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Integrated Program in

Data Science, Artificial Intelligence & Machine Learning

In collaboration with

Hero Vired’s Integrated program in Data Science, Machine Learning and Artificial Intelligence is designed to give you the right skills to analyze data and build complex models to solve business problems. Here's what you can expect:

Ranked #1 PG Data Science Course by Analytics India Magazine

80+ Live Sessions with faculty from industry and academia

Eligibility for an MITx MicroMasters® Program Certificate

Apply now
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Dates to be announced

Program Start Date

11 months


Bachelor’s degree


Dates to be announced

Application Deadline

Custom curated international curriculum

Hero Vired’s Integrated program in Data Science, Machine Learning and Artificial Intelligence is designed to give you the right skills to analyze data and build complex models to solve business problems.

Learning Duration



Total Effort Required



Total Weekly Effort



Tools Covered

Tensor Flow


Program Brochure

For detailed curriculum and program structure, download the program brochure

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  • Installation and set-up, Python Basics - Variables, built-in functions (print, type, help, range, input,...)
  • Loops and simple operations, Data Structures and Operations (Lists and Tuple)
  • Data Structures and Operations (Dictionary, Set), Conditional Statements, Functions and methods
  • Python Modules overview - Pandas, Numpy - DataFrames & Arrays. Functions, Numpy Array Operations, OOPs and Debugging concepts, Overview of ML Libraries
  • Pandas Basics - DataFrames & Arrays, reading files, row/column selection, sub-setting, EDA, variable creation, data summaries
  • Pandas Advanced - Visualization with matplotlib and Seaborn, Data profiling and analysis, variable correlation. Handling data anomalies, feature engineering
  • Mathematics Basics and Advanced
  • Linear Algebra: Vectors and Scalars, Matrices and Matrix operations, 2D/3D Plots, Functions, Limits and Derivatives, Notations, Numbers, Sequences, Points, Lines and Planes, Gaussian Distribution, Probability Density Functions
  • Industry Application, Core Concepts (Train & Test samples, model metrics)
  • Linear/ Non-linear Regression models and implementation in Scikit-learn
  • Classification models (Logistic, SVM) and implementation in Scikit-learn
  • Classification models (Decision Trees) and implementation in Scikit-learn
  • Ensemble Learning: Tree-based and others
  • Working with Linear Classifiers and Linearly Seperable Data
  • Estimatiing Model Parameters using Perceptron Algorithm and Gradient Descent
  • Working with Linear SVM
  • Linear Regression
  • Non Linear Models and Feature Maps
  • SVM with Kernels
  • Recommendation Engines using Memory Based and Model Based Methods (Matrix Factorization)
  • Introduction to Neural Networks: Activation Functions, Forward pass, Backward pass
  • Recurrent Neural Networks and Sequence Models
  • Convolutional Neural Networks, Using Pytorch for Neural Network Models Implementation
  • KMeans Clustering and EM Algorithm
  • Gaussian Mixture Models for Collaborative Filtering
  • Reinforcement Learning
  • Statistics Concepts - Descriptive Statistics (mean, median, variance, std. dev, percentiles)
  • Understanding Univariate and Multivariate Distributions through plots (Histograms, Bar Plots, Box Plots, Two-way Tables, Scatter Plots, Q-Q Plots)
  • Correlation, Inferential Statistics (Point & Interval Estimation and use of various statistics)
  • CLT and Law of large numbers
  • Parametric Statistical Models, Parametric Estimation and Confidence Interval
  • Delta Method and Confidence Intervals
  • Introduction to Hypothesis Testing, and Type 1 and Type 2 Errors
  • Total Variation Distance, Kullback-Leibler (KL) Divergence, and the Maximum Likelihood Principle, MLE
  • Covariance Matrices, Multivariate Statistics, and Fisher Information
  • Maximum Likelihood Estimation (Continued) and the Method of Moments, M Estimation
  • Hypothesis Testing: χ2 distribution and T-test, Hypothesis Testing: Wald’s test, Likelihood Ratio Test, and Implicit Hypothesis
  • Hypothesis Testing: χ2-test for Multinomial Distribution, Goodness of Fit Test; Hypothesis Testing: Kolmogorov-Smirnov Test, Kolmogorov Lilliefors Test, QQ-plot
  • Introduction to Bayesian Statistics; Jeffrey’s Prior and Bayesian Confidence
  • Linear Regression 1; Linear Regression 2
  • Introduction to Generalized Linear Model: Exponential Families; The Canonical Link Function
  • Basic Text Processing and NLP: Using Regex, creating tfidf features, POS Tagging and dependency parsing
  • DL in Practice: Using tf/pytorch to build simple neural networks, understand automatic differentiation, carry out gradient computations
  • DL in NLP 1: LSTMs and GRUs, Encoder Decoder Architecture for Translation
  • DL in NLP 2: BERT based models
  • DL In Computer Vision1: Use transfer learning to build image classifiers. Build multiclass and multilabel classifiers
  • DL In Computer Vision2: Single Shot Object Detection, measuring Object Detector Performance, custom labelling and custom training
  • Get the Hero Vired Advantage

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    70-90% live interactive classes
    100% Program Completion Rate
    Personalised guidance & support
    Pre recorded Lectures
    Low completion rate
    No personalised assistance

    Industry Leading Faculty

    Learn from world-class faculty that will guide you through this certification program

    Manoj Agarwal

    Manoj Agarwal

    Ph.D. Computer Science | 17+ years of experience

    An expert in designing solutions using machine learning techniques. Specializes in multi-objective optimization techniques and evolutionary computations.

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    Subhashis Majumder

    Subhashis Majumder

    Ph.D. in CSE | 27+ years of experience

    M.Tech in CS from IIT, he has been a full-time academic since 2003, with research spanning Social Networking, Data Analysis, and more.

    Guru-nanak-university dono
    Maurya Malineni

    Maurya Malineni

    B.Tech in Electrical Engineering | 8+ years of experience

    A skilled Data Scientist excelling in areas like statistics, machine learning, gamification, etc., he also adeptly applies numerous game techniques in practical scenarios.

    javadpur university HIT KOLKATA
    Siddarth Kothotya

    Siddarth Kothotya

    MBA, General Management | 4+ years of experience

    A Data Analyst with hands on experience in Data Analytics, Data Science, Artificial Intelligence, and ML. Has previous experience as a Machine Learning Engineer.

    Guru-nanak-university dono

    Programs with leading universities and organizations

    MIT Open Learning

    In collaboration with

    MIT Open Learning

    National Skill Development Corporation

    Integrated With

    National Skill Development Corporation

    Get certified after you graduate from the program

    Upon successful completion of this Data Science program, you will be eligible for the following certificate:


    Hero Vired Certificate

    Benefit from the Hero Group’s decades of research and understanding of the Indian education and job landscape.
    * Certificates are indicative and subject to change

    Achieve your career goals through our personalized career mentorship and guidance

      CV and LinkedIn profile building

    CV and LinkedIn profile building

     interview preparation

    Interview preparation

     Career Preparedness

    1-1 Career coaching sessions


    Hritwij Shrivastava

    Hritwij Shrivastava

    Integrated Program in Data Science, Machine Learning & AI

    From live projects to coaching sessions, Hero Vired helped me at every stage. I became extremely confident in ML after successfully completing the MITx assignments.

    Ghagran Lokesh

    Ghagran Lokesh

    Integrated Program in Data Science, Machine Learning & AI

    The program was taught well and opened doors for me in my career. The career services team was excellent and helped me at every step of my journey.

    Faisal Khan

    Faisal Khan

    Integrated Program in Data Science, Machine Learning & AI

    The program not only helped me acquire knowledge of programming languages but also helped enhance my technical skills which helped me secure a promotion at Amazon.

    Application Process

    A simple yet thorough application process that will help you learn key skills to supercharge your career

    Step 1

    Application Submission:

    Complete and review the form before submission. A meticulously filled out application aids us in evaluating your educational objectives and facilitates your seamless enrollment into the program

    Step 2

    Offer Letter Receipt:

    Upon successful application, you will receive an offer letter to enroll, encapsulating comprehensive details about the program, the associated fee structure, and the payment schedule.

    Step 3

    Block your seat:

    Secure your seat by making a nominal payment to confirm your acceptance into the program.

    *This is contingent upon specific program eligibility requirements, criteria, and any potential tests that may be incorporated as part of the enrollment procedure.

    Upskill yourself with Hero Vired

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    70% to 90% Live Instructor-led Classes
    Gamified & Interactive Learning
    Discussion Forums and Community
    Industry Projects & Case Studies
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    Practical Hands-on Learning Session
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    Career Assistance and Workshops
    Data Science, Machine Learning & AI
    Starting at
    Price:4,25,000 + GST
    Start Application
    Seeking Financial Aid?
    Application Deadline:
    Apply early to secure your seat

    Frequently Asked Questions

    1. Neural NetworksThe 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. 2. Probability and StatisticsThe 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 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. 3. Python and RPython – 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.4. Tensorflow or PyTorchWe believe that as future creators you need to be tools agnostic. The 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.
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    Hero Vired is a premium LearnTech company offering industry-relevant programs in partnership with world-class institutions to create the change-makers of tomorrow. Part of the rich legacy of the Hero Group, we aim to transform the skilling landscape in India by creating programs delivered by leading industry practitioners that help professionals and students enhance their skills and employability.

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