Guaranteed placement opportunities in leading global firms

Comprehensive industry-aligned course in Data science, Machine learning, and AI

Hands-on learning with 18 real-world projects covering each technology

Upskill without quitting your job – become a Python Analytics expert in just 7 months

Independently solve critical business problems using machine learning and deep learning techniques

Covers in-demand AI concepts such as Natural Language Processing and Computer Vision

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Classroom Training

Online Instructor-Led Training

Online Self Paced Videos

Analytics & Artificial Intelligence Course

The PG Program in Analytics & Artificial Intelligence is an industry-oriented program which focuses on helping aspirants learn practical application of Machine learning and Analytics, and make them job ready. This program is specifically designed for working professionals with backgrounds in Math, Software Engineering, Statistics and Analytics. It aims to help them gain practical knowledge and accelerate their entry into the roles of Data Scientist, Analyst, or Machine Learning Engineer. This project-based, multi skill course will get you comfortable dealing with different types of structured and unstructured data to solve critical business problems using machine learning and deep learning.



Industry-Endorsed Curriculum
Master the most popular tools used by most of the Data Scientists and Machine Learning Engineers

Experiential Learning
Multiple lab sessions throughout the course to work on your projectsand internalize key concepts


Industry Mentorship
Guest lectures & mentorship teach you cutting-edge techniques to solve complex business problems

Imarticus Immersion
Connect with industry experts & develop a professional network at Imarticus’ alumni events


Career Services
Enhance your employability through thorough mock interviews and resume building workshops

Placement Assurance
Guaranteed interview opportunities and placements at leading firms through extensive career support


Smart Classrooms
Learn in technologically-augmented classrooms, enhanced with live lecture recording

Learning Management System
Get access to our state-of-the-art LMS portal to track your learning journey and revisit challenging topics



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Python Basics
Statistics Essentials
Data Analytics with Python
Machine Learning Application
Deep Learning
Computer Vision

Basic of Python

Introduction to Python

  • Python Basics
  • Spyder IDE
  • Jupyter Notebook
  • Floats and Strings
  • Simple Input & Output
  • Variables
  • Single and Multiline Comments

Control Structures

  • Booleans and Comparisons
  • Conditional Statements (IF ELSE)
  • Operator Precedence
  • Lists – Operations and Functions

Functions and Modules

  • Function Arguments
  • Comments and Doc Strings
  • Functions as Objects
  • Modules
  • Standard lib and pip

Exceptions and Files

  • Exception Handling
  • Raising Exceptions
  • Assertions
  • Working with Files

Introduction to Statistics

Basic Probability and Terms

  • Events and their Probabilities
  • Rules of Probability
  • Conditional Probability and Independence
  • Permutations and Combinations
  • Bayers Theorem
  • Descriptive Statistics
  • Compound Probability
  • Conditional Probability

Probability Distributions

  • Types of Distributions
  • Functions of Random Variables
  • Probability Distribution Graphs
  • Confidence Intervals

Data Transformations and Quality Analysis

  • Merge, Transpose and Append
  • Missing Analysis and Treatment
  • Outlier Analysis and Treatment

Exploratory Data Analysis

  • Summarizing and Visualizing the Important Characteristics of Data
  • Hypothesis Testing
  • Visualizations
  • Univariates & Bivariates
  • Crosstabs
  • Correlation


  • Introduction to Pandas
  • IO Tools
  • Basics of NumPy
  • NumPy Functions
  • Pandas – Series and Dataframes

Data Visualization

  • Basics of Data Visualization
  • Line Plots
  • Bar Charts
  • Pie Charts
  • Histograms
  • Scatter Plots
  • Parallel Coordinates

Linear Regression

  • Implementing Simple & Multiple Linear Regression with Python
  • Making Sense of Result Parameters
  • Model Validation
  • Handling other Issues/Assumptions in Linear Regression: Handling Outliers, Categorical Variables, Autocorrelation, Multicollinearity, Heteroskedasticity
  • Prediction and Confidence Intervals

Logistic Regression

  • Implementing Logistic Regression with Python
  • Making Sense of Result Parameters: Wald Test, Likelihood Ratio Test Statistic, Chi-square Test
  • Goodness of Fit Measures
  • Model Validation: Cross Validation, ROC Curve, Confusion Matrix

Decision Trees

  • Implementing Decision Trees using Python
  • Homogeneity
  • Entropy
  • Information Gain
  • Gini Index
  • Standard Deviation Reduction
  • Vizualizing & Prunning a Tree
  • Implementing Random Forests using Python
  • Random Forest Algorithm
  • Important Hyper-Parameters of Random Forest for Tuning the Model
  • Variable Importance
  • Out of Bag Error

Time Series

  • Handling Time Series Data
  • Holt-Winters Model
  • ARIMA Model
  • ACF/PACF Functions

Introduction to Machine Learning

  • Introduction to Machine Learning
  • Machine Learning Modelling Flow
  • How to Treat Data in ML
  • Parametric & Non-Parametric ML Algorithm
  • Types of Machine Learning
  • Performance Measures
  • Bias-Variance Trade-Off
  • Overfitting & Underfitting
  • Bootstrap Sampling
  • Bagging Aggregation
  • Boosting

Optimisation Techniques (2 hours)

  • Constant Learning Rate Procedures
  • Adaptive Learning Procedures
  • Batch Gradient Descent
  • Mini-Batch Gradient Descent
  • Stochastic Gradient Descent
  • Nesterov Accelerated Gradient
  • Root Mean Squared Propagation
  • Adaptive Moment Estimation Procedure

ML Algorithm – Supervised Learning

  • Linear Regression with Stochastic Gradient Descent
  • Logistic Regression with Stochastic Gradient Descent
  • K-Nearest Neighbour
  • Eager Methods vs. Lazy Methods
  • Nearest Neighbor Classification
  • Building kD-Trees
  • Support Vector Machine
  • Perceptron Algorithm

ML Algorithm – Unsupervised Learning

  • What is Clustering?
  • K-means Algorithm
  • Types of Clustering
  • Evaluating K-means Clusters

Ensemble Algorithms

  • Intro to Ensemble Techniques
  • Bootstrap Sampling
  • Bootstrap Aggregation
  • Boosting
  • Stack Generalization
  • Homogeneous Ensemble
  • Heterogeneous Ensemble

SciKit Learn

  • Introduction to SciKit Learn
  • Load Data into Scikit Learn
  • Run Machine Learning Algorithms both for Unsupervised and Supervised Data
  • Supervised Methods: Classification & Regression
  • Unsupervised Methods: Clustering, Gaussian Mixture Models
  • Decide What’s the Best Model for Every Scenario

Neural Networks

  • Understanding Neural Networks
  • The Biological Inspiration
  • Perceptron Learning & Binary Classification
  • Backpropagation Learning
  • Object Recognition


  • Keras for Classification and Regression in Typical Data Science Problems
  • Setting up KERAS
  • Different Layers in KERAS
  • Creating a Neural Network
  • Training Models and Monitoring
  • Artificial Neural Networks


  • Introducing Tensorflow
  • Neural Networks using Tensorflow
  • Debugging and Monitoring
  • Convolutional Neural Networks
  • Unsupervised Learning

Recurrent Neural Network (RNN)

  • Introduction to RNN
  • RNN Network Structure
  • Different Types of RNNs
  • Bidirectional RNN
  • Limitations of RNN

Long Short Term Memory (LSTM)

  • Introduction to LSTM
  • LSTM Architecture
  • Variants on LSTM

Natural Language Processing – I

  • Introduction to NLP
  • Basic NLP Pipeline (add a schematic)
  • Morphology
  • Sentence Segmentation & Tokenization
  • Extracting Tokens – Regular Expression
  • Stemming, Lemmatization
  • Part of Speech – POS
  • Named Entity Recognition (NER)
  • Parsing, Chunking
  • Stop Words Removal (English)
  • Corpora/Corpus
  • Language Modeling – MLE
  • Context Window – Bi-gram, N-gram
  • Applications of NLP

Natural Language Processing – II

  • Deep Learning for NLP
  • RNN for NLP
  • LSTM for NLP

Computer Vision – I

  • Introduction to Computer Vision
  • Deep Learning for Computer Vision
  • ANN & CNN

Computer Vision – II

  • Transfer Learning
  • Object Detection

Career Services

  • Resume Building Workshop
  • Interview Preparation Workshop
  • Mock interviews

Training Methodology

With a strong emphasis on ‘learning by doing’, our programs are developed with the goal of creating well-rounded, job-ready professionals that can add immediate value to any organization.

We have partnered with Coding Ninja to deliver an optimal learning experience. You will start your training on the highly interactive Coding Ninja platform, where you will learn the basics through engaging videos from Machine Learning experts, practice exercises, and TA support.

You will move to a more engaged training structure with live lectures by our expert faculty along with hands-on projects. The lectures and lab sessions are synced to ensure that you get a holistic learning experience.

Hands-on Projects & Case Studies

Our case studies are crafted in association with both, industry leaders and innovative disruptors to expose you to industry trends and how machine learning & analytics experts solve complex business problems.

Real Estate Price Prediction using Linear Regression
Bankruptcy Prediction using Logistic Regression
Identifying Good and Bad Customers for Granting Credit, using Decision Trees
Forecasting the Sale of Furniture of a Superstore, using Time Series
Predicting Term Deposit Subscriptions for a Bank, using Decision Tree and Random Forest
Predicting occurrence of Breast Cancer, using KNN
Predicting House Prices using Real Estate Data, using Linear Regression
Classifying Type of Flower based on Botanical Data, using Logistic regression
Predicting the Close Value of a Financial Firm’s Stock, using Neural Network
Predicting Credit Card Default for a Bank, using SVM
Predicting Brand of a Car by its Specifications, using K-Means Clustering
Calculating the estimated probability of credit default to manage the risk of a bank,using ANN on Keras
Building image classification model to identify hand-written digits in an image, using CNN on TensorFlow
Classifying consumer complaint of a number of products, using RNN
Classifying music in different genres, using LSTM
Sentiment analysis, using NLP
Object detection, using Computer Visions


Upon completing the third semester, eligible* candidates can expect placement opportunities across top tier domestic and global Analytics firms.



Refining and polishing the candidate’s resume with insider tips to help them land their dream job



Preparing candidates to ace HR and Technical interview rounds with model interview questions and answers



Preparing candidates to face interview scenarios through 1:1 and panel mock interviews with industry veterans



Access to all available leads and references from open and private networks on our placement portal



Assured 3-5 interview opportunities at leading Indian and global firms to ensure you get placed

The Career Assistance Services (CAS) team works hand in hand with you from the first placement session during the program launch right until the final mock interviews on course completion. We thoroughly prepare you to be interview-ready and ensure you land your dream job.



Data Scientist


Machine Learning Engineer


Data Analyst


Business Analyst


Data Mining Specialist


Artificial Intelligence Engineer


Machine Learning Architect


Web & Social Media Analyst

“Since I joined Imarticus my life has changed drastically. I feel that I am much more confident in myself and my professional abilities.The thing I like most about Imarticus is the level of comfort and approachability that they provide. Every professor here is always ready to solve your doubts and is prepared to answer all your questions.I have a fantastic job because of Imarticus, and I enjoy going to work every day.”

– Karen Soares linked

“I believe Imarticus Learning is an outstanding institute. Anyone looking forward to kick-start his or her career in Data Analytics or AI needs to go for Imarticus. Their teaching faculty is highly experienced and deliver the knowledge effectively. Not only the curriculum is extensive and informative, but you get to work on the real-world Analytics problems. Whenever any doubts or confusion arises, you will find yourselves accompanied by an experienced faculty to solve the problems.”

– Febin George linked


On completion of this course, aspirants will receive the following certificate:

  • Post Graduate Program in Analytics and Artificial Intelligence by Imarticus Learning


Imarticus Immersion

Imarticus Immersion is an industry-driven networking event that we organize for our students to provide them with an opportunity to:

Network with industry veterans

Gain valuable insights from industry speakers

Connect with Imarticus alumni group

Participate in the batch convocation ceremony


A Program Mentor is the one-point contact for a PG student for query-resolution and support throughout their learning journey with Imarticus.
Our program mentors will help you with:

Academic Assistance

  • Provide unparalleled 1:1 support and guidance
  • Help execute in-class assignments and case studies
  • Discuss and identify learning gaps and offer solutions such as refresher sessions and one-on-one project feedback

Career Assistance

  • Maintain close interaction with students during the career assistance and placements phase of the program
  • Talk you through industry insights and best practices
  • Provide you with interview tips and job search advice

Monitor Progress

  • Set learning goals
  • Discuss your progress status with Trainers and other Industry Mentors on a regular basis to ensure consistent advancement

Industry Advisors

The program is developed in consultation with senior industry experts to ensure a high degree of relevance in accordance to the needs and demands of the industry.



Ex- Target and Genpact




Managing Partner – YDatalytics (Antuit & Y Group)




Founder And CEO, Infinite Analytics, Kyazoonga




Consultant & Sr. Data Scientist – Apple, Maersk


Coding Ninja as Technology Partner

Coding Ninja is our Technology Partner for this course. Students will learn the basics of Python and Statistics on the highly interactive Coding Ninja platform. The student will learn through engaging videos developed by Machine Learning experts from Coding Ninja, practice with multiple exercises, and run their code on the live coding platform.

Once students finish their course content on Coding Ninja and pass the evaluation test, they will be eligible for a certificate of completion from Coding Ninja.


Training Delivery

Engaging videos, multiple practice exercises and training delivered by industry experts



Get personalized support from Training Assistants to resolve your queries quickly

Coding Ninja Platform

Start your training on the highly interactive Coding Ninja platform


The Prodegree is ideal for aspirants and professionals who are interested in working in the analytics industry and are keen on enhancing their technical skills with exposure to cutting-edge practices.


Recent Post Graduates

Bachelors or Masters in Science, Math, Statistics or Computer Applications/IT


Experienced Professionals in Programming or IT

Engineers, Software/IT Professionals & Data Professionals looking to up-skill or change career paths


Individuals Looking for Global Certifications

To enhance their resumes & build a portfolio of demonstrable work


Recent Post Graduates

Bachelors or Masters in Science, Math, Statistics or Computer Applications/IT


Experienced Professionals in Programming or IT

Engineers, Software/IT Professionals & Data Professionals looking to up-skill or change career paths


Individuals Looking for Global Certifications

Those looking to enhance their resumes & build a portfolio of demonstrable work

Eligibility: Candidates should have basic knowledge of Programming and Mathematics
Fees: Classroom: र 2,20,000/- and Online Instructor led: र 1,50,000/-


Y Laxmi Prasad

Python, ML, Deep Learning and R Programming


Vinay Borhade

Python, ML, Deep Learning and R Programming


Harish Kasiviswanathan

Senior Data Scientist, DXC Technology


Sitaram Ramachandrula

Master Data Scientist, DXC Technology




What is the format of the program?
The PG in Analytics and Artificial Intelligence is a 7-month program which starts with self-paced primers on Python and Statistics. After you finish these two modules, you start with live instructor-led training. Every module has multiple lab sessions (to work on real-world projects) to ensure you learn implementation of Machine Learning, Deep Learning, NLP, and Computer Vision. You will work on a Capstone project in the end to reinforce your learning.
The course is available in Classroom as well as Online live training formats.

What are the prerequisites?
You should be a Graduate with at least 60% marks, and have basic understanding of Mathematics and Programming.
Which tools will be covered in the program?
This program covers the most in-demand tools across the Analytics and Artificial Intelligence domain such as Python, Keras and Tensorflow.
What certificate will I receive at the end of the program?
On completion of the course, aspirants will receive certificate:
Post Graduate Program in Analytics and Artificial Intelligence by Imarticus Learning
Does this program offer live lectures?
Yes, most of the modules will be delivered through live instructor-led sessions by Machine learning and AI experts. There will be a high level of engagement throughout the program.
What are the fees for the AI and Analytics program?
The Analytics & Artificial Intelligence Program is priced at INR 2,20,000 for the classroom mode, and INR 1,50,000 for the online mode. You can pay by Credit card, Debit Card or Net banking from all leading banks to the nearest Imarticus centre, or online. In the event you are unable to pay, please contact our toll-free number 1-800-267-7679 for further assistance.

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