Masters in Data Science, Artificial Intelligence & Business Analytics

Python - Machine Learning - NLP - Deep Learning - Big Data Technologies - Tableau - R - Power BI - Azure

Become the First Generation Leader of the Data Science, Artificial Intelligence & Business Analytics Revolution.

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9 Months

Recommended 7-8 Hrs/Week

June 20, 2021

Start Date



Build a foundation for the most in-demand programming language of the 21st century.

  • Understanding the Jupyter Anconda,Coding Console
  • Data Structures in Python
  • Control Structure and Functions

Learn how to manipulate datasets in Python using Pandas which is the most powerful library for data preparation and analysis.

  • Introduction to NumPy
  • Operations on NumPy Arrays
  • Introduction to Pandas Getting and Cleaning Data

Humans are visual learners and hence no task related to data is complete without visualisation. Learn to plot and interpret various graphs in Python and observe how they make data analysis and drawing insights easier.

  • Introduction to Data Visualization
  • Basics of Visualization: Plots, Subplots and their Functionalities Plotting Data Distributions Plotting Categorical and Time-Series Data

Learn how to find and analyze the patterns in the data to draw actionable insights.

  • Data Sourcing
  • Data Cleaning
  • Univariate Analysis
  • Segmented Univariate
  • Bivariate Analysis
  • Derived Metrics
R Programming:

a) Reading and Getting Data into R

b) Data Objects-Data Types & Data Structure.

c) Viewing Named Objects, Structure of Data Items, Manipulating and Processing Data in R (Creating, Accessing, Sorting data frames, Extracting, Combining, Merging, reshaping data frames)

d) Control Structures, Functions in R (numeric, character, statistical)

e) working with objects, Viewing Objects within Objects, Constructing Data Objects, Packages – Tidyverse, Dplyr, Tidyr etc., Queuing Theory,

f) Non parametric Tests- ANOVA, chi-Square, t-Test, U-Test

g) Interactive reporting with R markdown


Learners will fill in the shoes of an analyst at an investment bank and determine where the firm should invest. They will then have to explain their recommendations in lieu of the analysis conducted

  • Problem Statement
  • Evaluation Rubric
  • Final Submission
  • Solution

Build a strong statistical foundation and learn how to ‘infer’ insights from a huge population using a small sample.

  • Basics of Probability
  • Discrete Probability Distributions
  • Continuous Probability Distributions
  • Central Limit Theorem

Understand how to formulate and validate hypotheses for a population to solve real-life business problems.

  • Concepts of Hypothesis Testing - I: Null and Alternate Hypothesis, Making a Decision, and Critical Value Method.
  • Concepts of Hypothesis Testing - II: p-Value Method and Types of Errors
  • Industry Demonstration of Hypothesis Testing: Two-Sample Mean and Proportion Test, A/B Testing.

Venture into the machine learning community by learning how one variable can be predicted using several other variables through a housing dataset where you will predict the prices of houses based on various factors.

  • Introduction to Simple Linear Regression
  • Simple Linear Regression in Python
  • Multiple Linear Regression
  • Multiple Linear Regression in Python
  • Industry Relevance of Linear Regression

Learn your first binary classification technique by determining which customers of a telecom operator are likely to churn versus who are not to help the business retain customers.

  • Univariate Logistic Regression
  • Multivariate Logistic Regression - Model Building
  • Multivariate Logistic Regression - Model Evaluation
  • Logistic Regression - Industry Applications

Understand the basic building blocks of Naive Bayes and learn how to build an SMS Spam Ham Classifier using Naive Bayes technique.

  • Bayes Theorem and Its Building Blocks
  • Naive Bayes For Categorical Data
  • Naive Bayes for Text Classification

Learn the pros and cons of simple and complex models and the different methods for quantifying model complexity, along with regularisation and cross validation.

  • Principles of Model Selection
  • Model Evaluation

Learn how to find a maximal marginal classifier using SVM, and use them to detect spam emails, recognize alphabets and more!

  • SVM - Maximal Margin Classifier
  • SVM - Soft Margin Classifier
  • Kernels

Learn how the human decision-making process can be replicated using a decision tree and other powerful ensemble algorithms.

  • Introduction to Decision Trees
  • Algorithms for Decision Tree
  • Construction Truncation and Pruning
  • Random Forests

Learn how weak learners can be ‘boosted’ with the help of each other and become strong learners using different boosting algorithms such as Adaboost, GBM, and XGBoost.

  • Introduction to Boosting and AdaBoost
  • Gradient Boosting

Learn how to group elements into different clusters when you don’t have any pre-defined labels to segregate them through K-means clustering, hierarchical clustering, and more.

  • Introduction to Clustering
  • K Means Clustering
  • Executing K Means in Python
  • Hierarchical Clustering
  • Other Forms of Clustering

Solve the most crucial business problem for a leading telecom operator in India and southeast Asia - predicting customer churn

  • Problem Statement
  • Evaluation
  • Rubric
  • Final Submission
  • Solution

Do you get annoyed by the constant spams in your mail box? Wouldn’t it be nice if we had a program to check your spellings? In this module learn how to build a spell checker & spam detector using techniques like phonetic hashing, bag-of-words, TF-IDF, etc.

  • Introduction to NLP
  • Basic Lexical Processing
  • Advanced Lexical Processing

Learn how to analyze the syntax or the grammatical structure of sentences with the help of algorithms & techniques like HMMs, Viterbi Algorithm, Named Entity Recognition (NER), etc.

  • Introduction to Syntactic Processing
  • Parsing
  • Information Extraction
  • Conditional Random Fields

Build a POS tagger for tagging unknown words using HMMs and modified Viterbi algorithm

  • Problem Statement
  • Evaluation
  • Rubric
  • Final Submission
  • Solution

Learn the most interesting area in the field of NLP and understand different techniques like word-embeddings, LSA, topic modeling to build an application that extracts opinions about socially relevant issues (such as demonetization) on social media platforms.

  • Introduction to Semantic Processing
  • Distributional Semantics Topic Modelling
  • Social Media Opinion Mining
  • Semantic Processing
  • Case Study

Imagine if you could make restaurant booking without opening Zomato. Build your own restaurant-search chatbot with the help of RASA - an open source framework and deploy it on Slack.

  • Problem Statement
  • Evaluation
  • Rubric
  • Final Submission
  • Solution

Learn the most sophisticated and cutting-edge technique in machine learning - Artificial Neural Networks or ANNs.

  • Structure of Neural Networks
  • Feed Forward in Neural Networks
  • Backpropagation in Neural Networks
  • Modifications to Neural Networks
  • Hyperparameter Tuning in Neural Networks

Build a neural network from scratch in Numpy to identify the type of skin cancer from images.

  • Problem Statement
  • Evaluation
  • Rubric
  • Final Submission
  • Solution

Learn the basics of CNN and OpenCV and apply it to Computer Vision tasks like detecting anomalies in chest X-Ray scans, vehicle detection to count and categorise them to help the government ascertain the width and strength of the road.

  • Building CNNs with Python and Keras
  • CNN Architectures and Transfer Learing
  • Style Transfer and Object Detection Industry
  • Demo:Using CNNs with Flowers Images Industry
  • Demo:Using CNNs with X-ray Images


  • Explore and analyze data with Python
  • Train and evaluate machine learning models
  • Train and evaluate regression models
  • Train and evaluate classification models
  • Train and evaluate clustering models
  • Train and evaluate deep learning models
  • Use Automated machine learning in Azure Machine Learning
  • Creating a regression model with Azure Machine Learning designer
  • Creating a classification model with Azure Machine Learning designer
  • Creating a clustering model with Azure Machine Learning designer

  • Introduction to Azure machine learning SDK
  • Train a machine learning model with Azure Machine Learning
  • Work with Data in Azure Machine Learning
  • Work with Compute in Azure Machine Learning
  • Orchestrate machine learning with pipelines
  • Deploy real-time machine learning services with Azure Machine Learning
  • Deploy batch inference pipelines with Azure Machine Learning
  • Tune hyper parameters with Azure Machine Learning
  • Automate machine learning model selection with Azure Machine Learning
  • Explore differential privacy
  • Explain machine learning models with azure machine learning
  • Detect and mitigate unfairness in models with azure machine learning
  • Monitor models with azure machine learning
  • Monitor data drift with azure machine learning.

Big Data - Beyond The Hype, Big Data Skills And Sources Of Big Data, Big Data Adoption, Research And Changing Nature Of Data Repositories, Data Sharing And Reuse Practices And Their Implications For Repository Data Curation, Overlooked And Overrated Data Sharing, Data Curation Services In Action, Open Exit: Reaching The End Of The Data Life Cycle, The Current State Of Meta-Repositories For Data, Curation Of Scientific Data At Risk Of Loss: Data Rescue And Dissemination


Introduction of Big data programming-Hadoop, The ecosystem and stack, The Hadoop Distributed File System (HDFS), Components of Hadoop, Design of HDFS, Java interfaces to HDFS, Architecture overview, Development Environment, Hadoop distribution and basic commands, Eclipse development, The HDFS command line and web interfaces, The HDFS Java API (lab), Analyzing the Data with Hadoop, Scaling Out, Hadoop event stream processing, complex event processing, MapReduce Introduction, Developing a Map Reduce Application, How Map Reduce Works, The MapReduce Anatomy of a Map Reduce Job run, Failures, Job Scheduling, Shuffle and Sort, Task execution, Map Reduce Types and Formats, Map Reduce Features, Real-World MapReduce,

Hadoop Environment:

Setting up a Hadoop Cluster, Cluster specification, Cluster Setup and Installation, Hadoop Configuration, Security in Hadoop, Administering Hadoop, HDFS – Monitoring & Maintenance, Hadoop benchmarks,

Introduction to HIVE,

Programming with Hive: Data warehouse system for Hadoop, Optimizing with Combiners and Practitioners (lab), Bucketing, more common algorithms: sorting, indexing and searching (lab), Relational manipulation: map-side and reduce-side joins (lab), evolution, purpose and use, Case Studies on Ingestion and warehousing


Overview, comparison and architecture, java client API, CRUD operations and security

Apache Spark APIs for large-scale data processing:

Overview, Linking with Spark, Initializing Spark, Resilient Distributed Datasets (RDDs), External Datasets, RDD Operations, Passing Functions to Spark, Job optimization, Working with Key-Value Pairs, Shuffle operations, RDD Persistence, Removing Data, Shared Variables, EDA using PySpark, Deploying to a Cluster Spark Streaming, Spark MLlib and ML APIs, Spark Data Frames/Spark SQL, Integration of Spark and Kafka, Setting up Kafka Producer and Consumer, Kafka Connect API, Mapreduce, Connecting DB’s with Spark

Data warehousing Concepts

Understand how to formulate and validate hypotheses for a population to solve real-life business problems.

  • What is DWH?
  • Characteristics of Datawarehouse
  • Difference between OLTP and DWH
  • Architecture of DWH
  • Various BI tools
  • Types of DWH
  • Types of Dimensional Data Modeling
  • Surrogate key
  • Types of Dimension
Tableau Desktop (Introduction)
  • Introduction Tableau
  • Connecting to Excel, CSV Text Files
  • Getting Started
  • Product Overview
  • Connecting to Databases
  • Working with Data
  • Analyzing
  • Formatting
  • Introduction to Calculations
  • Dashboard Development
  • Sharing
  • Data Calculations
  • Aggregate Calculations
  • User Calculations
  • Table Calculations
  • Logical Calculations
  • String Calculations
  • Number Calculations
  • Type Conversion
  • Parameters
  • Filtering Conditions
  • Filtering Measures
  • Histograms
  • Sorting
  • Grouping
  • Sets
  • Tree maps, word clouds and bubble charts
  • Pareto Charts
  • Waterfall Charts
  • Bump Charts
  • Funnel Charts
  • Bollinger Bands
Module 1: Get Started with Microsoft Data Analytics

This module explores the different roles in the data space, outlines the important roles and responsibilities of a Data Analysts, and then explores the landscape of the Power BI portfolio.

  • Data Analytics and Microsoft
  • Getting Started with Power BI
Lab : Getting Started
  • Getting Started

After completing this module, you will be able to:

  • Explore the different roles in data
  • Identify the tasks that are performed by a data analyst
  • Describe the Power BI landscape of products and services
  • Use the Power BI service
Module 2: Prepare Data in Power BI

This module explores identifying and retrieving data from various data sources. You will also learn the options for connectivity and data storage, and understand the difference and performance implications of connecting directly to data vs. importing it.

  • Get data from various data sources
  • Optimize performance
  • Resolve data errors
Lab : Preparing Data in Power BI Desktop
  • Prepare Data

After completing this module, you will be able to:

  • Identify and retrieve data from different data sources
  • Understand the connection methods and their performance implications
  • Optimize query performance
  • Resolve data import errors
Module 3: Clean, Transform, and Load Data in Power BI

This module teaches you the process of profiling and understanding the condition of the data. They will learn how to identify anomalies, look at the size and shape of their data, and perform the proper data cleaning and transforming steps to prepare the data for loading into the model.

  • Data shaping
  • Enhance the data structure
  • Data Profiling
Lab : Transforming and Loading Data
  • Loading Data

After completing this module, students will be able to:

  • Apply data shape transformations
  • Enhance the structure of the data
  • Profile and examine the data
Module 4: Design a Data Model in Power BI

This module teaches the fundamental concepts of designing and developing a data model for proper performance and scalability. This module will also help you understand and tackle many of the common data modeling issues, including relationships, security, and performance.

  • Introduction to data modeling
  • Working with tables
  • Dimensions and Hierarchies
Lab : Data Modeling in Power BI Desktop
  • Create Model Relationships
  • Configure Tables
  • Review the model interface
  • Create Quick Measures
Lab : Data Modeling in Power BI Desktop
  • Create Model Relationships
  • Configure Tables
  • Review the model interface
  • Create Quick Measures
Lab : Advanced Data Modeling in Power BI Desktop
  • Configure many-to-many relationships
  • Enforce row-level security

After completing this module, you will be able to:

  • Understand the basics of data modeling
  • Define relationships and their cardinality
  • Implement Dimensions and Hierarchies
  • Create histograms and rankings
Module 5: Create Measures using DAX in Power BI

This module introduces you to the world of DAX and its true power for enhancing a model. You will learn about aggregations and the concepts of Measures, calculated columns and tables, and Time Intelligence functions to solve calculation and data analysis problems.

  • Introduction to DAX
  • DAX context
  • Advanced DAX
Lab : Introduction to DAX in Power BI Desktop
  • Create calculated tables
  • Create calculated columns
  • Create measures
Lab : Advanced DAX in Power BI Desktop
  • Use the CALCULATE() function to manipulate filter context
  • use Time Intelligence functions

After completing this module, you will be able to:

  • Understand DAX
  • Use DAX for simple formulas and expressions
  • Create calculated tables and measures
  • Build simple measures
  • Work with Time Intelligence and Key Performance Indicators
Module 6: Optimize Model Performance

In this module you are introduced to steps, processes, concepts, and data modeling best practices necessary to optimize a data model for enterprise-level performance.

  • Optimze the model for performance
  • Optimize DirectQuery Models
  • Create and manage Aggregations

After completing this module, you will be able to:

  • Understand the importance of variables
  • Enhance the data model
  • Optimize the storage model
  • Implement aggregations
Module 7: Create Reports

This module introduces you to the fundamental concepts and principles of designing and building a report, including selecting the correct visuals, designing a page layout, and applying basic but critical functionality. The important topic of designing for accessibility is also covered.

  • Design a report
  • Enhance the report
Lab : Designing a report in Power BI
  • Create a live connection in Power BI Desktop
  • Design a report
  • Configure visual fields adn format properties
Lab : Enhancing Power BI reports with interaction and formatting
  • Create and configure Sync Slicers
  • Create a drillthrough page
  • Apply conditional formatting
  • Create and use Bookmarks

After completing this module, you will be able to:

  • Design a report page layout
  • Select and add effective visualizations
  • Add basic report functionality
  • Add report navigation and interactions
  • Improve report performance
  • Design for accessibility
Module 8: Create Dashboards

In this module you will learn how to tell a compelling story through the use of dashboards and the different navigation tools available to provide navigation. You will be introduced to features and functionality and how to enhance dashboards for usability and insights.

  • Create a Dashboard
  • Real-time Dashboards
  • Enhance a Dashboard
Lab : Designing a report in Power BI Desktop - Part 1
  • Create a Dashboard
  • Pin visuals to a Dashboard
  • Configure a Dashboard tile alert
  • Use Q&A to create a dashboard tile

After completing this module, students will be able to:

  • Create a Dashboard
  • Understand real-time Dashboards
  • Enhance Dashboard usability
Module 9: Create Paginated Reports in Power BI

This module will teach you about paginated reports, including what they are how they fit into Power BI. You will then learn how to build and publish a report.

  • Paginated report overview
  • Create Paginated reports
Lab : Creating a Paginated report
  • Use Power BI Report Builder
  • Design a multi-page report layout
  • Define a data source
  • Define a dataset
  • Create a report parameter
  • Export a report to PDF

After completing this module, you will be able to:

  • Explain paginated reports
  • Create a paginated report
  • Create and configure a data source and dataset
  • Work with charts and tables
  • Publish a report
Module 10: Perform Advanced Analytics

This module helps you apply additional features to enhance the report for analytical insights in the data, equipping you with the steps to use the report for actual data analysis. You will also perform advanced analytics using AI visuals on the report for even deeper and meaningful data insights.

  • Advanced Analytics
  • Data Insights through AI visuals
Lab : Data Analysis in Power BI Desktop
  • Create animated scatter charts
  • Use teh visual to forecast values
  • Work with Decomposition Tree visual
  • Work with the Key Influencers visual

After completing this module, you will be able to:

  • Explore statistical summary
  • Use the Analyze feature
  • Identify outliers in data
  • Conduct time-series analysis
  • Use the AI visuals
  • Use the Advanced Analytics custom visual
Module 11: Create and Manage Workspaces

This module will introduce you to Workspaces, including how to create and manage them. You will also learn how to share content, including reports and dashboards, and then learn how to distribute an App.

  • Creating Workspaces
  • Sharing and Managing Assets
Lab : Publishing and Sharing Power BI Content
  • Map security principals to dataset roles
  • Share a dashboard
  • Publish an App

After completing this module, you will be able to:

  • Create and manage a workspace
  • Understand workspace collaboration
  • Monitor workspace usage and performance
  • Distribute an App
Module 12: Manage Datasets in Power BI

In this module you will learn the concepts of managing Power BI assets, including datasets and workspaces. You will also publish datasets to the Power BI service, then refresh and secure them.

  • Parameters
  • Datasets

After completing this module, you will be able to:

  • Create and work with parameters
  • Manage datasets
  • Configure dataset refresh
  • Troubleshoot gateway connectivity
Module 13: Row-level security

This module teaches you the steps for implementing and configuring security in Power BI to secure Power BI assets.

  • Security in Power BI

After completing this module, you will be able to:

  • Understand the aspects of Power BI security
  • Configure row-level security roles and group memberships

platforms covered


Jupyter Notebooks

These notebooks are widely used for coding in Python.



This is one of the most dominant languages for data science in the industry today because of its ease, flexibility, open-source nature. It has gained rapid popularity and acceptance in the ML community.



It is Numerical computing tool



It is data processing and data manipulation tool



It is data visualization tool.


Scikit learn

Simple and efficient tools for predictive data analysis. It features various Classification, Regression and clustering algorithms .



It is easily the most widely used tool in the industry today. Google might have something to do with that!



This super flexible deep learning framework is giving major competition to TensorFlow. PyTorch has recently come into the limelight and was developed by researchers at Facebook



It is used extensively for building deep learning applications


NLTK and Textblob

It is used for natural language processing


Azure Machine Learning

It is a platform for operating machine learning workloads in the cloud. Azure Machine Learning enables you to manage:

  • Scalable on-demand compute for machine learning workloads.
  • Data storage and connectivity to ingest data from a wide range sources.
  • Machine learning workflow orchestration to automate model training, deployment, and management processes.
  • Model registration and management, so you can track multiple versions of models and the data on which they were trained.
  • Metrics and monitoring for training experiments, datasets, and published services.
  • Model deployment for real-time and batch inferencing


Masters in Data Science, Artificial Intelligence & Business Analytics certified by Microsoft.


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Masters in Data Science, Artificial Intelligence & Business Analytics

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  • Online - live Classes
  • No Cost EMI Available

Masters in Data Science, Artificial Intelligence & Business Analytics

INR. 79,990*

Inclusive of all Taxes

  • Training
  • 4 Certifications
  • Online - live Classes
  • No Cost EMI Available

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Frequently Asked Questions

What is artificial intelligence?

AI can be described as an area of computer science that simulates human intelligence in machines. It’s about smart algorithms making decisions based on the available data. Whether it’s Amazon’s Alexa or a self-driving car, the goal is to mimic human intelligence at lightning speed (and with a reduced rate of error).

What are intelligent agents?

An intelligent agent is an autonomous entity that leverages sensors to understand a situation and make decisions. It can also use actuators to perform both simple and complex tasks. In the beginning, it might not be so great at performing a task, but it will improve over time. The Roomba vacuum cleaner is an excellent example of this.

What’s the most popular programming language used in AI?

The open-source modular programming language Python leads the AI industry because of its simplicity and predictable coding behavior. It's popularity can be attributed to open-source libraries like Matplotlib and NumPy, efficient frameworks such as Scikit-learn, and practical version libraries like Tensorflow and VTK.

What are AI neural networks?

Neural networks in AI mathematically model how the human brain works. This approach enables the machine to think and learn as humans do. This is how smart technology today recognizes speech, objects, and more.

What’s a Turing test?

The Turing test, named after Alan Turing, is a method of testing a machine’s human-level intelligence. For example, in a human-versus-machine scenario, a judge will be tasked with identifying which terminal was occupied by a human and which was occupied by a computer based on individual performance. Whenever a computer can pass off as a human, it’s deemed intelligent. The game has since evolved, but the premise remains the same.

What is Data Science?

Data science is a broad field that refers to the collective processes, theories, concepts, tools and technologies that enable the review, analysis and extraction of valuable knowledge and information from raw data. It is geared toward helping individuals and organizations make better decisions from stored, consumed and managed data. Data science is formerly known as datalogy.

What is the difference between supervised and unsupervised machine learning?

Supervised Machine learning: Supervised learning is the learning of the model where with input variable ( say, x) and an output variable (say, Y) and an algorithm to map the input to the output. That is, Y = f(X).

Unsupervised Machine learning: Unsupervised learning is where only the input data (say, X) is present and no corresponding output variable is there.

What is pruning in Decision Tree ?

When we remove sub-nodes of a decision node, this process is called pruning or opposite process of splitting.

What is Random Forest?

Random forest is a versatile machine learning method capable of performing both regression and classification tasks. It is also used for dimentionality reduction, treats missing values, outlier values. It is a type of ensemble learning method, where a group of weak models combine to form a powerful model.

What is deep learning?

Deep learning is a machine learning technique. It teaches a computer to filter inputs through layers to learn how to predict and classify information. Observations can be in the form of images, text, or sound.

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