Advanced Certification in
Data Science & AI

No Prior Programming Knowledge Required

Get hands-on experience in Artificial Intelligence & Machine Learning. Learn from Industry masters along with 1-to-1 mentorship.

digital marketing course online

Advanced Certification in
Data Science & AI

No Prior Programming Knowledge Required

Get hands-on experience in Artificial Intelligence & Machine Learning. Learn from Industry masters along with 1-to-1 mentorship.

limited seats

Only 15 Seats/Batch

Live online class

12 hours per week

9 Months

8 Months Class + 1 Month Industry Capstone Project

100% placement

100% Placement Guarantee & 150+ Hiring Partners


EMI starts @ Rs.5,625/-

limited seats

15 Seats/Batch

Live Online class

12 Hours per Week

9 Months

8 Months Class + 1 Month Industry Capstone Project

100% placement

100% Placement Guarantee & 150+ Hiring Partners


EMI starts @ Rs.5,625/-

Industry Trends

"An average salary of a Junior Data Scientist in India is Rs. 7,15,042/Year as of 31st December 2021"

Course Eligibility

This comprehensive, Advanced Certification in Data Science & AI, program is created in collaboration with leading subject matter experts is ideal for freshers with a Mathematics background, Software Professionals, Number crunchers, Finance, Marketing professionals interested to learn from the numbers. Technical Managers, Executives, anyone who is willing to play with Numbers.


We’ve packed tons of information into our classroom-style learning. Our intimate setting allows us to learn about you and cater examples, strategies, and concepts accordingly. Over the years, we’ve perfected the approach with current trends, to make sure that you have everything you need to be successful.

Rank #4

Digital Education Shcool in Asia

Rank #1

Online bootcamp with world-class facilites


track-record in Placements

best in class

We have the best mentors


We have the best rating in the industry

Meet our Top Faculties

Sankar Narayanan - Best Digital Marketing trainer in India

Mr. Sankara Narayanan

18+ Years of Experience

M.Tech, M.B.A., AFRM (Ph.D.). An IIM Kashipur alumnus.

Hitesh Motwani - Best Digital Marketing trainer in India

Mr. Hitesh Motwani

10+ Years of Experience

Visiting Faculty in 4 IIMs (Bangalore, Udaipur, Shillong, Sambalpur), University of London, & many more

Sachin Goel - Best Digital Marketing trainer in India

Mr. Sachin Goel

10+ Years of Experience

Sr. Lead – Data & Analytics, Boston Consulting Group, USA. An alumnus of IIM, Bangalore & IMT, Ghaziabad

Our Curriculum

Our Data Science course curriculum is not only designed what to teach also how best to teach

Goal – Get an introduction to Data Science in this Module and see how Data Science helps to analyze large and unstructured data with different tools.

Objectives – At the end of this Module, you should be able to:

  • Define Data Science
  • Discuss the era of Data Science
  • Describe the Role of a Data Scientist
  • Illustrate the Life cycle of Data Science
  • List the Tools used in Data Science
  • State what role Big Data and Hadoop, R, Spark and Machine Learning play in Data Science


  • What is Data Science?
  • What does Data Science involve?
  • Era of Data Science
  • Business Intelligence vs Data Science
  • Life cycle of Data Science
  • Tools of Data Science
  • Introduction to R
  • Introduction to Machine Learning

Goal – In this Module, you should learn about different statistical techniques and terminologies used in data analysis.

Objectives – At the end of this Module, you should be able to:

  • Define Statistical Inference
  • List the Terminologies of Statistics
  • Illustrate the measures of Center and Spread
  • Explain the concept of Probability
  • State Probability Distributions


  • What is Statistical Inference?
  • Terminologies of Statistics
  • Measures of Centers
  • Measures of Spread
  • Probability
  • Normal Distribution
  • Binary Distribution
  • Central Limit theorem

Goal – Discuss the different sources available to extract data, arrange the data in structured form, analyze the data, and represent the data in a graphical format.

Objectives – At the end of this Module, you should be able to:

  • Discuss Data Acquisition techniques
  • List the different types of Data
  • Evaluate Input Data
  • Explain the Data Wrangling techniques
  • Discuss Data Exploration


  • Data Analysis Pipeline
  • What is Data Extraction
  • Types of Data
  • Raw and Processed Data
  • Exploratory Data Analysis
  • Visualization of Data


  •       Loading different types of dataset in R 
  •       Arranging the data 
  •       Plotting the graphs

Goal – Get an introduction to Machine Learning as part of this Module. You will discuss the various categories of Machine Learning and implement Supervised Learning Algorithms.

Objectives – At the end of this module, you should be able to:

  • Define Machine Learning
  • Discuss Machine Learning Use cases
  • List the categories of Machine Learning
  • Illustrate Supervised Learning Algorithms


  • What is Machine Learning?
  • Machine Learning Use-Cases
  • Machine Learning Process Flow
  • Machine Learning Categories
  • Supervised Learning (Linear Regression & Logistic Regression)


  • Implementing Linear Regression model in R
  • Implementing Logistic Regression model in R 

Goal – In this module, you should learn the Supervised Learning Techniques and the implementation of various Techniques, for example, Decision Trees, Random Forest Classifier etc. 

Objectives – At the end of this module, you should be able to:

  • Define Classification
  • Explain different Types of Classifiers such as Decision Tree, Random Forest, Naïve Bayes Classifier, Support Vector Machine…etc


  • What is Classification and its use cases?
  • What is Decision Tree?
  • Algorithm for Decision Tree Induction
  • Creating a Perfect Decision Tree
  • Confusion Matrix
  • What is Random Forest?
  • What is Navies Bayes?
  • Support Vector Machine: Classification


  • Implementing Decision Tree model in R
  • Implementing Linear Random Forest in R
  • Implementing Navies Bayes model in R
  • Implementing Support Vector Machine in R

Learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data.

Objectives – At the end of this module, you should be able to:

  • Define Unsupervised Learning
  • Discuss the following Cluster Analysis (K – means Clustering, C – means Clustering, & Hierarchical Clustering)


  • What is Clustering & its Use Cases?
  • What is K-means Clustering?
  • What is C-means Clustering?
  • What is Canopy Clustering?
  • What is Hierarchical Clustering?


  • Implementing K-means Clustering in R
  • Implementing C-means Clustering in R
  • Implementing Hierarchical Clustering in R

In this module, you should learn about association rules and different types of Recommender Engines.

Objectives – At the end of this module, you should be able to:

  • Define Association Rules
  • Define Recommendation Engine
  • Discuss types of Recommendation Engines (Collaborative Filtering & Content-Based Filtering
  • Illustrate steps to build a Recommendation Engine


  • What is Association Rules & its use cases?
  • What is Recommendation Engine & it’s working?
  • Types of Recommendation Types
  • User-Based Recommendation
  • Item-Based Recommendation
  • Difference: User-Based and Item-Based Recommendation
  • Recommendation Use-case


  • Implementing Association Rules in R
  • Building a Recommendation Engine in R

Discuss Unsupervised Machine Learning Techniques and the implementation of different algorithms, for

Objectives – At the end of this module, you should be able to:

  • Define Text Mining
  • Discuss Text Mining Algorithms (Bag of Words Approach & Sentiment Analysis)


  • The concepts of text-mining
  • Use cases
  • Text Mining Algorithms
  • Quantifying text


  • Implementing Bag of Words approach in R
  • Implementing Sentiment Analysis on twitter Data using R

In this module, you should learn about Time Series data, different component of Time Series data, Time Series modelling – Exponential Smoothing models and ARIMA model for Time Series forecasting.

Objectives – At the end of this module, you should be able to:

  • Describe Time Series data
  • Format your Time Series data
  • List the different components of Time Series data
  • Discuss different kind of Time Series scenarios 
  • Choose the model according to the Time series scenario
  • Implement the model for forecasting
  • Explain working and implementation of ARIMA model
  • Illustrate the working and implementation of different ETS models
  • Forecast the data using the respective model


  • What is Time Series data?
  • Time Series variables
  • Different components of Time Series data
  • Visualize the data to identify Time Series Components
  • Implement ARIMA model for forecasting
  • Exponential smoothing models
  • Identifying different time series scenario based on which different Exponential Smoothing model can be applied
  • Implement respective ETS model for forecasting


  •       Visualizing and formatting Time Series data
  •       Plotting decomposed Time Series data plot
  •       Applying ARIMA and ETS model for Time Series forecasting
  •       Forecasting for given Time period
  1. Basics of Python for Data Analysis
    • Why learn Python for data analysis?
    • Python 2.7 v/s 3.4
    • How to install Python?
    • Running a few simple programs in Python
  2. Python libraries and data structures
    • Python Data Structures
    • Python Iteration and Conditional Constructs
    • Python Libraries
  3. Exploratory analysis in Python using Pandas
    • Introduction to series and data frames
  4. Data Mugging in Python using Pandas
  5. Building a Predictive Model in Python
    • Logistic Regression
    • Decision Tree
    • Random Forest

Objectives of this module:

  • To make the learner familiar with the basic principles of AI,
  • Capable of using heuristic searches
  • Aware of knowledge based systems
  • Able to use fuzzy logic and neural networks
  • Learn various applications domains AI

Foundations of AI

  • Applications of AI
  • Fundamentals of AI.
  • Neural Network Foundations

Introduction to Artificial Neural Networks

  • The Detailed ANN
  • The Activation Functions
  • How do ANNs work & learn
  • Gradient Descent
  • Stochastic Gradient Descent
  • Backpropagation
  • Understand limitations of a Single Perceptron
  • Understand Neural Networks in Detail
  • Illustrate Multi-Layer Perceptron
  • Understand How Deep Network Works?
  • How Backpropagation Works?
  • Illustrate Forward pass, Backward pass
  • Different variants of Gradient Descent

Convolutional Neural Networks

  • Image classification
  • Text classification
  • Image classification and hyper-parameter tuning
  • Emerging NN architectures

What are RNNs – Introduction to RNNs

  • Recurrent neural networks rnn
  • LSTMs understanding LSTMs
  • long short term memory neural networks lstm in python

Tensorflow with Python

  • Introducing Tensorflow
  • Why Tensorflow?
  • What is tensorflow?
  • Tensorflow as an Interface
  • Tensorflow as an environment
  • Tensors
  • Computation Graph
  • Installing Tensorflow
  • Tensorflow training
  • Prepare Data
  • Tensor types
  • Loss and Optimization
  • Running tensorflow programs

Building Neural Networks using Tensorflow

  • Tensors
  • Tensorflow data types
  • CPU vs GPU vs TPU
  • Tensorflow methods
  • Introduction to Neural Networks
  • Neural Network Architecture
  • Linear Regression example revisited
  • The Neuron
  • Neural Network Layers
  • The MNIST Dataset
  • Coding MNIST NN

Deep Learning using Tensor flow

  • Deepening the network
  • Images and Pixels
  • How humans recognise images
  • Convolutional Neural Networks
  • ConvNet Architecture
  • Overfitting and Regularization
  • Max Pooling and ReLU activations
  • Dropout
  • Strides and Zero Padding
  • Coding Deep ConvNets demo
  • Debugging Neural Networks
  • Visualising NN using Tensorflow
  • Tensorboard
  • Auto-encoders and unsupervised learning
  • Stacked auto-encoders and semi-supervised learning
  • Regularization – Dropout and Batch normalization


Introduction to Data Visualization

  • What is Data Visualization? 
  • How does it work? 
  • Why is it important?
  • Important types of Data Visualizations? 
  • Tools to make visualizations 
  • Which industry uses it?
  • Case Study

Data Analytics in Excel

  • What is Data Analytics in Excel? 
  • How does it work? 
  • Why is it important? 
  • Important types of Data 
  • Tools used for Data Analytics in Excel 
  • Which industry uses it 
  • Case Study


  • Introduction to Tableau 
  • How to connect to Data? 
  • Loading Data on Tableau 
  • Loading Workbooks on Tableau 
  • Navigating Tableau 
  • Interactive Elements in Tableau 
  • Dimensions and Measures in tools 
  • Building your first visualization 
  • Improving your first visualization 
  • Bringing it all together

Power BI

  • Introduction to BI 
  • Why Power BI is important today? 
  • Key benefits of using Power BI 
  • Flow of Power BI 
  • Components of Power BI 
  • Architecture of Power BI 
  • Building Blocks of Power BI 
  • Data sources in Power BI Desktop 
  • Connecting to a Data Source 
  • Combining Data – Merging & Appending 
  • Cleaning Irregularly formatted data 
  • How to Perform Modelling of Data? 
  • Optimizing Data Models

Programming Languages and Tools Covered

These are the 8+ Programming Languages and Tools covered during this online course, Advanced Certification in Data Science & Artificial Intelligence (AI). 

Copy of Facebook Ads Manager

Course Certificate

Once you successfully complete the course, Advanced Certification in Data Science & AI, NIDE will provide you with an industry-recognized course completion certificate.

Advanced Certification in Data Science & AI

100% Placement Guarantee

Getting a job is very important for all after completing the course, Advanced Certification in Data Science & AI. We have 100% Placement Guarantee. We are associated with 150+ Hiring partners and also we have a dedicated team who helps you to get the right placement. 

Data Science Brochure NIDE 1

Training Schedules

Plan out your schedule and determine your preferred timeline for completing this online course, Advanced Certification in Data Science.

10-12 Hours per Week

Monday – Friday (Weekly 3-5 classes)

Timings: 8.00 pm – 9.30 pm

Application closes on: 3rd June 2024

Admission: – 15 Seats/Batch

Duration: – 9 Months (8 Months Live Classes + 1 Month Capstone Project )

Course Fee

Instructor-led live online classroom training by top data science industry experts and instructors.

(15 Seats/Batch only)

Rs. 75,000/- (Incl. Taxes)


EMI facility is available. No Credit Card Required. Contact +919663334449 for any EMIs/Fees-related inquiry.

I am interested! Plz contact me

Register for a Free Demo Class

It’s always better to understand what we teach during 9 months course. Join the 1-Hour Free Demo class, ask your questions, clear your doubts, and take your final decision.

Support Team

Mob: +919663334449
Whatsapp: +919663334449
Mon - Fri: 10 AM - 6 PM
Saturday: 10 AM - 2 PM

Sunday & Govt Holidays are OFF


Download the Brochure


Let's have a chat

Download Brochure