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Data Science Certification

  • Get acquainted with one of the most in-demand skills of Data Science from scratch with Python.
  • Implement over 35+ diverse real-world projects in Data Science.
  • Make a trip to the Data Science pipeline from data preparation to model performance optimization.
  • Comprehend data-specific algorithms in Supervised, Unsupervised, and Reinforcement Learning.
  • Gamified learning for visual understanding and prolonged retention of concepts.
  • Explore the potential of machines with the help of Neural Networks, NLP, and Computer Vision models.
Data Science Certification


Real-world projects


Comprehensive and organized modules

70+ hours

Self-paced learning with hands-on coding

Data Science Certification

This course covers a wide range of topics in Data Science such as Data Wrangling, Data Analysis, Machine learning, Probability, Statistics, etc. You can enhance the capabilities of machines to imitate human-like behavior through Deep Learning, Computer Vision, and NLP modules. At the end of the last module, two capstone projects will allow you to implement your overall learning and solve real-world Data Science problems. We have broken down the mathematics behind the algorithms to simplify your learning experience.

  • Self-paced learning

  • 35+ Real-world projects

  • 2 Capstone Projects

  • 12 Comprehensive and organized modules

  • Interactive Games

  • Beginner Friendly

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Sample Certification
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Why This Course?


Data Science offers various job profiles like Data Scientist, Data Engineer, Data Analyst, and Machine Learning Engineer. According to Indeed, entry-level Data Scientists earn up to $101,571 per year,while experienced Data Scientists earn up to $138,397.

Job Aspects

Data Science is a buzzword for the 21st century, and the number of jobs in this field is increasing tremendously. According to the U.S. Bureau of Labor Statistics, there will be over 11.5 Million job opportunities in Data Science by 2026. Amazon, Facebook, Apple, and Microsoft are some of the top firms that are actively hiring Data Scientists.

Importance of Data Science

Harvard Business Review (HBR) asserts Data Scientist as 'the sexiest job of the 21st Century '. Data Science combines the significant knowledge of programming and mathematics to provide insights that drive modern business processes. Data science allows the user to submit an incredible quantity of data into algorithms, and makes predictions based exclusively on the input.

Why AI Probably?

Gamified Experience

Gamified approach for the understanding of concepts and memory retention based on Cognitive Neuroscience

Real-World Projects

Get hands-on experience on different real-world problems with an in-depth explanation

State-of-the-Art LMS

A modular LMS designed with an easy-to-navigate user interface

Taught by Industry Experts

Get on-demand video lectures and learn from renowned industry experts

Number of hours

Get more than 70 hours of explicit content in the form of lectures and code-along videos

24 x 7 Learning Support

Get learning support at any point of time during your enrolment in the course


Download Curriculum

Learning Objectives

Introduction to Python programming fundamentals, Object-oriented programming, and data analytics using NumPy, Pandas, and Matplotlib.


  • Data Types & Data Structures
  • Conditional Structures
  • Loops
  • Functions
  • OOPS
  • NumPy
  • Pandas

Learning Objectives

Introduction to Databases, SQL queries, and reformatting the data to make it a useful input for the data exploring phase.


  • PostgreSQL basics
  • CRUD operations
  • Clauses
  • Constraints
  • Joins
  • Aggregate Functions
  • String operations

Learning Objectives

Learn the mathematical concepts that drive data science


  • Charts
  • Distributions
  • Variation
  • Correlation
  • Hypothesis testing
  • Probability

Learning Objectives

Engineer the features of data and explore multiple methods to scale up or down its effect on the analysis.


  • Feature Scaling
  • Handling missing and null values
  • Encoding
  • Feature Transformation
  • Feature Selection
  • Handling imbalanced dataset

Learning Objectives

Explore, enact and comprehend various algorithms in supervised machine learning.


  • Regression models
  • Classification models
  • Testing and Training data
  • Multicollinearity
  • Model evaluation techniques

Learning Objectives

Focus more on the models and let the machines learn without human intervention with unsupervised and reinforcement learning.


  • Clustering and Classification
  • Partition clustering
  • Hierarchical Clustering
  • Reinforcement learning and applications
  • Q learning
  • Recommendation systems

Learning Objectives

Create models that work upon time series and derive better predictions with time series algorithms.


  • Basics of Time Series and Forecasting
  • Linear model
  • Exponential Smoothing Methods
  • Autoregression
  • ARIMA models

Learning Objectives

Explore Deep Neural Networks with the help of PyTorch.


  • Fundamentals of PyTorch
  • Forward and Backward Propagation
  • ANN, CNN, and RNN
  • GRU and Applications

Learning Objectives

Learn to extract visual information and allow machines to imitate the optical systems of humans.


  • Face application
  • OCR
  • Object Classification
  • Identification verification
  • Image segmentation and semantic segmentation cv in healthcare
  • Working with videos
  • Image search engine

Learning Objectives

Recognize how computers understand and comprehend the human language.


  • NLP Applications
  • Text Processing
  • Feature Extraction Techniques
  • Python Packages for NLP
  • Topic Modeling
  • Pre-trained Word Embeddings

Learning Objectives

Use Transfer Learning to improve the efficiency of a new model by transferring knowledge from a pre-learned model.


  • What is Transfer Learning
  • What, When and How to do Transfer Learning
  • Inductive Bias
  • Types of Transfer Learning in Deep Learning

Learning Objectives

Learn about the best standard practices of the Software Engineering industry to enable modularity, simplicity, and readability of the code.


  • Coding Practices
  • Documentation
  • Testing and Debugging
  • Logging
  • Version control

Tools and
Technologies covered

Frequently Asked Questions

All you need is a stable internet connection and a laptop to stream the video classes. The course starts with the most basic concepts of Python.

Your system should have a minimum of 4 GB RAM. You can also run your programs in Google Colab or Kaggle Notebooks.

You will be able to build analytic and predictive models in your daily work and develop programs of a faster magnitude. You will also be able to optimize the performance of your algorithms effectively.

Whether you are a student or a professional, the knowledge you gain from the course will open a bundle of opportunities, in terms of job and amazing problem-solving skills.

Our payment gateway supports a wide array of payment options. You can use any online mode viz. Netbanking, Credit Card, Debit Card, UPI, or Wallets.

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