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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery	Frequency Of Classes
English	Self Study, Virtual Classroom, Campus Based/Physical Classroom	Video and Text Based	Weekends

Course Overview

The Advanced Certification in Data Analytics for Business Certification Course is a specialized course provided by the faculty of IIT Madras exploring the core aspects of data architecture and business analytics. The course led by industrial experts aims to cover the fundamental features of data science and data analytics for business.

The Advanced Certification in Data Analytics for Business Live Course is taught over 7 months in a self-paced manner. The course will be handled by experienced mentors and academicians from the Indian Institute of Technology Madras in partnership with the specialists at Intellipaat. The course is particularly designed for both working professionals and freshers.

The Advanced Certification in Data Analytics for Business Training Course is delivered through live online videos, assignments, real-time industry projects, and case studies. Learners will also receive career support from the team at Intellipaat.

The Highlights

Certificate of completion
7-months course
Created for working professionals and freshers
400 hours of applied learning
218 hours of self-paced learning
500+ hiring partners
Curriculum by industrial experts
One-on-one sessions with industry mentors
50+ live online videos
Assignments and real-time projects
50+ industry projects and case studies
Career services and guaranteed 3 interviews by Intellipaat
24*7 Support and soft skills essential training
Learn from IIT Madras Faculty & Industry Practitioners
NIRF Ranking 1
IIT Madras Digital Skills Academy Alumni Status
No cost EMI option

Important dates

Other

Start Date : 19 Jan, 2025

Other

Start Date : 19 Jan, 2025

Programme Offerings

Certificate of completion
1 Year Duration
Self-paced learning
live online sessions
assignments
Real-time Projects
Case Studies
Job Interviews
Soft skills training
One-on-one Mentor Sessions.

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	IIT Madras (IITM)

Advanced Certification in Data Analytics for Business Course Fee Structure -

Fees	Amount
Total Admission Fee	₹ 85,044 (Inclusive of All)
EMI Starts at	₹ 5,500

What you will learn

Knowledge of Data VisualizationKnowledge of PythonSQL knowledgeDatabase knowledgeSupply ManagementMachine learningStatistical skillsKnowledge of Algorithms

After completing the Advanced Certification in Data Analytics for Business Classes, you will gain insights into the following topics:

Data analysis and data transformation
Basics of Python
SQL functions
Data transformation using Presto
Statistics and probability
Business problem solving
Data storytelling
Introduction to KNIME
Machine learning with R
Time series forecasting
Machine learning algorithms
Data science strategies
Logistics and supply chain management
Financial analytics and social analytics
Data modelling and data visualization

Who it is for

Data ScientistBusiness AnalystProject Manager

The Advanced Certification in Data Analytics for Business course is suitable for anyone with a bachelor’s degree and an interest in learning data science and business analytics.
The course can be opted by developers, project managers, professionals in data science who aim at developing their domain skills.
The course can be taken up by IT professionals who want a change in career to data scientists and business analysts along with professionals who wish to boost their IT career.
The course is suitable for freshers who dream of establishing their careers in business analytics and data science.

Admission Details

Follow the steps given below to enroll in the Advanced Certification in Data Analytics for Business Training Course:

Step 1: Click on the URL given below - https://intellipaat.com/data-science-business-analytics-iit-madras/

Step 2: Click on the “Apply Now” option on the page.

Step 3: Fill in your name, email id, mobile number and apply.

The Syllabus

Introduction to SQL
Database Normalization and Entity Relationship Model(self-paced)
SQL Operators
Working with SQL: Join, Tables, and Variables
Deep Dive into SQL Functions
Working with Subqueries
SQL Views, Functions, and Stored Procedures
Deep Dive into User-defined Functions
SQL Optimization and Performance
Advanced Topics
Managing Database Concurrency
Practice Session

Introduction to Python and IDEs – The basics of the Python programming language, and how you can use various IDEs for Python development like Jupyter, Pycharm, etc.
Python Basics – Variables, Data Types, Loops, Conditional Statements, functions, decorators, lambda functions, file handling, exception handling ,etc.
Object Oriented Programming – Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.
Data Manipulation with Numpy, Pandas, and Visualization – Using large datasets, you will learn about various techniques and processes that will convert raw unstructured data into actionable insights for further computations i.e. machine learning models, etc.

Descriptive Statistics

The measure of central tendency, the measure of spread, five points summary, etc.

Probability

Probability Distributions, Probability in Business Analytics
Probability Distributions, Binomial distribution, Poisson distribution, Bayes’ Theorem, central limit theorem

Inferential Statistics

Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.

Introduction to Machine Learning

Supervised, Unsupervised learning.
Introduction to scikit-learn, Keras, etc

Regression

Introduction classification problems, Identification of a regression problem, dependent and independent variables
How to train the model in a regression problem?
How to evaluate the model for a regression problem?
How to optimize the efficiency of the regression model?

Classification

Introduction to classification problems, Identification of a classification problem, dependent and independent variables
How to train the model in a classification problem?
How to evaluate the model for a classification problem?
How to optimize the efficiency of the classification model?

Clustering

Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables
How to train the model in a clustering problem?
How to evaluate the model for a clustering problem?
How to optimize the efficiency of the clustering model?

Supervised Learning

Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc.
Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc.

Unsupervised Learning

K-means – The K-means an algorithm that can be used for clustering problems in an unsupervised learning approach
Dimensionality reduction – Handling multi dimensional data and standardizing the features for easier computation

Performance Metrics

Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc.
Confusion matrix – To evaluate the true positive/negative, and false positive/negative outcomes in the model.
r2, adjusted r2, mean squared error, etc.

Time Series Forecasting

Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting

Business Domains – Learn about various business domains and understand how one differs from the other.
- Finance
- Marketing
- Retail
- Supply Chain
Understanding the business problems and formulating hypotheses – Learn about formulating hypotheses for various business problems on samples and populations.
Exploratory Data Analysis to Gather Insights – Learn about exploratory data analysis and how it enables a foolproof producer of actionable insights.
Data Storytelling: Narrate stories in a memorable way – Learn to narrate business problems and solutions in a simple relatable format that makes it easier to understand and recall.

Introduction to KNIME – Learn about the KNIME tool that can be quite efficient for data analytics, creating workflows, etc.
Working with data in KNIME – Learn about creating workflows, loading datasets in KNIME, etc.
Loops in KNIME – Learn about the loops in KNIME that enable efficient data transformation in KNIME
Web scraping in KNIME – Learn about techniques in KNIME that enable web scraping to collect data directly from the web.
Feature Selection, Hyperparameter optimization in KNIME – Learn about hyperparameter optimization, and feature selection in KNIME that will enable efficient machine learning models

Feature Selection – Feature selection techniques in Python that include recursive feature elimination, Recursive feature elimination using cross-validation, variance threshold, etc.
Feature Engineering – Feature engineering techniques that help in reducing the best features to use for data modeling.
Model Tuning – Optimization techniques like hyperparameter tuning to increases the efficiency of the machine learning models.

Introduction to Spark – Introduction to Spark, Spark overcomes the drawbacks of working on MapReduce, Understanding in-memory MapReduce, Interactive operations on MapReduce, Spark stack, fine vs. coarse-grained update, Spark Hadoop YARN, HDFS Revision, and YARN Revision, The overview of Spark and how it is better than Hadoop, Deploying Spark without Hadoop, Spark history server and Cloudera distribution
Spark Basics – Spark installation guide, Spark configuration, Memory management, Executor memory vs. driver memory, Working with Spark Shell, The concept of resilient distributed datasets (RDD), Learning to do functional programming in Spark, and architecture of Spark.

Spark SQL and Data Frames

Learning about Spark SQL
The context of SQL in Spark for providing structured data processing
JSON support in Spark SQL
Working with XML data
Parquet files
Creating Hive context
Writing data frame to Hive
Reading JDBC files
Understanding the data frames in Spark
Creating Data Frames
Manual inferring of schema
Working with CSV files
Reading JDBC tables
Data frame to JDBC
User-defined functions in Spark SQL
Shared variables and accumulators
Learning to query and transform data in data frames
Data frame provides the benefit of both Spark RDD and Spark SQL
Deploying Hive on Spark as the execution engine

Problem Statement and Project Objectives – You will learn how to formulate various problem statements and understand the business objective of any problem statement that comes as a requirement.
Approach for the Solution – Creating various statistical insights-based solutions to approach the problem will guide your learnings to finish a project from scratch.
Optimum Solutions – Formulating actionable insights backed by statistical evidence will help you find the most effective solution for your problem statements.
Evaluation Metrics – You will be able to apply various evaluation metrics to your project/solution. It will validate your approach and point towards shortcomings backed by insights, if any.
Gathering Actionable insights – You will learn about how a problem’s solution isn’t just creating a machine learning model, the insights that were gained from your analysis should be presentable in the form of actionable insights to capitalize on the solutions formulated for the problem statement.

Customer Churn – The case study involves studying the customer data for a given XYZ company, and using statistical tests and predictive modeling, we will gather insights to efficiently create an action plan for the same.
Sales Forecasting – By studying the various patterns and sales data for a firm/store, we will use the time series forecasting method to forecast the number of sales for the next time period(weeks, months, years, etc.)
Census – After studying the population data, we will gather insights and through predictive modeling try to create actionable insights on the same, it could be the average income of an individual, or most likely profession, etc.
Predictive Modeling – Various case studies on categorical and continuous data, to create predictive models that will predict specific outcomes based on the business problems.
HR Analytics – Based on the data provided by a firm, we will study the HR analytics data, and create actionable insights using various statistical tests and hypothesis testing.
Dimensionality Reduction – To understand the impact of multidimensional data, we will go through various dimensionality reduction techniques and optimize the computational time on the same that will eventually be used for various classification and regression problems.
Housing – A case study that will give you insight into how real estate firms can narrow down on pricing, customer choices, etc. using various predictive modeling techniques.
Customer Segmentation – Using unsupervised learning techniques, we will learn about customer segmentation, which can be quite useful for e-commerce sectors, stores, marketing funnels, etc.
Inventory Management – In this case study, you will learn about how meaningful insights can be used to drive a supply chain, using predictive modeling and clustering techniques.
Disease Prediction – A medical endeavor that is achieved through machine learning will give you an insight into how the predictive model can prove to be a great marvel in the early detection of various diseases.

Module 11: Microsoft Excel

Excel Fundamentals
- Reading the Data, Referencing in formulas , Name Range, and Logical
- Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering
- Working with Charts in Excel, Pivot Table, Dashboards, Data, and File Security
- VBA Macros, Ranges, and Worksheets in VBA
- IF conditions, loops, Debugging, etc.
Excel For Data Analytics
- Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc.
Data Visualization with Excel
- Charts, Pie charts, Scatter, and bubble charts
- Bar charts, Column charts, Line charts, Maps
- Multiples: A set of charts with the same axes, Matrices, Cards, Tiles
Ensuring Data and File Security
- Data and file security in Excel, protecting row, column, and cell, the different safeguarding techniques.
Getting Started with VBA Macros
- Learning about VBA macros in Excel, executing macros in Excel, the macro shortcuts, applications, the concept of relative reference in macros, In-depth understanding of Visual Basic for Applications, the VBA Editor, module insertion and deletion, performing an action with Sub and ending Sub if condition not met.
Statistics with Excel
- ONE-TAILED TEST AND TWO-TAILED T-TEST, LINEAR REGRESSION,PERFORMING STATISTICAL ANALYSIS USING EXCEL, IMPLEMENTING LINEAR REGRESSION WITH EXCEL

Module 12: Data Warehousing

Introduction to Data Warehouse – Introducing Data Warehouse and Business Intelligence, understanding the difference between database and data warehouse, working with ETL tools, and SQL parsing.
Architecture of Data Warehouse – Understanding the Data Warehousing Architecture, system used for Reporting and Business Intelligence, understanding OLAP vs. OLTP, and introduction to Cubes.
Data Modeling Concepts – The various stages from Conceptual Model, Logical Model to Physical Schema, Understanding the Cubes, benefits of Cube, working with OLAP multidimensional Cube, creating Report using a Cube.
Data Normalization – Understanding the process of Data Normalization, rules of normalization for first, second, and third normal, BCNF, deploying Erwin for generating SQL scripts.
Dimension and Fact Table – The main components of Business Intelligence – Dimensions and Fact Tables, understanding the difference between Fact Tables & Dimensions, and understanding Slowly Changing Dimensions in Data Warehousing.
SQL Parsing, Cubes, and OLAP – SQL parsing, compilation and optimization, understanding types and scope of cubes, Data Warehousing Vs. Cubes, limitations of Cubes and evolution of in-memory analytics.

Module 13: Visualizations using PowerBI

Power BI Basics
- Introduction to PowerBI, Use cases and BI Tools, Data Warehousing, Power BI components, Power BI Desktop, workflows and reports, and Data Extraction with Power BI.
- SaaS Connectors, Working with Azure SQL database, Python, and R with Power BI
- Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data, M Query, and Hierarchies in Power BI.
DAX
- Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features
Data Visualization with Analytics
- Slicers, filters, Drill Down Reports
- Power BI Query, Q & A and Data Insights
- Power BI Settings, Administration and Direct Connectivity
- Embedded Power BI API and Power BI Mobile
- Power BI Advance and Power BI Premium

Job Readiness

Job Search Strategy
Resume Building
Linkedin Profile Creation
Interview Preparation Sessions by Industry Experts
Mock Interviews
Placement opportunities with 400+ hiring partners upon clearing the Placement Readiness Test.

Module 14: R Programming

Introduction to R
R Packages
Sorting DataFrame
Matrices and Vectors
Reading Data from External Files
Generating Plots
Analysis of Variance (ANOVA)
K-Means Clustering
Association Rule Mining
Regression in R
Analyzing Relationship with Regression
Advanced Regression
Logistic Regression
Advanced Logistic Regression
Receiver Operating Characteristic (ROC)
Kolmogorov–Smirnov Chart
Database Connectivity with R
Integrating R with Hadoop
R Case Studies

Instructors

IIT Madras (IITM) Frequently Asked Questions (FAQ's)

1: How long does it take for application review?

The application for the Advanced Certification in Data Analytics for Business Live Course will be reviewed and candidates will be notified within 1 to 2 weeks.

2: Will I get a certificate after Advanced Certification in Data Analytics for Business course completion?

After completing the Advanced Certification in Data Analytics for Business Training course you will receive an Advanced Certification in Data Science and Business Analytics from Intellipaat and IIT Madras.

3: What is the total duration of the Advanced Certification in Data Analytics for Business Online Course?

The Advanced Certification in Data Analytics for Business Course has a duration of 7 months.

4: What is the solution if I fail to attend a live lecture?

If you fail to attend one or more live lectures of the Advanced Certification in Data Analytics for Business Course, you can get access to its recorded version within 12 hours.

5: Which university is offering the Advanced Certification in Data Analytics for Business Live Course?

The Advanced Certification in Data Analytics for Business course is offered by the expert faculty of IIT Madras on the Intellipaat learning platform.