Advanced Certificate course in

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PRICE STARTS - ₹2999/-

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Average CTC

18 LPA

Users Enrolled


Batch Starts


Program Duration

4 Months




Learning Path


Introduction to Data Science


Python for Data Science


Machine Learning


Data Visualization


Artificial Intelligence & Machine Learning






Course Content

Introduction to Python

  • Features, the advantages of Python over other programming languages · Python installation – Windows, Mac & Linux distribution for Anaconda Python

  • Deploying Python IDE

  • Basic Python commands, data types, variables, keywords and More

Basic Steps

  • Built-in data types in Python

  • Learn classes, modules, Str(String),

  • Basic operators, comparison, arithmetic, slicing and slice operator, logical etc

  • Loop and control statements while, for, if, break, else, continue


  • OOP concepts in Python · How to write OOP concepts program in Python Connecting to a database · Classes and objects in Python · OOPs paradigm, important concepts in OOP like polymorphism, inheritance, encapsulation · Python functions, return types and parameters · Lambda expressions


    NumPy for mathematical computing
  • Introduction to arrays and matrices

  • Broadcasting of array math, indexing of array

  • Standard deviation, conditional probability, correlation and covariance.

Data Visualization

    Matplotlib for data visualization
  • How to plot graph and chart with Python

  • Various aspects of line, scatter, bar, histogram,etc

  • Seaborn


  • Pandas for data analysis and machine learning

  • Introduction to Python dataframes

  • Importing data

  • Various data operations like selecting, filtering, sorting, viewing, joining, combining

Exceptions and Errors

    Exception Handling
  • Introduction to Exception Handling

  • Scenarios in Exception Handling with its execution

  • Arithmetic exception

  • RAISE of Exception

Introduction to Artificial Intelligence and Machine Learning

    By the end of this lesson, you will be able to:
  • Define Artificial Intelligence (AI) and understand its relationship with data

  • Understand machine learning approach

  • Identify the applications of machine learning

  • Identify the applications of machine learning

Data Wrangling and Manipulation

    By the end of this lesson, you will be able to:
  • Demonstrate data import and exploration using Python

  • Demonstrate different data wrangling techniques and their significance

  • Perform data manipulation in python

Supervised Learning

    By the end of this lesson, you will be able to:
  • Understand the different types of supervised learning

  • Build various regression models

Supervised Learning-Classification

    By the end of this lesson, you will be able to:
  • Understand classification as part of supervised learning

  • Demonstrate different classification techniques in Python

  • Evaluate classification models

Unsupervised learning

    By the end of this lesson, you will be able to:
  • Explain the mechanism of unsupervised learning

  • Practice different clustering techniques in Python

Machine Learning Pipeline Building

  • Machine learning automation using ML Pipelines

Decision Tree Analysis and Ensemble Learning

    By the end of this lesson, you will be able to:
  • Decision Tree algorithms

  • Explain ensemble learning

  • Models in ensemble learning

  • Evaluate the performance of bagging and boosting models

AI and Deep learning introduction

  • What is AI and Deep learning
  • Brief History of AI
  • Recap: SL, UL and RL
  • Deep learning : successes last decade
  • Demo & discussion: Self driving car object detection
  • Applications of Deep learning
  • Challenges of Deep learning
  • Fullcycle of a deep learning project
  • Key Takeaways
  • Knowledge Check

Artificial Neural Network

  • Biological Neuron Vs Perceptron
  • Shallow neural network
  • Training a Perceptron
  • Backpropagation
  • Role of Activation functions & backpropagation
  • Demo code: Backpropagation (Assisted)
  • Demo code: Activation Function (Unassisted)
  • Optimization
  • Regularization
  • Dropout layer
  • Key Takeaways
  • Knowledge Check
  • Lesson-end Project (MNIST Image Classification)

Deep Neural Network & Tools

  • Deep Neural Network : why and applications
  • Designing a Deep neural network
  • How to choose your loss function?
  • Tools for Deep learning models
  • Keras and its Elements
  • Tensorflow and Its ecosystem

Deep Neural Net optimization, tuning, interpretability

  • Optimization algorithms
  • SGD, Momentum, NAG, Adagrad, Adadelta , RMSprop, Adam
  • Batch normalization
  • Demo Code: Batch Normalization (Assisted)
  • Exploding and vanishing gradients
  • Hyperparameter tuning
  • Interpretability
  • Key Takeaways
  • Knowledge Check
  • Lesson-end Project: Hyperparameter Tunning With Keras Tuner

Convolutional Neural Net

  • Success and history
  • CNN Network design and architecture
  • Demo code: CNN Image Classification (Assisted)
  • Deep convolutional models
  • Key Takeaways
  • Knowledge Check
  • Lesson-end Project: Image Classification

Recurrent Neural Networks

  • Sequence data
  • Sense of time
  • RNN introduction
  • LSTM
  • Key Takeaways
  • Knowledge Check
  • Lesson-end Project: Stock Price Forecasting and Sentiment Analysis using LSTM

Overfit and underfit

  • explore several common regularization techniques, and use them to improve on a classification model.

Transfer Learning

  • deriving representations from a previous network to extract meaningful features from new samples

Working with Generative Adversarial Networks

  • Introduction to Generative Adversarial Networks

  • Generative vs. Discriminative Algorithms

  • Architectural Overview

  • Basic building block – generator

  • Basic building block – discriminator

  • Types of GANs

  • Introduction to Deep Convolutional GANs (DCGAN)


  • Image classification in ANN and CNN



Data Science

Get opportunity to work as a data scientist with our Advanced Certificate Course in Data Science. This Course is a 100% Placement Guarantee Program and is designed with market oriented needs. The curriculum is designed by Best in the business and suits the current needs of the market. You will also have an induction to Industry tools and Live Projects designed by Experts from Global companies

Why Data Science?

With the aid of a data science education, a career in data science will progress swiftly while also providing you with the top-notch training and skills you need to succeed. In-depth training in the most in-demand Data Science and Machine Learning skills is provided in this course, along with hands-on practice with crucial tools and technologies including Python, R, Tableau, and machine learning principles.

Understand Data Science

" Data science is the study of data with the intent of deriving important business insights. It is a multidisciplinary method for analyzing massive volumes of data that integrates ideas and techniques from the domains of mathematics, statistics, artificial intelligence, and computer engineering. "

Who this program is for?

This course is for freshers or intermediate business or technology people who want to continue or start their career in Data Science


Roles After The Course

Data Analyst

Data Engineer

Technical Specialist

Product Manager

Data Scientist

Software Engineer


2999 /-

  • Learning Video
  • Live Interactive session
  • Live Project
  • Case Studies
  • Reading essentials
  • Placement assistance
  • Internship opportunity
  • Learning Module: 20 Hours



119999 /-

  • Learning Video
  • Live Interactive session
  • Live Project
  • Case Studies
  • Reading essentials
  • Placement assistance
  • Internship opportunity
  • Call assistance from experts before Interview
  • Mock Interview
  • Personalized Mentoring
  • Industry tools training
  • Industry Specific training
  • Paid Internship
  • Exclusive access to premium group consisting of 500 different domain experts
  • Special Projects
  • Placement Guaranteed Service
  • Learning Module: 160 Hours

Amol Ghatole

Data Scientist, Unique Education

I was in a completely different field before taking a data science course, but the course helped me make a successful transition into the field of data science. The course provided a thorough introduction to key concepts and tools used in the field, including statistics, machine learning, and programming.

Ajit Thorat

Director, Community for Sustainable Development

I took a data science course to improve my skills and advance my career, and it exceeded my expectations. The course covered a wide range of topics, including statistical analysis, machine learning, and programming, and provided hands-on experience with real-world projects.

Vaibhav Pawar

Director, COMPASS, a research and consulting firm

The data science course not only provided me with the skills and knowledge to excel in my field, but it also gave me the confidence and inspiration to start my own company. Using the insights and techniques I learned in the course; I was able to identify a gap in the market and establish a successful data-driven business.


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