Business Analytics

Hybrid Mode | 36 Hours | Basic Certification
15,338.8217,698.82 (Inc. GST)

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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