Post Graduate Certificate in Data Science and Business Analytics with Generative AI

• An introduction to linear algebra

• Basic probability and statistics

• Descriptive statistics and distributions

• Hands-on exercises with descriptive statistics in R

• Working with real-world data to calculate mean, median, and variance

• An introduction to R and RStudio

• Basic data manipulation in R (dplyr, tidyr)

• Data visualisation in R (ggplot2)

• Hands-on data manipulation and visualisation in R

• Data summarisation and plotting using real datasets in R

An introduction to data cleaning

Handling missing data

• Detecting and managing outliers

• Hands-on data cleaning with Python or R

• Working with messy data: cleaning, transforming, and preparing for analysis

• Installation of MYSQL basic terms: database, tables, records, columns, and data types in MYSQL

• Types of commands in MYSQL: DDL, DML, TCL, DCL, and DQL

• Types of Constraints in MYSQL

• Creating tables using the constraints to maintain data integrity

• Different types of joins in MYSQL

• Combining multiple tables using various joins

• Various aggregation functions: Sum, Max, Min, Average, and Count

• Aggregation functions with commands such as where, group by, and having

• Focus on the interface of Power BI Desktop and importing data in Power BI

• Data visualisation using various visuals, such as line chart, bar chart, pie chart, map chart, table, card, and slicer

• Removing null values, duplicate records, error from data, and changing data types if required

• Merging and appending data from various sources

• Difference between Calculated Column and Measure; various DAX Formulas

• DAX functions and data modelling

• Creating roles in Power BI Desktop to maintain row-level security

• Publishing the report on Power BI services, and assigning access to different people as per roles

• Essentials of story telling

• Presenting report by using the art of story telling

• The installation of Python notebook and basics of Python

• Datatypes, typecasting, expressions and variables, and string operations

• Loading data with open and writing data with open

• Handling data in Python using Pandas and doing transformation

• An introduction to Python for data science

• An overview of NumPy

• An introduction to Pandas

• Hands-on exercises with NumPy and Pandas

• Data manipulation exercises

• Working with arrays, series, and DataFrames

• Advanced Pandas operations

• An introduction to Matplotlib and Seaborn for data visualisation

• Data visualisation with Matplotlib and Seaborn

• Advanced Pandas use cases

• An introduction to EDA

• Handling missing data

• Outliers detection

• Data summarisation techniques

• EDA on a real dataset

• Visualising distributions and correlations

• Creating summary statistics using Python

• Probability distributions

• Normal distribution

• Binomial distribution

• Other common distributions (Poisson, Exponential, etc.)

• Hypothesis testing basics

• Null hypothesis vs. Alternative hypothesis

• P-value and statistical significance

• Type I and Type II errors

• Confidence intervals

• Constructing and interpreting confidence intervals

• Margin of error

• Statistical tests

• Z-test and T-test

• ANOVA (Analysis of Variance)

• One-way and Two-way ANOVA

• Chi-Squared Test

• Chi-Squared Test for Independence

• Chi-Squared Test for Goodness of Fit

• Correlation vs. Causation

• Understanding Correlation

• Differentiating between Correlation and Causation

• Conducting Hypothesis Testing in Python

• Implementing tests using Python libraries (e.g., SciPy and Statsmodels)

• Practical examples with real data

• Statistical tests and case studies

• Applying various statistical tests

• Interpreting results and drawing conclusions

• Working on case studies requiring inferential statistics

• An introduction to predictive modeling

• Steps in building predictive models

• An introduction to regression models (Linear, Logistic, and more)

• Implementing regression models in Python

• Evaluating model performance (R-squared, MSE, etc.)

• Introduction to Machine Learning

• Supervised vs Unsupervised learning

• An overview of algorithms (KNN, Linear Regression)

• Building simple ML models using Scikit-Learn

• Data preparation and feature selection for ML models

• Linear Regression

• Decision Trees

• Overfitting and regularization

• Implementing Linear Regression and Decision Trees

• Evaluating model performance using cross-validation and metrics such MAE and RMSE

• Support Vector Machines (SVM)

• Random Forest

• Hands-on with SVM and Random Forest models

• Introduction to clustering (K-means, Hierarchical)

• Dimensionality Reduction (PCA)

• Applications of unsupervised learning

• Implementing clustering algorithms in Python

• Visualising clusters and evaluating cluster quality

• GridSearch and RandomisedSearch

• Model persistence with joblib and pickle

• Model optimisation techniques

• Hyperparameter tuning with Python (using Scikit-Learn)

• Saving and loading models

• Working on performance improvements with tuning

• Time series components (trend, seasonality, etc.)

• ARIMA, SARIMA, and other forecasting methods

• Stationarity in time series

• Implementing ARIMA and SARIMA models in Python

• Working with time series data (real-world datasets)

• Evaluating forecast accuracy

• An introduction to ensemble methods

• Bagging, boosting, and stacking

• Introduction to XGBoost

• Working of XGBoost

• Implementing ensemble models (Bagging, Boosting)

• Case study on ensemble model performance

• Introduction to association rules

• Apriori algorithm

• Applications of association rules in market basket analysis

• Implementing the Apriori algorithm in Python

• Hands-on project for mining association rules using a dataset (e.g., transactional data)

• Introduction to Supply Chain Analytics

• Key metrics (Lead Time, Inventory Turnover, etc.)

• Demand Forecasting

• Working with supply chain datasets

• Implementing demand forecasting models (e.g., time series)

• Introduction to Marketing Analytics

• Customer Segmentation

• Customer Lifetime Value (CLV) Analysis

• Segmentation techniques using clustering

• Hands-on with Customer Lifetime Value models

• Introduction to Finance Analytics

• Case studies on financial data analysis

• Risk assessment using historical data and financial ratios

• Risk Analysis Overview

• Financial Metrics and Ratios (ROI, NPV, etc.)

Specialise in one of 2 domains:

• Generative AI for Data Science

• Analytics for Business

• A 2-week project

Learn advanced technical concepts and practical applications through live masterclasses conducted by top IIT Guwahati faculty. 

• Embedding Models & Agent Memory

• Vector Search & Chunking Strategies

• Advanced RAG Architectures & Tuning

• Learning & Adaptation (Reinforcement Learning, Human Feedback)

• Deployment Options (Cloud, Serverless, Embedded)

• Monitoring & Observability (LangSmith)

• Responsible Agentic AI (Risks, Bias, Privacy, Safety Layers)

• Industry Case Studies & Future Trends

• Interactive Design Exercise: Architect Your Own Agent

• Module 1: Python Basics

• Module 2: Python Data Structures

• Module 3: Python Programming Fundamentals

• Module 4: Working with Data in Python

• Module 5: Working with NumPy Arrays and Simple APIs

• Module 1: An Introduction to Prompt Engineering

• Module 2: Getting Started with Prompt Engineering

• Module 3: The Chain-of-Thought Approach

• Module 4: Advanced Techniques

• Module 5: Final Project

• Module 1: Web Analytics

• Module 2: Retail Analytics

• Module 3: Real-World Case Studies in Data Science

The data science course eligibility requires candidates to have a bachelor’s degree in any discipline. While prior knowledge of programming or statistics is beneficial, it is not mandatory. Professionals from IT, engineering, business, and finance backgrounds can apply.

The data science course duration is 10 months. The data science course fees are ₹1,32,000+GST, covering tuition, course materials, and mentorship from IIT Guwahati faculty. The programme provides a cost-effective alternative to an online data science master’s degree.

The key skills required to be a data scientist include expertise in Python for data science, machine learning, big data analytics, and generative AI. The programme equips learners with hands-on experience in AI-driven decision-making and business analytics.

Yes, this programme provides hands-on learning through live sessions, case studies, real-world projects, and practical assignments. The programme gives you access to 20+ most-in demand data science tools, which will be taught in cutting-edge virtual integrated labs. The programme is an excellent choice for those looking for a data science and analytics course with industry applications.

While the programme does not guarantee placements, it offers career support services such as resume-building workshops, industry mentorship, and job referrals, making it one of the best data science courses for professionals seeking career advancement.

Graduates can pursue roles such as Data Scientist, Business Analyst, AI Specialist, Data Engineer, and more. This certification programme is ideal for professionals aiming to enhance their qualifications through a data science and AI course.

While an online data science master’s degree is broader in scope, this Advanced Certification Programme in Data Science and Business Analytics with Gen AI is a time-efficient and industry-focused alternative, making it a strong choice for working professionals.

This programme is one of the best online data science courses, combining the expertise of E&ICT Academy, IIT Guwahati with the latest advancements in data science and generative AI. With hands-on training, expert faculty, and a prestigious certification, it is an ideal programme for anyone looking to advance in AI and data science.