Business Analytics

Hybrid Mode | 36 Hours | Basic Certification
₹15,338.82 – ₹17,698.82 (Inc. GST)

Centre for Summer Training

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SKU: N/A Categories: Data Analytics, Programming, Summer Training Program

This Business Analytics course is designed to equip engineering, management, and data-curious students with practical analytical skills to derive insights from data and apply machine learning in real business scenarios. This program combines core statistics, predictive modeling, and hands-on projects using tools like Excel and Python. The course does not require any prior coding knowledge and is beginner-friendly.

Course Details:

Course Type : Hybrid (Online + On-campus)
Duration : 36 hours (spread over 4–5 weeks)
Skill Level : Beginner
Prerequisites: None
Certificate : Yes

Outcome Expected:

Understand and apply core statistical and analytical concepts in real-world business contexts.
Perform data cleaning, visualization, and exploratory data analysis using Excel or Python.
Build predictive models using regression, classification, and clustering techniques.
Apply basic Natural Language Processing (NLP) for analyzing customer feedback and text data.
Complete a capstone project demonstrating end-to-end analytics with business interpretation.

Target Audience:

Undergraduate/Postgraduate Engineering students
MBA and Management students
Aspiring Data Analysts or Business Intelligence professionals
Open to all backgrounds – no coding knowledge required

Key Features:

Campus Immersion Program (CIP)*
Hands-on Mini Projects & Final Capstone
Certificate from E&ICT Academy – IIT Kanpur
Mentorship & Peer Learning

Note: An additional fee of ₹750 per day will apply for student accommodation during the 3-day immersion.

Additional information

Centre for Summer Training	Hybrid Mode (Online Live + CIP), Online Live

Curriculum

Module 1: Introduction to Analytics & Business Relevance (2 hrs)

Why Predictive Analytics matters in today’s data-driven world
Real business cases
Importance of EDA and data preparation

Module 2: Exploratory Data Analysis & Statistical Foundations (6 hrs)

Descriptive statistics (mean, median, mode, variance)
Data cleaning techniques
Data visualization using Python or Excel
Statistical inference and hypothesis testing for diagnostics

Module 3: Regression Models for Prediction (6 hrs)

Linear regression: assumptions, diagnostics
Multiple linear regression
Use cases
Hands-on mini-project

Module 4: Classification Models & Business Use Cases (7 hrs)

Logistic regression (binary & multiple)
Classification trees
Naïve Bayes
Business case: Customer churn or loan approval

Module 5: Machine Learning Techniques in Business (7 hrs)

Support Vector Machines (basic intro)
Clustering: K-Means and business applications
Classification vs regression problems
Evaluation metrics (accuracy, precision, recall, etc.)

Module 6: Applied Text Analytics & Capstone (6 hrs)

Introduction to Text Analytics
Simple NLP for customer feedback, reviews
Peer + mentor feedback session

Module 7: Applications of AI in Industry (6 hrs)

Use cases
Image Manipulation
Final group project (Predictive model + business interpretation)

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Instructor Profile

Name: Prof. Saurabh Agarwal

About:

He holds both undergraduate and postgraduate degrees from IIT Kanpur and IIT Delhi. Currently affiliated with HBTU Kanpur, Great Lakes, AIMA New Delhi, and NMIMS Mumbai, he serves as a consultant for IBM and UNESCO in Business Analytics. He has published a paper on integrating alternate payment channels, presented at The Fourth International Conference on Payment Channels organized by Banknet India, Mumbai. His interests include Data Mining, Data Analysis, Business Analytics, Structural Equation Modeling, and Multivariate Analysis, utilizing platforms like R, Python, SPSS, Amos, Modeler, and IBM Watson Analytics. He has conducted international workshops for organizations such as Saudi Aramco, Kiatanin Bank, and the Ministry of Road Transport in Abu Dhabi, along with prominent domestic trainings at AIMA, Symbiosis University, and IIT Kharagpur, among others.

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