Overview

Data Science Architect Master’s Certification Training

Master Data Science and Machine Learning skills with this industry-oriented training program designed by experts after extensive research. Hands-on training on Python, R, Sklearn, TensorFlow, Keras, SAS, Tableau, and more.

Key Features

  • 35+ Projects and case studies

  • 180+ Hours of interactive learning

  • 140+ Hours of exercise and project work

  • Lifetime access to LMS

  • Attend as many batches for lifetime

  • 24/7 Technical Support

  • Resume Building

  • Placement Assistance

  • Dedicated Learner Delight Team

Python, R, Tableau, SAS, TensorFlow, Keras, Data Science, Machine Learning Algorithm, Time Series Analysis, Natural Language Processing, Deep Network, more.


The landscape of Data Science is projected to double its size by the year of 2025 (in 2019 it was 3.03 billion.) Indian government has decided to invest Rs 3,063 crore or $477 Million in the field of AI, ML, and 3-D printing. The salaries of Data Scientists in India and in the US are 8 lacs/yr. and $124k/yr. respectively. The salaries of AI Engineers in India and in the US are 7.5 lacs/yr. and $111k/yr. respectively. So, this is the perfect time to get a Master’s certification on the same.


Anyone willing to build a cloud computing career in Data Science.


No prerequisite. We teach everything from scratch.


Hiring Companies

Source : Indeed

Course Content


Program Syllabus

Course 1: Data Science with R

An industry-oriented course designed by experts. Become a Data Scientist by mastering concepts like data collection, manipulation, analysis, statistical methods, machine learning, and more.

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  • ● SAS Vs R Vs Python
  • ● Business objectives
  • ● Key driving factors in the analytics world


  • ● How Data Analysis is helpful in the Sales Industry
  • ● R studio
  • ● Graphical User Interfaces (GUI)
  • ● Installing R studio
  • ● Install essential packages along with different GUIs


  • ● Installing R studio and its essential packages along with different GUIs
  • ● Data Structure
  • ● Data Types
  • ● Vectors
  • ● Matrices
  • ● Factors
  • ● Data frames
  • ● Lists
  • ● Importing Data
  • ● Connecting to database
  • ● Exporting Data
  • ● Viewing partial data and full data
  • ● Variable & Value Labels: Date Values


  • ● We will go through a Case Study on HR Analytics
  • ● Variables to perform calculations & binning
  • ● Operators and using multiple operators
  • ● Built-in Functions & User-Defined Functions
  • ● Control Structures
  • ● Conditional statements
  • ● Loops
  • ● Functions
  • ● Sorting, Merging and Appending Data
  • ● Aggregating/summarizing Data
  • ● Reshaping & Subsetting Data
  • ● Data Type Conversions
  • ● Sampling
  • ● File preparation
  • ● Aggregation
  • ● Merging
  • ● Appending
  • ● Type conversion
  • ● Renaming and formatting data
  • ● Handling Duplicates/Missing values


  • ● Creating new variables to perform calculations & binning
  • ● Working with operators
  • ● Working with conditional statements
  • ● Using loops
  • ● Using functions
  • ● Sorting, merging, and appending data
  • ● Use case to learn about aggregating/summarizing Data
  • ● Use case to learn about reshaping & Subsetting Data
  • ● Use case to learn about data Type Conversions
  • ● Use case to learn about sampling
  • ● Learning to implement file preparation, Aggregation, Merging, and Appending
  • ● Working on type conversion, Renaming, and formatting data
  • ● Handling Duplicates/Missing values
  • ● Case study on Descriptive Analytics based-on Industrial based problem statements
  • ● Creating Interactive Graphs on R using packages like
  • ● GGPLOT, GGPLOT2, and PLOTLY
  • ● Working with Histograms & Density Plot
  • ● Dot Plots
  • ● Bar Plots
  • ● Line Charts
  • ● Pie Charts
  • ● Box Plots
  • ● Scatterplots


  • ● Creating visualization on top of a large dataset using packages like GGPLOT, GGPLOT 2 and PLOTLY and visualizing various attributes from the relevant rows.
  • ● Working with Histograms & Density Plot, Dot Plots, Bar Plots, Line Charts, Pie Charts, Boxplots, Scatterplots
  • ● Exploratory Data Analysis (EDA)
  • ● Understanding the spread and data points
  • ● Understanding the sourced data for better analysis


  • ● Working with a Case Study on Exploratory Data Analysis in R using an Industrial-based problem statement
  • ● Understanding more about Analytics World
  • ● Data Science Vs Data Analytics Vs ML Vs AI Vs Business Analysis
  • ● Analytics keywords and their definitions
  • ● Business Objectives
  • ● Key driving factors in Analytics world


  • ● Case Study on how Predictive Analysis is helpful for Sales Industry
  • ● Types of Business problems
  • ● Mapping of Techniques
  • ● Different Phases of Predictive Modeling
  • ● EDA - Exploratory Data Analysis and Need of Data preparation
  • ● Data Preparation
  • ● Performing Data Preparation steps
  • ● Consolidation/aggregation
  • ● Outlier treatment
  • ● Flatliners
  • ● Missing values
  • ● Dummy Creation
  • ● Variable Reduction
  • ● Data Alignment and fine-tuning
  • ● Cluster and Segmentation in R
  • ● Working with various Behavioral Segmentation Techniques
  • ● K-Means Cluster Analysis in R
  • ● Heuristic Segmentation Techniques
  • ● Value-Based, RFM Segmentation
  • ● Life Stage Segmentation


  • ● Performing Data Preparation
  • ● Case Study on Segmentation Modeling
  • ● Implementing Decision Tree model in R
  • ● Understanding steps to perform the Classification based on inferences on Decision Tree
  • ● Extensive standard R Packages and Functions


  • ● Hands-on & Case-Study on Decision Tree Modeling Problem Statements
  • ● Assumptions of Linear Regression & Logistic Regression
  • ● Linear & Logistic Regression
  • ● Use of Linear & Logistic Regression Model


  • ● Linear & Logistic Regression Analysis
  • ● Building Linear & Logistic Regression Model
  • ● What is Time Series Data?
  • ● Different components of Time Series data
  • ● Visualize the data to identify Time Series Components


  • ● Working on a Case Study of Time Series and Arima Model
  • ● Implement ARIMA model for forecasting
  • ● Understanding differences in Statistical learning vs. Machine learning
  • ● Understanding essential classes of Machine Learning Algorithms: Supervised vs Unsupervised Learning
  • ● Text Mining and Sentiment Analysis in R


  • ● Performing Machine learning on Sentiment Analysis

Course 2: Python for Data Science

An industry-oriented course designed by experts. Become a Data Scientist by mastering Python programming and concepts of Data Science as well as Machine Learning.

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  • ● Introduction to Python
  • ● Features of Python
  • ● Advantages of using Python
  • ● Companies using Python
  • ● Installation process of Python
  • ● Basic commands of Python


  • ● Installing Python Anaconda for Windows, Linux, and Mac
  • ● Writing a “Hello World Program”
  • ● Python Data Types
  • ● Numbers
  • ● Simple arithmetic operations in Python
  • ● Assigning Variables in Python
  • ● Operators in Python
  • ● Strings
  • ● Indexing, slicing, and formatting
  • ● Lists
  • ● Tuples
  • ● Sets
  • ● Boolean
  • ● Dictionaries


  • ● Adding, Subtracting, Multiplying and Dividing numbers using arithmetic operations
  • ● Creating a list with multiple distinct and duplicate elements
  • ● Accessing and removing the elements from a list
  • ● Slicing a list
  • ● Creation and Concatenation of Tuples
  • ● Slicing of Tuples
  • ● Demonstration of Set and Boolean operations
  • ● Demonstration on Python Dictionaries
  • ● Python statements
  • ● If Elif and Else Statements
  • ● For loop
  • ● While loop
  • ● Range vs xrange in Python
  • ● List Comprehensions in Python
  • ● Chaining comparison in python
  • ● Else with for and Switch Case in Python
  • ● Using iteration in python
  • ● Iterators in Python
  • ● Iterators function
  • ● Python functions and its types
  • ● Defining a Function in Python
  • ● Rules for naming Python function (identifier)
  • ● Python Function Parameters
  • ● Python Return Statement and calling a function
  • ● Function arguments
  • ● Python function argument and its types
  • ● Default argument in Python
  • ● Python keyword arguments
  • ● Python arbitrary arguments
  • ● Python built-In functions with syntax and examples
  • ● Lambda expressions, map, and filter Functions


  • ● Write a Python Function with or without the parameters
  • ● Demo on If Else Statements and Iterators Functions
  • ● Demo on Simple Boolean and Simple Math Functions
  • ● Demo on create an object and write a for loop to print all odd numbers
  • ● Demo on smaller or a greater number
  • ● Use Lambda Expression to Map and Filter the Functions
  • ● OOP concept
  • ● Attributes
  • ● Class Keywords
  • ● Class Object Attributes
  • ● Methods in Python
  • ● Data Hiding and Object Printing
  • ● Constructors and Destructors in Python
  • ● Class and static variable in python
  • ● Class method and static method in python
  • ● Inheritance, Encapsulation, Polymorphism & Abstraction
  • ● Special Methods - Magic Method


  • ● Write a Class
  • ● Writing a Python program and incorporating the OOP concepts in it
  • ● Creating a Bank Account using OOP concepts
  • ● Modules
  • ● Installing external packages and modules
  • ● Working oPn yPi using pip Install
  • ● Numeric, Logarithmic, Power, Trigonometric and Angular functions
  • ● Python Errors and Exceptions
  • ● Syntax Errors in Python
  • ● Handling Exceptions in Python
  • ● Raising Exceptions
  • ● User-defined Exceptions
  • ● Unit Testing in Python


  • ● Demo on Modules
  • ● Demo on Exception Handling
  • ● Running Tests with Unit test Library
  • ● Decorators in Python
  • ● Syntax of Decorators and Working with them
  • ● Generators in Python
  • ● Working with Generators


  • ● Demonstration on Decorators and Generators
  • ● NumPy
  • ● Creating Arrays in NumPy
  • ● Using Arrays and Scalars
  • ● Indexing NumPy Arrays
  • ● NumPy Array Manipulation
  • ● Array Transportation
  • ● Universal Array Function
  • ● Array Processing
  • ● Array Input and Output


  • ● Importing NumPy Modules
  • ● Creating and Initializing NumPy Arrays of different dimensions
  • ● Working with arange in NumPy arrays
  • ● Perform arithmetic operation on NumPy Arrays
  • ● Create 3 Dimensional NumPy array
  • ● SciPy
  • ● Clusters, Lining, Signals, Optimization, Integration, Sub packages
  • ● Bayesian Theory


  • ● Working with SciPy Cluster and Lining
  • ● Import SciPy by applying the Bayes phrase to the specified notes.
  • ● Data manipulation
  • ● Pandas libraries
  • ● Dependency of NumPy libraries
  • ● Pandas Series objects
  • ● Pandas data frames
  • ● Load and process data with Pandas
  • ● Combining data objects
  • ● Merging, and various types of data object attachments
  • ● Record & clean notes, edit notes, visualize notes


  • ● Manipulating data with pandas by Importing & navigating spreadsheets containing variable types such as float, integer, double, and others.
  • ● Matplotlib
  • ● Seaborn
  • ● Pandas Built-in Data Visualization
  • ● Plotly and Cufflinks
  • ● Geographical Plotting


  • ● Using Matplotlib to create pie charts, scatter plots, line graphs, and histograms
  • ● Create Graphs and Charts using different libraries
  • ● Web scraping in Python
  • ● Web scraping libraries
  • ● Beautifulsoup and Scrapy
  • ● Installation of beautifulsoup
  • ● Installation of Python parser lxml
  • ● Creating soup object with input HTML
  • ● Searching of tree
  • ● Full or partial parsing
  • ● Output print
  • ● Searching the tree


  • ● Installation of Beautiful soup and lxml Python parser
  • ● Making a soup object with input HTML file
  • ● Navigating using by objects in soup tree
  • ● Introduction to Machine Learning
  • ● Understanding SciKit Learn
  • ● Need of Machine Learning
  • ● Types in Machine Learning
  • ● Machine Learning Workflow
  • ● Understanding SciKit Learn!
  • ● Machine Learning Use-Cases
  • ● Machine Learning Algorithms
  • ● Supervised Learning
  • ● Unsupervised Learning


  • ● Working with Machine Learning Algorithms
  • ● Supervised learning
  • ● Classification and Regression Algorithms
  • ● Linear regression and how to do calculations in Linear Regression?
  • ● Understanding Linear regression in Python
  • ● Understanding Logistics regression
  • ● Working with Supports vector machine
  • ● xgboost (standalone step)


  • ● Working with Classification and Regression Algorithms
  • ● Using SciKit Library with Random Forest algorithm for implementing Supervised Learning
  • ● Xgboost
  • ● Unsupervised Learning
  • ● Use Cases of Unsupervised Learning Understanding Clustering,
  • ● Types of Clustering - Exclusive Clustering, Overlapping Clustering, Hierarchical Clustering
  • ● Understanding K-Means Clustering and its algorithm
  • ● Stepwise calculation of k-means algorithm
  • ● Running k-means with SciKit Library
  • ● Understanding association mining rule
  • ● Market basket analysis
  • ● Association rule mining and Apriori Algorithm


  • ● Demo on Unsupervised Learning
  • ● Demo on Algorithms in the SciKit Learn package for applying machine learning techniques, and training the network model
  • ● Demo on Apriori

Course 3: Machine Learning with Python

An industry-oriented course designed by experts. Become a Machine Learning Engineer by learning how to implement Python programming in Machine Learning algorithms.

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  • ● Basic commands of Python
  • ● Understanding Python Objects and Data Types
  • ● Understanding Python Operators
  • ● Understanding Python Statements, Control flow and Functions
  • ● Understanding Python OOP Concepts


  • ● Installing Python Anaconda for Windows, Linux, and Mac
  • ● Writing a “Hello World Program”
  • ● Using objects, data types, operators, statements, control flow, functions
  • ● Using OOP Concepts
  • ● Intro to NumPy
  • ● Creating Arrays in NumPy
  • ● Using Arrays and Scalars
  • ● Indexing NumPy Arrays
  • ● NumPy Array Manipulation
  • ● Array Transportation
  • ● Universal Array Function
  • ● Array Processing
  • ● Array Input and Output


  • ● Importing NumPy Modules
  • ● Creating and Initializing NumPy Arrays of different dimensions
  • ● Working with arange in NumPy arrays
  • ● Perform arithmetic operation on NumPy Arrays
  • ● Create 3 Dimensional NumPy array
  • ● Introduction to SciPy
  • ● Its Functions Building NumPy
  • ● Clusters, Lining, Signals, Optimization, Integration, Sub packages and SciPy with Bayesian Theory.


  • ● Working with SciPy Cluster and Lining
  • ● Import SciPy by applying the Bayes phrase to the specified notes
  • ● What is data manipulation?
  • ● Use panda libraries to manipulate data
  • ● Dependency of NumPy library libraries
  • ● Pandas Series objects,
  • ● Panda data frames
  • ● Load and process data with pandas
  • ● Combining data objects
  • ● Merging, and various types of data object attachments.
  • ● Record & clean notes, edit notes, visualize notes


  • ● Manipulating data with pandas by Importing & navigating spreadsheets containing variable types such as float, integer, double, and others
  • ● Matplotlib
  • ● Seaborn
  • ● Pandas Built-in Data Visualization
  • ● Plotly and Cufflinks
  • ● Geographical Plotting


  • ● Using Matplotlib to create pie charts, scatter plots, line graphs, and histograms
  • ● Create Graphs and Charts using different libraries
  • ● What is Machine Learning?
  • ● Need of Machine Learning
  • ● Types of Machine Learning
  • ● Supervised Learning
  • ● Unsupervised Learning
  • ● Reinforcement Learning


  • ● Intro to Supervised Learning
  • ● Types of Supervised Learning
  • ● Regression
  • ● Classification
  • ● What is Regression?
  • ● Understanding Simple Linear Regression
  • ● Understanding Multiple Linear Regression


  • ● Working with Linear Regression
  • ● Working with Multiple Linear Regression
  • ● Linear regression vs Logistic Regression
  • ● Math in the logistic regression and their formulas
  • ● Confusion matrix and find the accuracy
  • ● True and False positive rates
  • ● Threshold evaluation with ROCR


  • ● Implementing logistic regression
  • ● Confusion Matrix
  • ● Impurity function and Entropy in Decision Tree
  • ● Information gain
  • ● Gini index
  • ● Overfitting, pruning, pre-pruning, post-pruning, cost-complexity pruning
  • ● Ensemble techniques and bagging
  • ● Random forest


  • ● Implementing Decision Tree
  • ● Finding Information gain
  • ● Finding Gini index
  • ● Finding out overfitting, pruning, pre-pruning, post-pruning, cost-complexity pruning
  • ● Using ensemble techniques and bagging
  • ● Implementing Random forest
  • ● Naïve Bayes concepts and the formula for the Bayes theorem
  • ● What is SVM or Support Vector Machine?
  • ● Kernel Functions of SVM
  • ● Math and Formula behind SVM


  • ● Using Naïve Bayes
  • ● Unsupervised Learning
  • ● Types of Unsupervised Learning
  • ● Clustering
  • ● Dimensionality Reduction
  • ● Types of Clustering
  • ● K-means clustering and math behind it
  • ● Dimensionality reduction with PCA


  • ● Implementation of K-means clustering
  • ● Learning about dimensionality reduction with PCA
  • ● Natural Language Processing (NLP)
  • ● Text mining
  • ● Why text mining?
  • ● Working of NPL with text mining
  • ● Working with Natural Language Toolkit (NLTK) environment
  • ● Performing cleaning and pre-processing and text classification in text mining


  • ● Working of NPL with text mining
  • ● Working with Natural Language Toolkit (NLTK) environment
  • ● Performing cleaning and pre-processing and text classification in text mining
  • ● Understanding Deep Learning
  • ● Understanding Deep Learning with Neural Networks
  • ● Understanding essential differences b/w biological neural network & artificial neural network
  • ● Understanding and implementing perceptron learning algorithm
  • ● Working with Deep Learning frameworks like TensorFlow
  • ● Working with TensorFlow constants, variables and place-holders


  • ● Understanding and implementing perceptron learning algorithm
  • ● Working with Deep Learning frameworks like TensorFlow
  • ● Working with TensorFlow constants, variables and place-holders
  • ● Introduction to Time Series Analysis
  • ● Applications and components of time series
  • ● Moving average, smoothing techniques, exponential smoothing, univariate time series models, multivariate time series analysis
  • ● ARIMA model


  • ● Working with moving average, smoothing techniques, exponential smoothing, univariate time series models, multivariate time series analysis, and the ARIMA model
  • ● Performing Time series in Python
  • ● Sentiment analysis in Python (Twitter sentiment analysis) with text analysis.

Course 4: Artificial Intelligence & Deep learning

Become an Artificial Intelligence and Deep Learning Engineer by mastering concepts from basics to advanced with this course, designed by experts, filled with real-world projects and hands-on exercises.

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  • ● Basics of Python
  • ● OOPs Concept in Python
  • ● Introduction to NumPy
  • ● Introduction to Pandas
  • ● Data Pre-processing
  • ● Data Manipulation
  • ● Data Visualization


  • ● Loading different types of dataset in Python
  • ● Arranging the data
  • ● Plotting the graphs
  • ● NumPy
  • ● Pandas
  • ● Scikit-learn
  • ● Matplotlib
  • ● Fundamentals of Statistics
  • ● Generalized Linear Models
  • ● Regression and Clustering


  • ● What is Machine Learning?
  • ● Supervised Learning – Regression
  • ● Supervised Learning – Classification
  • ● Model Selection and Boosting
  • ● Unsupervised Learning
  • ● Dimensionality Reduction
  • ● Association Rules Mining and Recommendation


  • ● Regression Use case: Weather Forecasting
  • ● Clustering Use Case: Image classification
  • ● Clustering Use Case: Recommender system
  • ● Dimensionality Reduction Use Case: Structure Discovery
  • ● Association Rule Mining
  • ● Use Case Apriori Algorithm: Market Basket Analysis
  • ● What is a Time Series?
  • ● Time Series Analysis techniques and applications
  • ● Components of Time Series
  • ● Moving average
  • ● Smoothing techniques
  • ● Exponential smoothing
  • ● Univariate time series models
  • ● Multivariate time series analysis
  • ● Arima model
  • ● Time Series in Python


  • ● Use Case of Checking Stationarity
  • ● Learn how to convert a non-stationary data to stationary
  • ● Implement Dickey Fuller Test
  • ● Use case of ACF and PACF
  • ● Generate the ARIMA plot
  • ● Time Series Analysis Forecasting
  • ● Understanding graphical model
  • ● Bayesian Network
  • ● Inference
  • ● Model learning


  • ● Use case on Bayesian Network
  • ● Getting started with Reinforcement Learning
  • ● Bandit Algorithms and Markov Decision Process
  • ● Dynamic Programming and Temporal Difference Learning methods
  • ● What is Deep Q Learning?


  • ● Calculating Reward
  • ● Discounted Reward
  • ● Calculating Optimal quantities
  • ● Implementing Q Learning
  • ● Setting up an Optimal Action
  • ● Text Preprocessing and Natural Language Processing
  • ● Analyzing Sentence Structure
  • ● Text Classification
  • ● Sentiment Analysis


  • ● Use case: Twitter Sentiment Analysis
  • ● Use case: Chat Bot
  • ● What is Deep Learning?
  • ● Why Deep Learning?
  • ● Advantage of Deep Learning over Machine learning
  • ● 3 Reasons to go for Deep Learning
  • ● Real-Life use cases of Deep Learning


  • ● How Deep Learning Works?
  • ● Activation Functions
  • ● Illustrate Perceptron
  • ● Train a Perceptron
  • ● Parameters of Perceptron
  • ● TensorFlow
  • ● Graph Visualization
  • ● Constants, placeholders, and variables
  • ● Create a Model


  • ● TensorFlow code- basics
  • ● Use case Implementation
  • ● Building a single perceptron for classification on SONAR dataset
  • ● Understand limitations of a Single Perceptron
  • ● Understand Neural Networks in Detail
  • ● Illustrate Multi-Layer Perceptron
  • ● What is a backpropagation?
  • ● Getting started with TensorBoard


  • ● Understand Backpropagation with an example
  • ● Using TensorFlow build MLP Digit Classifier
  • ● Building a multi-layered perceptron for classification of Hand-written digits
  • ● What is Deep Network?
  • ● Why Deep Networks?
  • ● Understand How Deep Network Works?
  • ● How Backpropagation Works?
  • ● Illustrate Forward pass, Backward pass
  • ● Different variants of Gradient Descent
  • ● Types of Deep Networks


  • ● Use-Case Implementation on SONAR dataset
  • ● Building a multi-layered perceptron for classification on SONAR dataset
  • ● What is CNN?
  • ● Application of CNN
  • ● Architecture of a CNN
  • ● Convolution and Pooling layers in a CNN


  • ● Understanding and Visualizing a CNN
  • ● Learn how to build a convolutional neural network for image classification
  • ● Application use cases of RNN
  • ● Modelling sequences
  • ● Training RNNs with Backpropagation
  • ● Long Short-Term memory (LSTM)
  • ● Recursive Neural Tensor Network Theory
  • ● Recurrent Neural Network Model


  • ● Building a recurrent neural network for SPAM prediction.
  • ● Introduction to Restricted Boltzmann Machine
  • ● Applications of RBM
  • ● Collaborative Filtering with RBM
  • ● Getting started with Autoencoders
  • ● Autoencoders applications


  • ● Learn how to build an autoencoder model for classification of handwritten images extracted from the MNIST Dataset
  • ● Getting started with Keras
  • ● Compose Models in Keras
  • ● What is sequential composition?
  • ● What is functional composition?
  • ● Predefined Neural Network Layers
  • ● What is Batch Normalization?
  • ● Save and Load a model with Keras
  • ● Customize the model training process


  • ● Use case Keras implementation
  • ● Learn how to build a model using Keras to do sentiment analysis on twitter data reactions on GOP debate in Ohio
  • ● Using TensorBoard with Keras
  • ● What is TFLearn?
  • ● Compose Models in TFLearn
  • ● Sequential Composition
  • ● Functional Composition
  • ● Predefined Neural Network Layers
  • ● Batch Normalization
  • ● Save and Load a model with TFLearn
  • ● Customize the Training Process


  • ● Use case Implementation with TFLearn
  • ● Use TensorBoard with TFLearn
  • ● Build a recurrent neural network using TFLearn to do image classification on hand-written digits

Course 5: Tableau Certification Training

Become an Artificial Intelligence and Deep Learning Engineer by mastering concepts from basics to advanced with this course, designed by experts, filled with real-world projects and hands-on exercises.

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  • ● Data Visualization
  • ● Different Business Intelligence tools
  • ● Introduction to Tableau
  • ● Tableau Architecture
  • ● Tableau user interface
  • ● Connection to DataSource
  • ● Tableau data types


  • ● Tableau installation
  • ● Tableau user interface
  • ● Connection to DataSource
  • ● Tableau data types
  • ● Getting Started with Data
  • ● Data preparation and processing with NULL values
  • ● Data linking
  • ● Cross-database links
  • ● Merging data
  • ● Managing Metadata
  • ● Managing Extracts
  • ● Saving and Publishing Data Sources
  • ● Connecting to Data from the Web
  • ● Data Preparation with Text and Excel Files
  • ● Join Types with Union
  • ● Cross-database Joins
  • ● Data Blending
  • ● Additional Data Blending Topics
  • ● Connecting to PDFs
  • ● Connecting to Cubes


  • ● Implementing different ways to work with data in Tableau
  • ● Select and Mark
  • ● Sort and Group
  • ● Work with Sets
  • ● Permanent Sets
  • ● Calculated Sets
  • ● Bins


  • ● Working with sets in Tableau
  • ● Filter (Add and Remove)
  • ● Continuous Date Filtering
  • ● Dimensions
  • ● Sizes
  • ● Interactive Filters
  • ● Brand Mapping
  • ● Hierarchy
  • ● Creating Tableau Folders
  • ● Tableau Sorting
  • ● Sorting Types
  • ● Filtering in Tableau
  • ● Filtering Types
  • ● Filtering by Operations


  • ● Working with different types of filters in Tableau
  • ● Data formatting
  • ● Formatting panels (Menus, Settings, Fonts, Orientation, Copy-paste
  • ● Trends and reference lines
  • ● Forecasting
  • ● Cluster analysis of tableau k-means
  • ● Visual analysis in baselines and ribbons Tableau
  • ● Confidence interval


  • ● Working with formatting panels
  • ● Visual analysis in baselines and ribbons Tableau
  • ● Drawing Coordinates
  • ● Latitude and Longitude
  • ● Editing Unrecognized Places
  • ● Custom Geocoding
  • ● Polygon Maps
  • ● WMS: Web Mapping Service
  • ● Background Image
  • ● Generating Image Coordinates
  • ● Map Preview
  • ● Custom Areas
  • ● Cards
  • ● WMS Map


  • ● Editing Unrecognized Places
  • ● create map projects in Tableau
  • ● create maps with multiple access
  • ● Editing locations
  • ● Syntax and Tableau calculation functions
  • ● Calculation types (table, string, logic, date, number, aggregate)
  • ● LOD expressions (concepts and syntax)
  • ● Nested LOD expressions
  • ● Level of detail
  • ● Level of fixed details
  • ● Level of detail lower
  • ● Higher level of detail
  • ● Fast table calculations
  • ● Creating calculated fields
  • ● Preset calculations
  • ● Validating


  • ● Working with Tableau calculations
  • ● Aggregation and replication with LOD expressions
  • ● Creating calculated fields
  • ● Create parameters
  • ● Parameters in the calculation of filter parameters
  • ● Column selection parameters
  • ● Chart selection parameters
  • ● Parameters in the filter session
  • ● Parameters in calculated fields
  • ● Parameters in reference lines


  • ● Using parameters in the filter session
  • ● Using parameters in calculated fields
  • ● Using parameters in reference lines
  • ● Double axis charts, bar charts
  • ● Fields
  • ● Pareto diagrams
  • ● Motion graphs and funnel diagrams
  • ● Tree maps and heat charts, heat maps
  • ● Text tables
  • ● Grained graphs
  • ● Pie charts Tree diagrams
  • ● Bar charts
  • ● Line charts
  • ● Balloon Diagrams
  • ● Bullet Diagrams
  • ● Scatterplots
  • ● Biaxial Diagrams
  • ● Funnel Diagrams
  • ● Pare Diagrams
  • ● Pare Diagrams


  • ● Implementation of all graphs and charts
  • ● Getting Started with Dashboards and Stories
  • ● Building a Dashboard
  • ● Dashboard Objects
  • ● Dashboard Formatting
  • ● Dashboard Interactivity Using Actions
  • ● Dashboard Extensions
  • ● Device Designer
  • ● Story Points


  • ● Working with Dashboards and Stories
  • ● Tableau Prep Builder Introduction
  • ● Prep Builder Interface
  • ● Input Step
  • ● Cleaning Step
  • ● Group and Replace
  • ● Profile Pane
  • ● Pivot Step
  • ● Aggregate Step
  • ● Join Step
  • ● Union Step
  • ● Output Step
  • ● Tableau Prep Conductor


  • ● Working on input Step, Cleaning Step, Group and Replace, and Profile Pane
  • ● Working on Pivot Step, Aggregate Step, Join Step, Union Step, and Output Step
  • ● Using Tableau Prep Conductor
  • ● Introduction to the R programming
  • ● Applications and use cases of R
  • ● R programming on Tableau platform implementation
  • ● Integration Tableau with R
  • ● Introduction to Hadoop
  • ● Integration Tableau with Hadoop


  • Implementation of R programming on Tableau platform
  • ● Integration Tableau with Hadoop

Course 6: Data Science with SAS

An industry-oriented course designed by experts. Become a SAS Analyst by mastering concepts in Data Science and implementing SAS from scratch to advance level.

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  • ● Getting started with SAS
  • ● What is SAS?
  • ● Why is it so getting so popular?
  • ● Installation of SAS
  • ● Different types of SAS windows
  • ● Rules for definition of a SAS name
  • ● Working with different data types in SAS
  • ● Using Formats and Informatics in SAS
  • ● How to work with data sets?
  • ● Data step and procedural step
  • ● Instream SAS dataset
  • ● Search, editor, log, and explorer
  • ● What are the SAS functions?
  • ● Different library types and programming files


  • ● Navigate the SAS windows environment
  • ● Get started with SAS programming
  • ● Convert data as per required format
  • ● Create a dataset using CARDS
  • ● Generate the output using PROC PRINT
  • ● Import data in SAS
  • ● Manipulate influx of datasets into SAS
  • ● Create a new variable
  • ● Temporary and permanent datasets
  • ● Set and merge statements


  • ● Import external data within SAS using INFILE
  • ● Import external data within SAS using PROC IMPORT
  • ● Create permanent datasets
  • ● Perform operations with KEEP, DROP and RENAME and LABEL options
  • ● Construct a new variable
  • ● Create integrated datasets using SET/MERGE
  • ● Conditional and iterative processing
  • ● DO
  • ● DO WHILE
  • ● DO UNTIL
  • ● IF ELSE
  • ● Arrays in SAS
  • ● Useful SAS Functions
  • ● PUT
  • ● INPUT
  • ● Date
  • ● Time
  • ● Numeric
  • ● Character


  • ● Use of DO, DO WHILE, DO UNTIL, IF-ELSE, ELSE, ELSE IF
  • ● Demonstrate the use of arrays in SAS
  • ● Demonstrate the use of INPUT/PUT functions
  • ● Demonstrate the use of date/time functions
  • ● Demonstrate the use of Numeric functions
  • ● Execute a program with Character Functions
  • ● Proc Dataset
  • ● Proc Format
  • ● Proc Sort
  • ● Proc Means
  • ● Proc Freq
  • ● Proc Surveyselect
  • ● Proc Transpose
  • ● Proc Summary
  • ● Proc Rank
  • ● Proc Corr
  • ● Proc Univariate


  • ● Obtain statistical means of variables
  • ● Check the degree of dependence within different variables
  • ● Generate ranks for statistical data
  • ● Perform re-structuring of data
  • ● Conduct sampling: Random and Stratified
  • ● Introduction to Regression
  • ● Simple and Multiple Linear Regression
  • ● Logistic Regression
  • ● Introduction to Clustering
  • ● Hierarchical Clustering
  • ● Non-Hierarchical Clustering: K means Clustering


  • ● Demonstrate the use of PROC CLUSTER
  • ● Writing a SAS advanced program with PROC FASTCLUS
  • ● Performing operations on regression with PROC REG
  • ● Demonstrate modelling using PROC LOGISTIC
  • ● What is Data Optimization?
  • ● Realizing Optimization Models
  • ● Proc Optmodel and its workings
  • ● Rosenbrock Problem and how to solve it
  • ● What is ODS in SAS?
  • ● Why use ODS?
  • ● Generate RTF file
  • ● Generate PDF file
  • ● Generate HTML file
  • ● Generate doc file


  • ● Write a program with PROC OPTMODEL
  • ● Solve the optimization model using SOLVE
  • ● Extract optimized outputs
  • ● Route quality presentation files
  • ● Create HTML files
  • ● Create RTF file
  • ● Create PDF and doc files
  • ● Create new tables
  • ● The SELECT statement
  • ● Sort Data
  • ● The CASE expression
  • ● Other SELECT statement clauses
  • ● JOINS and UNIONS


  • ● Demonstrate the SQL Procedure
  • ● Write the SELECT clause
  • ● Application of WHERE clause
  • ● Merge Datasets
  • ● Introduction to Macros
  • ● Benefits of using SAS Macros
  • ● Macro Code Constituents
  • ● Macro Variables
  • ● What is Macro Step?
  • ● Positional Parameters to Macros


  • ● Demonstrate the use of Macro variables in SAS
  • ● Write a Macro code to simplify a program
  • ● Demonstrate Macro step programming
  • ● Perform Macro coding by passing parameters

Data Science Masters Projects Projects

A learning experience filled with real-world industry-oriented projects to help you understand the subject better by the method implementation.

A major government bank has approached your company to analyze from their customer loan data set. Since the last 4 months, a lot of customers who are not able to repay their loan amount has increased. You have been assigned a task of analyzing from the data set and giving insights about which customer should be given the loan approval and which shouldn’t be.
Develop an ML algorithm to identify the most optimal ratio/ aspects to allocate funds/ spending proportionately by organizations in different areas of expenses like R&D, Marketing, Employee Cost, HR & Admin Cost, Infrastructure Cost etc. Identifying the optimal ratio of the amount of allocation of funds to various segments is utmost important. This would also help the Management team with below aspects, to increase the revenue and profitability, to better design marketing strategies, to allocate the internal resources better.
As you are holding the position of Data Scientist in your current organization, you need to build a model to categorize words based on sentiments. This model should tell whether words detected are positive or negative.
Andrew is a Data Analyst in a company named ValueAnalytics, he has been assigned a project to analyze the Stock Market from a data set of Technology Stocks, by using the different libraries, he has to extract the stock information and perform the visualization of different aspects, along with analyzing the risk of a stock from its past history. find the change in the price of the stock over time, find the daily return of the stock on average, find the moving average of the various stocks, find the correlation between different stocks' closing prices, find the correlation between different stocks' daily returns, find the value we should put at risk by investing in a particular stock, also, attempt to predict future stock behaviour.
Post the election, the government has given your company a contract of doing the analysis on the Election and Donor Data. You as the data analyst are supposed to answer a few questions by analyzing the aggregated poll data. Like how many votes are done and different aspects in it along with analyzing the average donations given to Democrats and Republican (more questions are asked during the project).
Provide your machine with the multiple datasets of multiple persons in OpenCV, and after running it, it should be able to detect those faces and display their names whenever detected on the screen or camera. A Dataset which should consist of everyone’s face images into a subfolder by names. Each person’s dataset should consist of at least 3 images which will be used to verify the operation of your model
Build a model to identify the frauds from the Data Set. Then, move to build an advanced deep learning mapping model to identify and predict the probability that each customer cheated. First, build the unsupervised deep learning branch from your hybrid deep learning model. Second, develop the supervised learning branch and then compose this hybrid deep learning model comprising both supervised and unsupervised deep learning.
Build a Spam filter which can identify the SPAM Messages from the Data Set given to you by using NLTK and Scikit Learn.
Provide your machine with the multiple dataset of multiple persons in OpenCV, and after running it, it should be able to detect those faces and display their names whenever detected on the screen or camera. A Dataset which should consist of everyone’s face images into subfolders by names. Each person’s dataset should consist of at least 3 images which will be used to verify the operation of your model
Build a model to identify the frauds from the Data Set. Then, move to building an advanced deep learning mapping model to identify and predict the probability that each customer cheated. First, build the unsupervised deep learning branch from your hybrid deep learning model. Second, develop the supervised learning branch and then compose this hybrid deep learning model comprising both supervised and unsupervised deep learning.
You are working as a BI Specialist for a company called DataViziOne. Your company got a project from one of the top megastore companies in the US. They want a sales analysis in USA, city, state-wise, and country wise. You need to create a dashboard visualizing. Which country which has the best sales record (need to show the map view), which state which has the best sales record based on revenue (need to show geographical visualization), which salesman from a city to find out the best salesman (Drill down from every city of a state to find out the best sales record in a city)?
A giant garment store company has approached your company, now you as a BI professional need to find out the insights from one of their stores in a region and perform the following tasks on their sales report. Use report background colour or background image on your report. Create a report header using Text, Image, Shape visuals (Report Name, Company Name, Company Logo). Make sure to use the containers in the above tasks. Plot all required filters which are useful to analysis. Show Gross Sales & Total Profit using card visuals. Show sales and profit using appropriate visuals by Year/Month (Using Drilldown). Show Segment wise sales & profit. Maximum & minimum discount has to be given to which product. Show profit % by country. Publish report to the Tableau Workbook in the "Finance" workspace. Create and share the workbook with your colleagues
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$420/- $700/-

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What People Think

Vijay Joshi

Vijay Joshi

Head SW Business Development at Diebold Nixdorf

I Joined Eduranz, for "Data Science Master's Training". Instructor were very knowledgeable. They explain everything in detail with everyone's concerns/ questions answered.

Dr. Essam Zaghloul

Dr. Essam Zaghloul

Board Member at Al geology Petroleum

I am currently undergoing Data Science Master's program with Eduranz the course content is top notch, Instructors are good, they are knowledgeable and all of them are professionals from their specific field.

Rajeev Kathuria

Rajeev Kathuria

West Head-Partner Management at Samsung

The instructor was very good and prompt in responding to questions. Excellent virtual class experience. Good Work eduranz!

Ljiljana Spasovic Botha

Ljiljana Spasovic Botha

Business Development Officer at SASLO

Had a great learning session where the concepts are clear to understand and can solve the given assignments easily.

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FAQ

Eduranz offers this unique online master’s program for you to master skills in Python, Data Science, Machine Learning, Artificial Intelligence, Tableau, SAS, and kick-start your career in this rousing domain. There are many reasons to choose Eduranz::
• Interactive online instructor-led live classes conducted by SMEs
• Personal mentors who will keep a stage track of your progress in AWS Solutions Architects course
• A Substantial LMS which allows the users to view their recorded sessions from their live classes along with the self-recorded courses
• Real-time exercises, assignments, industry-based use cases and real-world projects
• 24/7 learning support by the Eduranz’s dedicated tech support team
• Large community of learners from across the globe
• Industrially as well as globally recognized certificate by Eduranz
• Personalized job support, resume and interview preparation

You never miss any lecture at Eduranz, because you will be provided with the recorded sessions of the live class on your LMS within 24 hours and despite that, you can also attend any different live session to cover up the missed topic and ask your doubts from the trainer or you can simply reschedule your batch and get yourself a new batch assigned.

Live Virtual Classes or Online Classes. With online class training, you can access courses via video conferencing from your desktop to increase productivity and reduce work time and personal time.

Eduranz offers a 24/7 request solution and you can pick up your tickets at any time from our dedicated support team. You can use email support for all your questions. If your request is not answered via email, we can also arrange one-on-one discussions with the faculty. You will be glad to know that you can switch to Eduranz support after completing training. We also don’t limit the number of tickets you can collect when solving questions and doubts.

Yes, Eduranz has a dedicated placement assistance team. Our job assistance program will help you reach the job you have been seeking. Under this program, we help you by building your professional resume and then sharing it across our network companies that we have tie ups with.

Eduranz offers the most up-to-date, relevant and valuable projects in the real world as part of the training program. In this way, you can integrate what you have learned in the real industry. Each training is delivered with various projects where you can thoroughly test your skills, learning and practical knowledge so that you are well prepared for the industry. They work on very interesting projects in the fields of high technology, e-commerce, marketing, sales, networking, banking, insurance and more. After successfully completing your project, your skills will be counted as a result of six months intensive industry experience.

After completing the Eduranz Training Program along with all real projects, tests and assignments and achieving at least 60% points in the qualification exam; you will receive an industrial recognized certificate by Eduranz. This certification is recognized by companies all across the industry, which includes a lot of top MNCs worldwide.

Our job assistance program will help you reach the job you have been seeking. Under this program, we help you by building your professional resume and then sharing it across our network companies that we have tie ups with. You will also be prepared for interviews through mock sessions. However, Eduranz is not a recruitment agency. We do not guarantee you a job. After we share your profiles with the companies, the further process depends upon your performance and their decision.

All of our highly qualified instructors are industry experts with minimum 10-12 yrs. of relevant IT experience. Each of them underwent a rigorous selection process that included screening profiles, teaching assessments, and training demonstrations.



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