: There will be One Assignment based on the topic discussed in this Section. Please note Google Colab (https://colab.google/) will be used for the lab.
Readings: PPT & link of online references will be shared with the participants.
Evaluation: One Multiple Choice Quiz will be conducted to assess the understanding of Advanced Data Manipulation Techniques concepts.
Data Visualization with Matplotlib and Seaborn (2 hrs)
- What is visualization?
- Choosing an appropriate visual
- Basic Plots & Charts
- Bar chart
- Line Chart
- Scatter Plot
- Distribution Plots
- Histogram
- Boxplots
- Heatmaps
- Visualizing Correlations & Missing Values
Readings:
- Textbook: Reimagining Data Visualization Using Python by Seema Acharya, Wiley.
- Online links & PPTs
Evaluation: Quiz
Introduction to Machine Learning with Scikit-Learn (4 hrs)
- Supervised and Unsupervised Learning
- Prediction and classification methods
- k-Nearest Neighbours (kNN)
- Decision Trees
- Mining relationships among records
- Association Rules and Recommendation Systems
- Cluster analysis
Case study:
- Harvard study: Improving Lead Generation at Eureka Forbes Using Machine Learning Algorithms (IMB779-PDF-ENG), Nandini Seth, Manupriya Agrawal, Manaranjan Pradhan, Dinesh Kumar Unnikrishnan (2019, HBR)
Readings:
- Machine Learning with Python Cookbook by Chris Albon (Published by O’Reilly)
- Online links, PPTs, Handout
Evaluation: Subjective question paper for one hour
Mathematics for Analytics (Linear Algebra, Calculus): (6 hrs)
Linear Algebra:
- Matrices: Index, Types, Transpose, Addition, Multiplication (scalar, vector), Determinant, Inverse
- Solving System of Equations
- Eigenvalues and Eigenvectors
- Hands-on tasks of the above in Excel using a business problem
Calculus:
- Basic functions and their types and applications
- Differentiation: product rule, quotient rule, chain rule
- Integration: Techniques and common integrals
- Partial Differentiation
- Hands-on tasks of the above in Excel using a business problem
Readings:
- Handout, Online links & PPTs
Evaluation: Quiz
Probability Distributions (4 hrs)
- Recap of Basics of Probability
- Random variables, rules of expectations
- Joint, marginal, and conditional probability distributions
- Discrete distributions: Binomial, Poisson
- Continuous Distribution: Normal
- Hands-on tasks of the above in Excel using a business problem
Case study:
- Harvard study: Probability Distributions (621704), Michael Parzen, Paul J. Hamilton (2021, HBR)
- Harvard study: Histograms and the Normal Distribution in Microsoft Excel (W16413), Kyle Maclean, Lauren E. Cipriano, Gregory S. Zaric, (2016, Ivey)
Readings:
- Textbook: Aczel Amir D and Sounderpandian J Complete Business Statistics, Tata McGraw Hill (7th edition, 2012).
- Online links, PPTs, Handout
Evaluation: Quiz
Inferential Statistics (4 hrs)
- Population, Sample, Parameter, Statistic, Sample Survey
- Sampling techniques: probabilistic, non-probabilistic
- Sampling distribution of sample statistic, mean, proportion, variance
- Degrees of freedom
- Point and Interval Estimation
- Sample size determination
- Estimation properties
- Hands-on tasks of the above in Excel using a business problem
Case study:
Harvard Study: Sampling and Statistical Inference (191092), Arthur Schleifer Jr.(1990, HBR)
Readings:
- Textbook: Aczel Amir D and Sounderpandian J Complete Business Statistics, Tata McGraw Hill (7th edition, 2012).
- Online links, PPTs, Handout
Evaluation: Quiz
Hypothesis Testing (4 hrs)
- Define Null and Alternate Hypotheses
- Types of errors in decision making process and their significance in business decisions (Type -I and Type-II errors)
- Mean, proportion, and variance tests (Z test, t-test, Chi-square-test, F-test)
- Chi-square Contingency test
- Hands-on tasks of the above in Excel using a business problem
Case study:
- The repercussions of Type-I error beyond Statistics: Kakali Kanjilal (October 2022), IMI Insights BlogPost
- Harvard study: Testing Marketing Hypotheses at WSES (IMB693), Dinesh Kumar Unni Krishnan (2018, IIMB)
Readings:
- Textbook: Aczel Amir D and Sounderpandian J Complete Business Statistics, Tata McGraw Hill (7th edition, 2012).
- Online links, PPTs, Handout
Evaluation: Take home assignment
Regression Analysis (4 hrs)
- Linear regression analysis
- Regression diagnostics
- Logistic regression analysis
- Identifying gaps & anomalies
- Hands-on tasks of the above in Python using a business problem
Case study:
- Harvard study: Women and Children First on the Titanic (W13259), Chris A. Higgins, Crystal Ji (2013, Ivey)
Readings:
- Textbook: Machine Learning with Python Cookbook by Chris Albon (Published by O’Reilly)
- Online links & PPTs
Evaluation: Take home assignment
Time Series Analysis (2 hrs)
- Time series decomposition
- Time trend analysis
- Exponential smoothing
- ARIMA forecasting
- Hands-on tasks of the above in Python using business problems
Case study:
- Harvard study: City of London Water: Predicting Electricity Prices and Optimizing Operations (W19122), Ernesto Arandia, Joe Naoum-Sawaya, Kira Wembo Xue (2019, Ivey)
Readings:
- Online links, PPTs, and research articles.
Evaluation: Take home assignment
Advanced Formulas and Functions (4 hrs)
- Introduction to Referencing, Formula and Functions
- Logical Functions
- Lookup Functions
- Financial Functions
Data Analysis Tools in Excel (2 hrs)
- Descriptive Statistics
Data Visualization Pivot Tables and Pivot Charts (4 hrs)
- Multi-dimensional Analysis of Data
- Creating dashboard using Pivot Tables and Charts
Data Modeling and Forecasting Techniques (2 hrs)
- Introduction to Data Schema using Power Pivot
- Moving average method of forecasting
- Linear regression method of forecasting
What-If Analysis and Scenario Building (2 hrs)
- Goal-Seek
- Data Tables
- Scenario Manager
What-If Analysis and Scenario Building (2 hrs)
- Goal-Seek
- Data Tables
- Scenario Manager
Excel Automation with Macro (2 hrs)
- Recording macros
- Modifying and applying macros
Readings:
- Text book and Session Handouts
- Online links
Evaluation: Quiz and Take Home Assignment
Introduction to Power BI (2 hrs)
- Power BI – What and Why?
- Installation and understanding the interface
Data Importing and Transformation (2 hrs)
- Type of data
- Data pre-processing
Data Modeling and Relationships (4 hrs)
- Introduction to Joins
- Introduction to cardinalities
Introduction to DAX (Data Analysis Expressions) (4 hrs)
- Handling the queries
- Advanced DAX Functions for Calculated Columns and Measures
Dashboard Design and Interactive Visualization Techniques (4 hrs)
- Developing interactive dashboards
Readings:
- Text book and Session Handouts
- Online links
Evaluation:
- Quiz and Take Home Assignment
- Real-world Business Analytics Project
- Problem Definition and Data Acquisition
- Data Exploration and Analysis
- Interpretation and Presentation of Findings
- Implementation of Solutions - Project Presentation and Evaluation
The capstone project serves as a culmination of the learning outcomes from the Business Analytics certification program. Participants are tasked with selecting a real-world business problem and applying the tools and techniques acquired during the program to develop solutions. The recommendations derived from the analysis should be practical and actionable, providing tangible value to organizations. The capstone project will be evaluated by industry practitioners and instructors. Detailed guidelines for the capstone project are provided below:
Problem statement: Participants are required to select a real-world business problem as the focus of their capstone project.
Data acquisition: Utilize open-source data repositories such as Kaggle, Github, UCI, etc., or participants may opt to use their own datasets. Guidelines for dataset selection should be followed, ensuring relevance and suitability for the chosen problem.
Data pre-processing and exploration: After approval from instructors regarding the selected business problem and datasets, participants start data preparation using tools such as DAX and other relevant techniques. Thorough exploration of the data is encouraged to understand its characteristics and potential insights.
Analysis & Interpretation of findings: Participants should employ machine learning tools learned during the program to analyze the business goals. Findings should be interpreted with respect to the defined business objectives. Additionally, participants should diligently identify and address any gaps or anomalies in the results before formulating recommendations. Recommendations must be practical and actionable, aligning with the objectives of the analysis.
Presentation: During the presentation phase, participants are required to showcase their end-to-end ownership of the identified business problem and communicate the outcomes of the analysis in a storytelling format.
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Faculty & Mentors
Kakali Kanjilal
Dr. Kakali Kanjilal is a Professor of Operations Management and Quantitative Techniques at IMI New Delhi. With a Ph.D. in macroeconomics and finance, she has 23 years of experience in industry, research, and teaching. She founded and led the Risk Authorization & Customer Management Analytics Team at American Express India. Her research interests include data analytics, econometrics, and credit risk management. Dr. Kanjilal teaches courses in Business Analytics and Predictive Analytics and is the Editor of Global Business Review.
