Integrated Program in Data Science, Machine Learning and Artificial Intelligence (IDMA – Full Time)
India’s only industry focused, comprehensive, full time online Data Science, Machine Learning and Artificial Intelligence program.
About the Program
Hero Vired’s integrated program in Data Science, Machine Learning and Artificial Intelligence is designed to give you the right tools to become indispensable in the future that is driven by smart technologies and predictive analytics. With this course, you get to be at the forefront of innovation and become the thought leader and game changer the world needs.
Live Online Classes
Program Delivery
11 Months
Program Duration
August, 2021
Program Start Date
INR 6,50,000+ Applicable Taxes
Program Fees (0% EMIs Available*)
UG Degree In STEM OR Finance-Economics Background With Maths and/or Stats
Program Eligibility
Program Snapshots
The IDMA full time course is designed to completely pivot your career to a fulfilling future in Data Science, Machine Learning and AI
With a strong academic and industry focus, the course provides hands-on project based learning on real data
You will learn end to end programming, including data engineering and MLOPs
The course is designed to deliver global academic quality with complete Indian contextualisation for a holistic experience
Once you complete this program, you will be able to create your own ML algorithms and libraries from scratch. You won’t be dependent on existing libraries
This is for You
Your choice
Are you a naturally curious critical thinker? Interested in science and maths? Understand the power and potential of Artificial Intelligence? This program is for you!
Your opportunity
India’s Big Data Analytics sector is estimated to be worth USD 18.9 Billion by 2025. Here’s your opportunity to evolve into a future Data Professional.
Your domain
Job roles are changing, with increased play of Big Data / Analytics. Revenues from analytics will grow by 8X by 2025. 600+ global firms are looking at integrated, pure play products, with AI and Deep learning algorithms. Go, rule this Domain.
Your eligibility
If you have an Undergraduate Degree in STEM (science, technology, engineering and mathematics) and enjoy applied learning, this program is for you!
Program Curriculum
The immersive full time course is designed to make you proficient in the industry standards of Python, R, Tableau, PowerBI, Azure, Excel, Tensorflow, PyTorch, SQL and KNIME.
Overview of Analytics, Machine Learning and Python
programming
Overview of Analytics:
This module will focus on understanding key analytics concepts, solutions, and modus operandi, through real-world business use-cases. The module will additionally also introduce you to the core ideas of Machine Learning and programming on Python.
Learning Outcomes:
Understand why and how businesses use analytics through use-cases; the qualities of a good analyst; analytics methodologies and problem definitions; and the CRISP-OM architecture.
Understand the goal of machine learning; elements of supervised learning, and the difference between the training set and the test set; the difference of classification and regression – two representative kinds of supervised learning.
Introduced to python environments and ML packages, concept of Object-Oriented Programming, programming for a live environment, debugging, IDEs, and python basics such as class, objects, functions, conditions, loops/iterators, array, dictionary, lambda, mathematical and statistical operations, numpy for matrix algebra, exception handling, and file handling.
Overview of Analytics, Machine
Learning and Python programming
Overview of Analytics:
This module will focus on understanding key analytics concepts, solutions, and modus operandi, through real-world business use-cases. The module will additionally also introduce you to the core ideas of Machine Learning and programming on Python.
Learning Outcomes:
Understand why and how businesses use analytics through use-cases; the qualities of a good analyst; analytics methodologies and problem definitions; and the CRISP-OM architecture.
Understand the goal of machine learning; elements of supervised learning, and the difference between the training set and the test set; the difference of classification and regression – two representative kinds of supervised learning.
Introduced to python environments and ML packages, concept of Object-Oriented Programming, programming for a live environment, debugging, IDEs, and python basics such as class, objects, functions, conditions, loops/iterators, array, dictionary, lambda, mathematical and statistical operations, numpy for matrix algebra, exception handling, and file handling.
Getting started with Data
About the Module:
In this module, you will be introduced to Business Intelligence tools and the key concepts behind working with data on Python.
Learning Outcomes:
Understand data sources – data availability,
free/open-source/organizational data, policies and guidelines related to importing data, types of data sources. Understand types of data – qualitative v/s quantitative; categorical v/s numerical. Understand how to import data on Python – import/read data from different sources; import/read data of different file formats; metadata and data dictionaries; store data in required structures like dataframes; tools/packages/libraries to perform the above operations using python or other tool. Introduction to Bl tools – concepts of data warehouse and database; using Power Bl to conduct basic data warehouse reporting; Power Bl to conduct visualization of descriptive statist
Data Gym with Python
About the Module:
This module will focus on techniques involved in converting raw data into readable format which can be perceived by a machine and be used further in
ML applications.
Learning Outcomes:
Get a strong grasp of the following techniques:
Data formatting
Data description – high-level overview of data, dimension of data, data types of all features; summary statistics of data
Data manipulation
Data sanity checks
Exploratory Data Analysis – univariate and bivariate analysis
Data Pre-processing – missing value and outlier treatment
Feature Engineering – scaling and normalization, variable creation
Fundamentals of Statistics
About the Module:
This mathematics module offers an introduction to the theoretical foundations of statistical methods that are useful in many applications. The goal is to understand the role of mathematics in the research and development of efficient statistical methods.
Learning Outcomes:
Formulate a statistical problem in mathematical terms, from a real-life situation, formulate a statistical problem.
Understand the role of mathematics in the design and analysis of statistical methods.
Select appropriate statistical methods.
Understand the implications and limitations of various methods.
Expand your statistical knowledge to not only include a list of methods, but also the mathematical principles that link these methods together, equipping you with the tools you need to develop new ones.
Probability – The Science of Uncertainty and Data
About the Module:
This MITx course will cover all the basic probability concepts, including multiple discrete or continuous random variables, expectations, and conditional distributions, laws of large numbers, the main tools of Bayesian inference methods, and an introduction to random processes (Poisson processes and Markov chains).
Learning Outcomes:
Learn the basic structure and elements of probabilistic models, random variables, their distributions, means, and variances, probabilistic calculations, inference methods, laws of large numbers and their applications, random processes.
Probability – The Science of
Uncertainty and Data
About the Module:
This MITx course will cover all the basic probability concepts, including multiple discrete or continuous random variables, expectations, and conditional distributions, laws of large numbers, the main tools of Bayesian inference methods, and an introduction to random processes (Poisson processes and Markov chains).
Learning Outcomes:
Learn the basic structure and elements of probabilistic models, random variables, their distributions, means, and variances, probabilistic calculations, inference methods, laws of large numbers and their applications, random processes.
Machine Learning with Python-From Linear Models
to Deep Learning
About the Module:
In this module, you will learn about principles and algorithms for turning training data into effective automated predictions.
Topics covered will include – representation, over-fitting, regularization, generalization, VC dimension; clustering, classification, recommender problems, probabilistic modelling, reinforcement learning; online algorithms, support vector machines, and neural networks / deep learning.
Learning Outcomes:
Understand principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning.
Implement and analyze models such as linear models, kernel machines, neural networks, and graphical models.
Choose suitable models for different applications.
Implement and organize machine learning projects, from training, validation, parameter tuning, to feature engineering.
Machine Learning with Python- From
Linear Models to Deep Learning
About the Module:
In this module, you will learn about principles and algorithms for turning training data into effective automated predictions.
Topics covered will include – representation, over-fitting, regularization, generalization, VC dimension; clustering, classification, recommender problems, probabilistic modelling, reinforcement learning; online algorithms, support vector machines, and neural networks / deep learning.
Learning Outcomes:
Understand principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning.
Implement and analyze models such as linear models, kernel machines, neural networks, and graphical models.
Choose suitable models for different applications.
Implement and organize machine learning projects, from training, validation, parameter tuning, to feature engineering.
Statistics, Computation and Applications
About the Module:
This MITx course start with a review of common statistical and computational tools such as hypothesis testing, regression, and gradient descent methods. Then, learners will study common models and methods to analyze specific types of data in four different domain areas, viz. epigenetic codes and data visualization, criminal networks and network analysis, prices, economics and time series, and environmental data and spatial statistics.
Learning Outcomes:
Model, form hypotheses, perform statistical analysis on real data Use dimension reduction techniques such as principal component analysis to visualize high-dimensional data and apply this to genomics data.
Analyze networks (e.g. social networks) and use centrality measures to describe the importance of nodes, and apply this to criminal networks.
Model time series using moving average, autoregressive and other stationary models for forecasting with financial data.
Use Gaussian processes to model environmental data and make predictions.
Communicate analysis results effectively.
Statistics, Computation
and Applications
About the Module:
This MITx course start with a review of common statistical and computational tools such as hypothesis testing, regression, and gradient descent methods. Then, learners will study common models and methods to analyze specific types of data in four different domain areas, viz. epigenetic codes and data visualization, criminal networks and network analysis, prices, economics and time series, and environmental data and spatial statistics.
Learning Outcomes:
Model, form hypotheses, perform statistical analysis on real data Use dimension reduction techniques such as principal component analysis to visualize high-dimensional data and apply this to genomics data.
Analyze networks (e.g. social networks) and use centrality measures to describe the importance of nodes, and apply this to criminal networks.
Model time series using moving average, autoregressive and other stationary models for forecasting with financial data.
Use Gaussian processes to model environmental data and make predictions.
Communicate analysis results effectively.
Advanced Concepts in Machine Learning
About the Module:
This module will delve deeper into more advanced concepts in machine learning with python in linear regression, linear classifiers, tree- and ensemble-based models, non-linear classification, recommender systems, unsupervised learning, reinforcement learning, neural networks, natural language processing, image and video analytics.
Learning Outcomes:
Understand more advanced concepts in Machine Learning, and learn how to implement and analyze more complex algorithms.
Delve deeper into the fundamental concepts of classic ML algorithms such as linear regression, logistic regression, and neural networks.
Explore and analyze additional clustering and reinforcement learning algorithms.
Implement natural language processing applications such as text similarity, sentiment analysis, text classification, and deep transfer learning with transformers.
Implement image and video analytics applications such as handwriting recognition, object classification, object detection, and semantic segmentation.
Advanced Concepts in
Machine Learning
About the Module:
This module will delve deeper into more advanced concepts in machine learning with python in linear regression, linear classifiers, tree- and ensemble-based models, non-linear classification, recommender systems, unsupervised learning, reinforcement learning, neural networks, natural language processing, image and video analytics.
Learning Outcomes:
Understand more advanced concepts in Machine Learning, and learn how to implement and analyze more complex algorithms.
Delve deeper into the fundamental concepts of classic ML algorithms such as linear regression, logistic regression, and neural networks.
Explore and analyze additional clustering and reinforcement learning algorithms.
Implement natural language processing applications such as text similarity, sentiment analysis, text classification, and deep transfer learning with transformers.
Implement image and video analytics applications such as handwriting recognition, object classification, object detection, and semantic segmentation.
Concept of MLOps
About the Module:
This module will uncover some of the concepts and tools behind operationalizing ML models, to ensure that these can generate business benefits by optimizing, automating and scaling the ML project pipeline.
Learning Outcomes:
Learn how to make a web service online; operationalize a ML pipeline
Use Azure ML Studio, specifically AutoML and ML Designer, to demonstrate key components of MLOps.
Familiarized with prototyping using Flask.
Data Visualization and Storytelling
About the Module:
This module will use a blend of data visualization, analytics reporting, and summary statistics to craft a narrative which is anchored by compelling visuals.
Learning Outcomes:
Understand the principles of data storytelling.
Solid grasp on buidling visualizations on Tableau.
Understand the importance of ex-post storytelling by visualizing model outcomes, and post-modelling reporting of predications, and performance metrics.
Data Science in the Indian context
About the Module:
In this module, you will be introduced to the quirks and nuances of conducting data science and analytics in the Indian context.
Learning Outcomes:
Understand primary and secondary data research in India while dealing with data availability; working with open and semi-open datasets, and non-traditional supplementary sources of data.
Explainable AI
About the Module:
This module will take you through a set of tools and frameworks to help understand and interpret predictions made by ML models, with a view to de-mystifying models’ behaviour so that businesses can understand, appropriately trust, and effectively manage ML projects.
Learning Outcomes:
Understanding of concepts such as the “black box· of Al, accuracy versus explainability, interpretability versus explainability, the tree approach, sensitivity analysis, layer-wise relevance propagation, algorithmic generalization for deeper control and understanding.
Understanding techniques for improving explainability of models such as feature importance, local interpretable model-agnostic explanations (LIME), Shapley values, partial dependence plots, and DeepLIFT.
Other tools for Machine Learning
About the Module:
This module will introduce tools other than Python for data science and machine learning.
Learning Outcomes:
Implement a ML project workflow using CRISP-OM concepts on R and KNIME.
In-Person Industry & Learning Meetups
All participants are invited to a non-compulsory in-person interaction twice a year during the first and the final quarter of the program. This will be at a separate cost to be borne by participants and does not include travel costs to and from the location.
In-Person Industry & Learning Meetups
All participants are invited to a non-compulsory in-person interaction twice a year during the first and the final quarter of the program. This will be at a separate cost to be borne by participants and does not include travel costs to and from the location.
Interaction 1
Interaction 2
Note: These are purely optional sessions, and learners can have access to the same sessions and content through their learning management system as well. Additionally, the scheduling and details of these sessions will be subject to and dependent on external circumstances and prevailing regulations.
Your Faculty
Dr Sarabjot Singh Anand
Involved in the field of data mining since the early 1990s, developing algorithms, applying them to real-world problems, data analytics consulting and training the next generation of data analysts.
Indranath Mukherjee
Analytics practitioner with 2 decades of industry experience at companies like Deloitte, Accenture, EXL and Fractal Analytics and currently leading the India Strategic Analytics team at AXA XL.
Atul Tripathi
With over 16+ years of experience, and a consultant in the National Security Council Secretariat, PMO and member of the team that worked on the AI and Data Protection Policy for India and also a member of Leaders Excellence at Harvard Square and GARP.
Madhukar Kumar
Currently the Chief Analytics Officer at Shine.com with over 14 years of experience at firms like American Express, GE Money, WNS, SG Analytics & Upgrad; was awarded “AI Leader of the Year” in International Business and Academic Excellence Awards 2019.
Dr Avik Sarkar
Former Head of Data Analytics at NITI Aayog; currently faculty at the ISB working in the areas of Data, Emerging Technology, and Public Policy and heading the development of India Data Portal, for analysis and visualization of government data.
Get Certified
On the successful completion of this program, you will receive a certificate from Hero Vired in collaboration with MIT, MicroMasters® credentials, and postgraduate certification from BML Munjal University*.
*T&C apply
Program FAQs
Who is this program for?
If you wish to build or pivot your career in data science, machine learning and artificial intelligence, and willing to invest 11 months in learning it, the IDMA FT program is for you! Blending industrial and academic material it is the only comprehensive integrated program in DS, ML and AI. Developed for STEM graduates, who are just out of college or working professionals.
Why IDMA FT?
Most of the existing programs in DS, ML or AI do not integrate all of the three aspects for you to become a practitioner in the field. Additionally, nearly none of these programs also cover, what we like to call, the edges of data science. Which, on the left is data engineering, and on the right, is deployment and prototyping.
Most programs only focus on building models in the development environment, which is useless for implementation! The IDMA FT program is deeply integrated with the Data Science MicroMasters® from MIT, which provides you with deep knowledge which is unparalleled in online or in-person learning. This is augmented by extensive hands-on industry cases, real life data sets, big data engineering and ML Ops.
What do you mean by Indian contextualization?
Nearly all DS-ML-AI programs only showcase cases, data sets and analysis from a developed world context. While this is essential for learning, it does not allow you to understand the problems of data in the Indian context.
In IDMA we teach an entire module on DS-ML-AI from the Indian context. We believe this is essential for anyone interested in working with Indian data, Indian start-ups and even the government or government agencies.
Do you cover these topics:
- Neural Networks?
Yes. The program comprehensively covers Artificial Neural Networks (ANN) using Multi Layered Perceptrons for classification problems, Convolutional Neural Networks (CNN) for video analytics, Recurrent Neural Networks (RNN) for NLP and time series data, with relevant libraries (Keras, Tensorflow and PyTorch) and hands-on cases. - Probability and Statistics?
The foundation of all ML and AI is in probability/statistics, optimization and linear algebra. Most so-called ‘data scientists’ do not understand these basics and thus try to implement libraries in a GIGO (Garbage-In-Garbage-Out) mindset.
Our IDMA program covers these topics in detail but not as separate ‘theory’ material. These are treated as concepts and learning that we connect back closely to actual models and real-world applications. - Python and R?
Python – the fastest growing software in the world is our primary tool for the program. You can also access most ML-AI libraries (Tensorflow, Keras and Pytorch) through Python.
We also teach R because it is a software that is currently being used by many organizations. Additionally, R serves as a powerful tool for statistical analysis and visualization. - Tensorflow or PyTorch?
We believe that as future creators you need to be tools agnostic. The IDMA program helps you master both Tensorflow and PyTorch because each comes with its own set of pros and cons. Tools will keep changing and evolving. But core concepts, programming skills and business applications remain set in stone.
Do I need to know:
1.Programming?
No, we don’t expect you to have any pre-knowledge of programming. But if you have coded in the past, it will help you pick up the coding parts of the program faster. The program will start by setting the foundation.
Please note that entry to the IDMA FT is competitive, and knowledge of programming might help your application stand out.
2.Mathematics/Statistics?
Working with data and analysing does not mean that you need to come from a math/stat background.
But you will need to be comfortable with math and around numbers – because that is the language of data science.
Please refer to the mathematical requirements for MIT MicroMasters® courses to get a sense of the background. This is a transformational program. We expect it to change your life – so be prepared for the heavy lifting.
3.Computer Science?
If you have attended classes in algorithms, data structures, programming, etc in your graduation then you have the advantage. In any case, having a STEM background will help you. There are exceptionally successful data scientists who come from backgrounds in other engineering disciplines, economics, math, statistics, finance and similar numbers-based fields.
Will I be able to:
1.Create a New Algorithm?
Yes, ofcourse! The IDMA FT is arguably the only program which teaches you how to do that. Just be focused and understand the nuances of the material covered – learn from our leading global faculty – and soon you will be able to create an algorithm from scratch.
2.Create a New Library?
Absolutely. Again, we provide you the programming chops and the algorithms knowledge to create a new library in any area of DS-ML-AI. This will ensure that you are not only dependent on libraries created by others, nor are you one of those data scientists who never can create production ready code.
3.Create a Product Prototype?
Definitely. Remember the edges of DS-ML-AI? We believe that the edges have fat tails and we need to know those as much as we need to know the algorithms. Thus, we will teach you how to deploy your product using Flask, Django and R-Studio.
If you can’t work with the data, how would you effectively implement it in your algorithms? Through this program, we will take you to the edges of data. Ensuring that you can work with structured and unstructured data, in the cloud and on your systems – data which is big or small, and are able to clean it effectively for use in ML-AI algorithms.
What would work better for me – the full time or part time program?
It depends on you and the time and duration of engagement you would be able to commit towards the program. If you are a working professional, and intend to keep working through the program, we would recommend the part-time program for you.
However, if you are committed to a transformational experience, and are looking for a program that can catapult you into a highly rewarding job role – we would encourage enrolling for our full-time program.
What would work better for me –
the full time or part time program?
It depends on you and the time and duration of engagement you would be able to commit towards the program. If you are a working professional and intend to keep working through the program, we would recommend the part-time program for you.
However, if you are committed to a transformational experience, and are looking for a program that can catapult you into a highly rewarding job role – we would encourage enrolling for our full-time program.
