Masters in Data Science

Master's Program 4.9 out of 5 rating votes 5276

Data Scientist is a domain that assists with solving complex and puzzling issues through data by collecting and analyzing the perfect solutions extracted using modern tools and techniques.

INR 75000

Excluding GST

100% Placement



Ranked#2 among Top Full-Time

Masters in Data Science in India- 2010-2022

star date
Start Date : 12th Dec 2022Duration : 8 Months
Format : Live Online /Self-Paced/Classroom
Future of Masters in Data Science

₹25 LPA to 40 LPA

A data scientist with experience of 5+ years earns the best salary in the market with an average salary ranging from 25 LPA to 40 LPA.


Job opportunities

According to the U.S. Bureau of Labor Statistics, there is an increase of 28% in data science jobs through 2026, counting more than 11.5 million jobs.


Future Analytics

It is possible to see 75% growth in the enterprise shifting to operationalizing AI counting 5x growth in the analytics infrastructure.


Masters in Data Science Program

4.9 out of 5 rating vote 5276

Data Scientist is a domain that assists with solving complex and puzzling issues through data by collecting and analyzing the perfect solutions extracted using modern tools and techniques..

INR 75000 + GST
100% Placement Assistance

Program Overview

The data science training includes a combination of multiple domains including scientific method, artificial intelligence, statistics, and data analysis. All these combined help in extracting the data to understand the changes to integrate the perfect business process. The Data Science program offers complete industry mentorship covering 6 specializations with a blend of tools, analytics, machine learning, and business acumen. The program is dedicated to providing complete career support providing hands-on experience over 14 programming tools.

  • Data Science Generalist
  • Data science engineer
  • Natural Language processing
  • Data analytics and Business intelligence
  • Business Analytics
  • Deep Learning

Opportunities in data science are growing more and more making it a very lucrative option with less competition. Data science is declared as the most trending job in the 21st century and is in great demand for the domains covering finance, marketing, retail, FMCG, and many others. With such high demand, the career possibilities in data science see astonishing growth in the coming future.

  • Web_IconData science career sees a growth of 56% every year around the world holding the spot of the most promising job with increasing global reach.
  • branData science offers amazing career opportunities with an advanced degree in business intelligence development, Data Architect, Infrastructure architect, data analyst
  • polyThe data science domains including AI and Machine Learning serve many purposes from business to political institutions. This makes it the most demanding domain around the world
  • analyticsAccording to the World Economic forum that data science will become the most emerging role by 2022.

Data science is getting more popular course day by day. The training will help you to easily understand the use of big data using coding and algorithms. The main motive of the course is intended to derive problem-solving solutions for the business.

m IconWith 60+ industry projects and 14+ programming languages and tools, this training provides you the eligibility to enter the top organizations.

 m IconThe average entry-level salary after completing the training is around 6 LPA and an experienced data scientist with 1 to 4 years of experience earns 10 LPA.

icon 3According to the US Bureau of labor statistics, there is a 27.9% rise in employment with the average growth estimated at 14% growth through 2028

The vision is intended to provide aspirants proper knowledge to understand the current need for data science and its integration with other technologies. You will understand the procedures to align the outcomes and easily identify the challenges and objectives for achieving business goals.

  • Get master certification in data science without quitting the job
  • Updated course curriculum designed by the industry experts
  • Career growth with more than 50% salary hike
  • Job opportunities from around the world from top tier organizations

The main objective of getting trained in data science is to develop useful and precise knowledge, skills, and techniques. The training is a completely task-oriented activity that is designed to improve the performance in your current job roles and prepare you for future jobs.

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Tools Covered of Masters in Data Science

Apache HBase
Apache Spark
Hadoop hdfs
Power BI

Masters in Data Science Curriculum

Course 1
Understanding Data Science Concept

Data Science is a powerful analytics platform to make discoveries. By using different aspects of computer science, data visualisations, data analytics, statistics, R and Python Programming in data science, you may convert voluminous data into meaningful contents.


Course Content

    • In this program you will learn:
      • Python Statistics for Data Science
      • Databases – MySQL and SQL Queries
      • Data Science Master’s Program
      • Machine Learning
      • Deep Learning
      • Power BI
      • Hadoop- Apache Spark and Scala Certification
      • Tableau - For Data Visualization
      • Data Science Masters - Live Projects
Course 2
Python Statistics for Data Science

This module offers knowledge to introduce you to the basic principles based on statistical methods and procedures followed in data analysis. This course will help you to understand the work process involved with summarizing the data, data storage, visualizing the data results, and a hands-on approach with statistical analysis with python.


Course Content

    • Introduction To Python:
      • Installation and Working with Python
      • Understanding Python variables
      • Python basic Operators
      • Understanding the Python blocks.
    • Introduction To Variables:
      • Variables, expression condition and function
      • Global and Local Variables in Python
      • Packing and Unpacking Arguments
      • Type Casting in Python
      • Byte objects vs. string in Python
      • Variable Scope
    • Python Data Type:
      • Declaring and usingNumeric data types
      • Using stringdata type and string operations
      • Understanding Non-numeric data types
      • Understanding the concept of Casting and Boolean.
      • Strings
      • List
      • Tuples
      • Dictionary
      • Sets
    • Introduction Keywords and Identifiers and Operators
      • Python Keyword and Identifiers
      • Python Comments, Multiline Comments.
      • Python Indentation
      • Understating the concepts of Operators
    • Data Structure
      • List
        • What is List.
        • List Creation
        • List Length
        • List Append
        • List Insert
        • List Remove
        • List Append & Extend using “+” and Keyword
        • List Delete
        • List related Keyword in Python
        • List Revers
        • List Sorting
        • List having Multiple Reference
        • String Split to create a List
        • List Indexing
        • List Slicing
        • List count and Looping
        • List Comprehension and Nested Comprehension
      • Dictionary
        • Dict Creation
        • Dict Access (Accessing Dict Values)
        • Dict Get Method
        • Dict Add or Modify Elements
        • Dict Copy
        • Dict From Keys.
        • Dict Items
        • Dict Keys (Updating, Removing and Iterating)
        • Dict Values
        • Dict Comprehension
        • Default Dictionaries
        • Ordered Dictionaries
        • Looping Dictionaries
        • Dict useful methods (Pop, Pop Item, Str , Update etc.)
    • Sets, Tuples and Looping Programming
      • Sets
        • What is Set
        • Set Creation
        • Add element to a Set
        • Remove elements from a Set
        • PythonSet Operations
        • Frozen Sets
      • Tuple
        • What is Tuple
        • Tuple Creation
        • Accessing Elements in Tuple
        • Changinga Tuple
        • TupleDeletion
        • Tuple Count
        • Tuple Index
        • TupleMembership
        • TupleBuilt in Function (Length, Sort)
      • Control Flow
        • Loops
        • Loops and Control Statements (Continue, Break and Pass).
        • Looping techniques in Python
        • How to use Range function in Loop
        • Programs for printing Patterns in Python
        • How to use if and else with Loop
        • Use of Switch Function in Loop
        • Elegant way of Python Iteration
        • Generator in Python
        • How to use nested IF and Else in Python
        • How to use nested Loop in Python
        • Use If and Else in for and While Loop
        • Examples of Looping with Break and Continue Statements
        • How to use IN or NOTkeywordin Python Loop.
    • Exception and File Handling, Module, Function and Packages
      • Python Exception Handling
        • Python Errors and Built-in-Exceptions
        • Exception handing Try, Except and Finally
        • Catching Exceptions in Python
        • Catching Specific Exception in Python
        • Raising Exception
        • Try and Finally
      • Python File Handling
        • Opening a File
        • Python File Modes
        • Closing File
        • Writing to a File
        • Reading from a File
        • Renaming and Deleting Files in Python
        • Python Directory and File Management
        • List Directories and Files
        • Making New Directory
        • Changing Directory
      • Python Function, Modules and Packages
        • Python Syntax
        • Function Call
        • Return Statement
        • Write an Empty Function in Python –pass statement.
        • Lamda/ Anonymous Function
        • *argsand **kwargs
        • Help function in Python
        • Scope and Life Time of Variable in Python Function
        • Nested Loop in Python Function
        • Recursive Function and Its Advantage and Disadvantage
        • Organizing python codes using functions
        • Organizing python projects into modules
        • Importing own module as well as external modules
        • Understanding Packages
        • Programming using functions, modules & external packages
        • Map, Filter and Reduce function with Lambda Function
        • More example of Python Function
    • Data Automation (Excel, SQL, PDF etc)
      • Python Object Oriented Programming—Oops
        • Concept of Class, Object and Instances
        • Constructor, Class attributes and Destructors
        • Real time use of class in live projects
        • Inheritance, Overlapping and Overloading operators
        • Adding and retrieving dynamic attributes of classes
        • Programming using Oops support
      • Python Database Interaction
        • SQL Database connection using
        • Creating and searching tables
        • Reading and Storing configinformation on database
        • Programming using database connections
      • Reading an excel
        • Reading an excel file usingPython
        • Writing toan excel sheet using Python
        • Python| Reading an excel file
        • Python | Writing an excel file
        • Adjusting Rows and Column using Python
        • ArithmeticOperation in Excel file.
        • Plotting Pie Charts
        • Plotting Area Charts
        • Plotting Bar or Column Charts using Python.
        • Plotting Doughnut Chartslusing Python.
        • Consolidationof Excel File using Python
        • Split of Excel File Using Python.
        • Play with Workbook, Sheets and Cells in Excel using Python
        • Creating and Removing Sheets
        • Formatting the Excel File Data
        • More example of Python Function
      • Working with PDF and MS Word using Python
        • Extracting Text from PDFs
        • Creating PDFs
        • Copy Pages
        • Split PDF
        • Combining pages from many PDFs
        • Rotating PDF’s Pages
      • Complete Understanding of OS Module of Python
        • Check Dirs. (exist or not)
        • How to split path and extension
        • How to get user profile detail
        • Get the path of Desktop, Documents, Downloads etc.
        • Handle the File System Organization using OS
        • How to get any files and folder’s details using OS
    • Data Analysis & Visualization
      • Pandas
        • Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to files
        • How to get record specific records Using Pandas Adding & Resetting Columns, Mapping with function
        • Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalue’s
        • Exploring the Data Plotting, Correlations, and Histograms
        • Getting statistical information about the data Analysis Concepts, Handle the None Values
        • Reading files with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
        • Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
        • Applying formulas on the columns Basic Grouping, Concepts of Aggregate Function
        • Complete Understanding of Pivot Table Data Slicing using iLocand Locproperty (Setting Indices)
        • Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data
        • Exporting the results to Excel Joins:
        • Python | Pandas Data Frame Inner Join
        • Under sting the properties of Data Frame Left Join (Left Outer Join)
        • Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
        • Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
        • Pandas | Find Missing Data and Fill and Drop NA Appending DataFrameand Data
        • Pandas | How to Group Data How to apply Lambda / Function on Data Frame
        • Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)
      • NumPy
        • Introduction to NumPy: Numerical Python
        • Importing NumPy and Its Properties
        • NumPy Arrays
        • Creating an Array from a CSV
        • Operations an Array from aCSV
        • Operations with NumPy Arrays
        • Two-Dimensional Array
        • Selecting Elements from 1-D Array
        • Selecting Elements from 2-D Array
        • Logical Operation with Arrays
        • Indexing NumPy elements using conditionals
        • NumPy’sMean and Axis
        • NumPy’sMode, Median and Sum Function
        • NumPy’sSort Function and More
      • MatPlotLib
        • Bar Chart using Python MatPlotLib
        • Column Chart using Python MatPlotLib
        • Pie Chart using Python MatPlotLib
        • Area Chart using Python MatPlotLib
        • Scatter Plot Chart using Python MatPlotLib
        • Play with Charts Properties Using MatPlotLib
        • Export the Chart as Image
        • Understanding plt. subplots () notation
        • Legend Alignment of Chart using MatPlotLib
        • Create Charts as Image
        • Other Useful Properties of Charts.
        • Complete Understanding of Histograms
        • Plotting Different Charts, Labels, and Labels Alignment etc.
      • Introduction to Seaborn
        • Introduction to Seaborn
        • Making a scatter plot with lists
        • Making a count plot with a list
        • Using Pandas with seaborn
        • Tidy vs Untidy data
        • Making a count plot with a Dataframe
        • Adding a third variable with hue
        • Hue and scattera plots
        • Hue and count plots
      • Visualizing Two Quantitative Variables
        • Introduction to relational plots and subplots
        • Creating subplots with col and row
        • Customizing scatters plots
        • Changing the size of scatter plot points
        • Changing the style of scatter plot points
        • Introduction to line plots
        • Interpreting line plots
        • Visualizing standard deviation with line plots
        • Plotting subgroups in line plots
      • Visualizing a Categorical and a Quantitative Variable
        • Current plots and bar plots
        • Count plots
        • Bar plot with percentages
        • Customizing bar plots
        • Box plots
        • Create and interpret a box plot
        • Omitting outliers
        • Adjusting the whiskers
        • Point plots
        • Customizing points plots
        • Point plot with subgroups
      • Customizing Seaborn Plots
        • Changing plot style and colour
        • Changing style and palette
        • Changing the scale
        • Using a custom palette
        • Adding titles and labels: Part 1
        • Face Grids vs. Axes Subplots
        • Adding a title to a face Grid object
        • Adding title and labels: Part 2
        • Adding a title and axis labels
        • Rotating x-tics labels
        • Putting it all together
        • Box plot with subgroups
        • Bar plot with subgroups and subplots
Course 3
Databases – MySQL and SQL

This module will help you to explore the query language SQL and its integration with My SQL to query the database. This module will help you to understand SQL access through data and the method to update and manipulate the data stored in the database. You will learn basic and advance concepts of MY SQL with complete practical exposure.


Course Content

    • Python - MySQL
      • Introduction to MySQL
      • What is the MySQLdb
      • How do I Install MySQLdb
      • Connecting to the MYSQL
      • Selecting a database
      • Adding data to a table
      • Executing multiple queries
      • Exporting and Importing data tables.
    • SQL Functions
      • Single Row Functions
      • Character Functions, Number Function, Round, Truncate, Mod, Max, Min, Date
    • General Functions
      • Count, Average, Sum, Now etc.
    • Joining Tables
      • Obtaining data from Multiple Tables
      • Types of Joins (Inner Join, Left Join, Right Join & Full Join)
      • Sub-Queries Vs. Joins
    • Operators (Data using Group Function)
      • Distinct, Order by, Group by, Equal to etc.
    • Database Objects (Constraints & Views)
      • Not Null
      • Unique
      • Primary Key
      • Foreign Key
    • Structural & Functional Database Testing using TOAD Tool
      • SQL Basic
        • SQL Introduction
        • SQL Syntax
        • SQL Select
        • SQL Distinct
        • SQL Where
        • SQL And & Or
        • SQL Order By
        • SQL Insert
        • SQL Update
        • SQL Delete
      • SQL Advance
        • SQL Like
        • SQL Wildcards
        • SQL In
        • SQL Between
        • SQL Alias
        • SQL Joins
        • SQL Inner Join
        • SQL Left Join
        • SQL Right Join
        • SQL Full Join
        • SQL Union
      • SQL Functions
        • SQL Avg()
        • SQL Count()
        • SQL First()
        • SQL Last()
        • SQL Max()
        • SQL Min()
        • SQL Sum()
        • SQL Group By
Course 4
Data Science Master’s Program

This course will help you to gain complete insights into the applied statistics, database systems, data preparation, and machine learning algorithms. The master in data science course will help you to gain a broad skill set to advance your career in respective fields such as data engineering, computer programming, and data architecture.


Course Content

    • Introduction to Data Science
      • What is Analytics & Data Science
      • Common Terms in Analytics
      • What is data
      • Classification of data
      • Relevance in industry and need of the hour
      • Types of problems and business objectives in various industries
      • How leading companies are harnessing the power of analytics
      • Critical success drivers
      • Overview of analytics tools & their popularity
      • Analytics Methodology & problem-solving framework
      • List of steps in Analytics projects
      • Identify the most appropriate solution design for the given problem statement
      • Project plan for Analytics project & key milestones based on effort estimates
      • Build Resource plan for analytics project
      • Why Python for data science
    • Accessing/Importing and Exporting Data
      • Importing Data from various sources (Csv, txt, excel, access etc)
      • Database Input (Connecting to database)
      • Viewing Data objects - sub setting, methods
      • Exporting Data to various formats
      • Important python modules: Pandas
    • Data Manipulation: Cleansing - Munging Using Python Modules
      • Cleansing Data with Python
      • Filling missing values using lambda function and concept of Skewness.
      • Data Manipulation steps (Sorting, filtering, duplicates, merging, appending, sub setting, derived variables, sampling, Data type conversions, renaming, formatting.
      • Normalizing data
      • Feature Engineering
      • Feature Selection
      • Feature scaling using Standard Scaler/Min-Max scaler/Robust Scaler.
      • Label encoding/one hot encoding
    • Data Analysis: Visualization Using Python
      • Introduction exploratory data analysis
      • Descriptive statistics, Frequency Tables and summarization
      • Univariate Analysis (Distribution of data & Graphical Analysis)
      • Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
      • Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc.)
      • Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas etc.)
    • Introduction to Statistics
      • Descriptive Statistics
      • Sample vs Population Statistics
      • Random variables
      • Probability distribution functions
      • Expected value
      • Normal distribution
      • Gaussian distribution
      • Z-score
      • Central limit theorem
      • Spread and Dispersion
      • Inferential Statistics-Sampling
      • Hypothesis testing
      • Z-stats vs T-stats
      • Type 1 & Type 2 error
      • Confidence Interval
      • ANOVA Test
      • Chi Square Test
      • T-test 1-Tail 2-Tail Test
      • Correlation and Co-variance
    • Introduction to Predictive Modelling
      • Concept of model in analytics and how it is used
      • Common terminology used in Analytics & Modelling process
      • Popular Modelling algorithms
      • Types of Business problems - Mapping of Techniques
      • Different Phases of Predictive Modelling
    • Data Exploration for Modelling
      • Need for structured exploratory data
      • EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
      • Identify missing data
      • Identify outliers’ data
      • Imbalanced Data Techniques
    • Data Pre-Processing & Data Mining
      • Data Preparation
      • Feature Engineering
      • Feature Scaling
      • Datasets
      • Dimensionality Reduction
      • Anomaly Detection
      • Parameter Estimation
      • Data and Knowledge
      • Selected Applications in Data Mining
Course 5
Machine Learning

Machine learning courses help to understand the complete concepts behind the processing of Artificial intelligence and Computer science. With the Machine learning course, you will cover topics based on supervised and unsupervised learning along with the development of software and algorithms to extract predictions based on data.


Course Content

    • Introduction to Machine Learning
      • Artificial Intelligence
      • Machine Learning
      • Machine Learning Algorithms
      • Algorithmic models of Learning
      • Applications of Machine Learning
      • Large Scale Machine Learning
      • Computational Learning theory
      • Reinforcement Learning
    • Techniques of Machine Learning
      • Supervised Learning
      • Unsupervised Learning
      • Semi-supervised and Reinforcement Learning
      • Bias and variance Trade-off
      • Representation Learning
    • Regression
      • Regression and its Types
      • Logistic Regression
      • Linear Regression
      • Polynomial Regression
    • Classification
      • Meaning and Types of Classification
      • Nearest Neighbor Classifiers
      • K-nearest Neighbors
      • Probability and Bayes Theorem
      • Support Vector Machines
      • Naive Bayes
      • Decision Tree Classifier
      • Random Forest Classifier
    • Unsupervised Learning: Clustering
      • About Clustering
      • Clustering Algorithms
      • K-means Clustering
      • Hierarchical Clustering
      • Distribution Clustering
    • Model optimization and Boosting
      • Ensemble approach
      • K-fold cross validation
      • Grid search cross validation
      • Ada boost and XG Boost
Course 6
Deep Learning

Deep learning is the most effective skill in AI. The course is intended to provide a complete foundation over the deep learning algorithms that help you to understand the process to build neural networks. The course of deep learning will help you to successfully handle the Machine learning projects needed by the organization today.


Course Content

    • Introduction to Deep Learning
      • What are the Limitations of Machine Learning
      • What is Deep Learning
      • Advantage of Deep Learning over Machine learning
      • Reasons to go for Deep Learning
      • Real-Life use cases of Deep Learning
    • Deep Learning Networks
      • What is Deep Learning Networks
      • Why Deep Learning Networks
      • How Deep Learning Works
      • Feature Extraction
      • Working of Deep Network
      • Training using Backpropagation
      • Variants of Gradient Descent
      • Types of Deep Networks
      • Feed forward neural networks (FNN)
      • Convolutional neural networks (CNN)
      • Recurrent Neural networks (RNN)
      • Generative Adversal Neural Networks (GAN)
      • Restrict Boltzman Machine (RBM)
    • Deep Learning with Keras
      • Define Keras
      • How to compose Models in Keras
      • Sequential Composition
      • Functional Composition
      • Predefined Neural Network Layers
      • What is Batch Normalization
      • Saving and Loading a model with Keras
      • Customizing the Training Process
      • Intuitively building networks with Keras
    • Convolutional Neural Networks (CNN)
      • Introduction to Convolutional Neural Networks
      • CNN Applications
      • Architecture of a Convolutional Neural Network
      • Convolution and Pooling layers in a CNN
      • Understanding and Visualizing CNN
      • Transfer Learning and Fine-tuning Convolutional Neural Networks
    • Recurrent Neural Network (RNN)
      • Intro to RNN Model
      • 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
      • Time Series Forecasting
    • Natural Language Processing
      • NLP with python
      • Bags of words
      • Stemming
      • Tokenization
      • Lemmatization
      • TF-IDF
      • Sentiment Analysis
    • Overview of Tensor Flow
      • What is Tensor Flow
      • Tensor Flow code-basics
      • Graph Visualization
      • Constants, Placeholders, Variables
      • Tensor flow Basic Operations
      • Linear Regression with Tensor Flow
      • Logistic Regression with Tensor Flow
      • K Nearest Neighbor algorithm with Tensor Flow
      • K-Means classifier with Tensor Flow
      • Random Forest classifier with Tensor Flow
    • Neural Networks Using Tensor Flow
      • Quick recap of Neural Networks
      • Activation Functions, hidden layers, hidden units
      • Illustrate & Training a Perceptron
      • Important Parameters of Perceptron
      • Understand limitations of A Single Layer Perceptron
      • Illustrate Multi-Layer Perceptron
      • Back-propagation – Learning Algorithm
      • Understand Back-propagation – Using Neural Network Example
      • TensorBoard
Course 7
Microsoft Power BI

The Power BI course assists the user to understand the way to install Power BI desktop also by understanding and developing the workshop and insights using the data. It offers tools and techniques that are used to visualize and analyze data. The course will help you to learn and grab insights on everything an organization need; to manage the data with Power BI.


Course Content

    • Introduction to Power BI
      • Overview of BI concepts
      • Why we need BI
      • Introduction to SSBI
      • SSBI Tools
      • Why Power BI
      • What is Power BI
      • Building Blocks of Power BI
      • Getting started with Power BI Desktop
      • Get Power BI Tools
      • Introduction to Tools and Terminology
      • Dashboard in Minutes
      • Refreshing Power BI Service Data
      • Interacting with your Dashboards
      • Sharing Dashboards and Reports
    • Power BI Desktop
      • Power BI Desktop
      • Power BI Dashboards
      • Power BI Q & A
      • Extracting data from various sources
      • Workspaces in Power BI
      • Data Transformation
      • Measures and Calculated Columns
      • Query Editor
    • Modelling with Power BI
      • Introduction to Modelling
      • Modelling Data
      • Manage Data Relationship
      • Optimize Data Models
      • Cardinality and Cross Filtering
      • Default Summarization & Sort by
      • Creating Calculated Columns
      • Creating Measures & Quick Measures
    • Data Analysis Expressions (DAX)
      • What is DAX
      • Data Types in DAX
      • Calculation Types
      • Syntax, Functions, Context Options
      • DAX Functions
      • Date and Time
      • Time Intelligence
      • Information
      • Logical
      • Mathematical
      • Statistical
      • Text and Aggregate
      • Measures in DAX
    • Publishing and Sharing
      • Introduction and Sharing Options Overview
      • Publish from Power BI Desktop and Publish to Web
      • Share Dashboard with Power BI Service
      • Workspaces and Apps (Power BI Pro) and Content Packs (Power BI Pro)
      • Print or Save as PDF and Row Level Security (Power BI Pro)
      • Export Data from a Visualization and Publishing for Mobile Apps
      • Export to PowerPoint and Sharing Options Summary
    • Refreshing Datasets
      • Understanding Data Refresh
      • Personal Gateway (Power BI Pro and 64-bit Windows)
      • Replacing a Dataset and Troubleshooting Refreshing
Course 8
Hadoop- Apache Spark and Scala Certification

This course is designed to help you master Apache Spark and Scala. The course includes modules such as Spark RDD, Spark SQL, and Spark ML library. The importance of this course is defined by the current growth of the global spark market that provides an estimated growth of 4.2 billion dollars by 2022 with a CAGR of 67%. This explains the need for Hadoop. The Spark will help you understand the crucial and demand apache skills providing a competitive advantage to your career.


Course Content

    • Introduction to Big Data Hadoop and Spark
      • What is Big Data
      • Big Data Customer Scenarios
      • Understanding BIG Data: Summary
      • Few Examples of BIG Data
      • Why BIG data is a BUZZ
      • How Hadoop Solves the Big Data Problem
      • What is Hadoop
      • Hadoop’s Key Characteristics
      • Hadoop Cluster and its Architecture
      • Hadoop: Different Cluster Modes
      • Why Spark is needed
      • What is Spark
      • How Spark differs from other frameworks
      • Spark at Yahoo!
    • BIG Data Analytics and why it’s a Need Now
      • What is BIG data Analytics
      • Why BIG Data Analytics is a ‘need’ now
      • BIG Data: The Solution
      • Implementing BIG Data Analytics – Different Approaches
    • Traditional Analytics vs. BIG Data Analytics
      • The Traditional Approach: Business Requirement Drives Solution Design
      • The BIG Data Approach: Information Sources drive Creative Discovery
      • Traditional and BIG Data Approaches
      • BIG Data Complements Traditional Enterprise Data Warehouse
      • Traditional Analytics Platform v/s BIG Data Analytics Platform
    • Big Data Technologies Scala
      • What is Scala
      • Scala in other Frameworks
      • Introduction to Scala REPL
      • Basic Scala Operations
      • Variable Types in Scala
      • Control Structures in Scala
      • Understanding the constructor overloading,
      • Various abstract classes
      • The hierarchy types in Scala,
      • For-each loop, Functions and Procedures
      • Collections in Scala- Array
    • Spark
      • Overview to Spark
      • Spark installation, Spark configuration,
      • Spark Components & its Architecture
      • Spark Deployment Modes
      • Limitations of Map Reduce in Hadoop
      • Working with RDDs in Spark
      • Introduction to Spark Shell
      • Deploying Spark without Hadoop
      • Parallel Processing
      • Spark MLLib - Modelling Big Data with Spark
    • Apache Kafka and Flume
      • What is Kafka Why Kafka
      • Configuring Kafka Cluster
      • Kafka architecture
      • Producing and consuming messages
      • Operations, Kafka monitoring tool
      • Need of Apache Flume
      • What is Apache Flume
      • Understanding the architecture of Flume
      • Basic Flume Architecture
Course 9
Tableau - For Data Visualization

The organizations are based on a process that is completely data-driven. This course is intended to help you master the key concepts to examine, learn and navigate the features of Tableau. Getting upgraded with Tableau will help you to perform the exploratory analysis by creating and designing visualizations and dashboards to provide relevant information for the intended audience.


Course Content

    • Introduction to Tableau
      • What is Tableau
      • Features of Tableau
      • Architecture of Tableau
      • Installation of Tableau Desktop
      • The interface of Tableau (Layout, Toolbars, Data Pane, Analytics Pane etc.)
      • How to Start with Tableau
      • Top Chart Types in Tableau
      • Introduction to the various File Type
      • Quick Introduction to the User Interface in Tableau
      • How to Create Data Visualization Using Tableau feature “Show Me”
      • Reorder & Remove Visualization Fields
      • How to Sort & Filter Data
      • How to Create a Calculated Field
      • How to Perform Operations using Cross-Tab
      • Working with Workbook Data & Worksheets
      • How to Create a Packaged Workbook
    • Data Preparation
      • Connecting to Different Data Sources
      • Live vs. Extract Connection
      • Data Source Editor
      • Managing Metadata and Extracts
      • Pivoting & Splitting
      • Data Interpreter: Clean dirty data
      • Join
      • Complex Joins
      • Union
      • Data Blending & when it is required
    • Data Visualization
      • Data Visualization Principles
        • What is Data Visualization
        • Data Interpretation
        • Pivot Tables
        • Split Tables
        • Responsive Tool Tips
        • Radial & Lasso Selection
        • Right Click Filtering
        • Creating Calculated Fields
        • Manipulating Text-Left and Right Functions
      • Basic Data Visualization
        • Heat Map
        • Highlight Table
        • Bar Charts
        • Line Charts
        • Pie Chart
        • Scatter Plot
        • Word Cloud
        • Tree Map
        • Blended Axis
        • Dual Axis
    • Managing Your Data
      • Filters
      • Top & Bottom N Filtering
      • Filtering order of operations
      • Sorting
      • Calculations - String, Basic, Date & Logic
      • Parameters
      • Working with Dates
      • Table Calculation
      • Discrete vs Continuous Measures
    • Grouping and Formatting Data
      • Grouping Data
        • Groups, Sets, Hierarchies, Bins, Combined Fields
      • Formatting
        • Size, Updating Axis, Colors, Borders, Transparency, Chart Lines, Trend Line, Forecasting, Reference Line, Mark Labels, Annotations
    • Dashboard Design
      • Canvas Selection & Adjusting Sizes
      • Tiled Objects
      • Floating Objects
      • Pixel Perfect Alignment
      • Summary Box
      • Chart Titles & Captions
      • Adding Images & Text
      • Adding Background Color
      • Adding Shading
      • Adding Separator Lines
      • Dynamic Chart Titles
      • Information Icons
      • Creating a Story
Course 10
Data Science Masters - Live Projects

The training offers complete career transitioning projects based on the current needs of the organization. These projects are guided by experts and help you to add more value to your profile. You will learn to initiate data science projects based on a high-level perspective helping you to understand and articulate the innovative solutions for topical real-time data science projects.


Course Content

    • Data Science Masters - Live Projects
      • Managing credit card Risks
      • Bank Loan default classification
      • YouTube Viewers prediction
      • Super store Analytics (E-commerce)
      • Buying and selling cars prediction (like OLX process)
      • Advanced House price prediction
      • Analytics on HR decisions
      • Survival of the fittest
      • Twitter Analysis
      • Flight price prediction
CertificatesMaster's Program Certificate

You will get certificate after completion of program

Course Structure
  • - 8 Months Online Program
  • - 130+ Hours of Intensive Learning
  • - 9+ Assigments & 6+ Projects
  • - 2 Live Projects
Career Assistance
  • - Build an Impressive Resume
  • - Get Tips from Trainer to Clear Interviews
  • - Attend Mock-Up Interviews with Experts
  • - Get Interviews & Get Hired

Get Ahead with Croma Campus master Certificate

Earn your certificate

Our Master program is exhaustive and this certificate is proof that you have taken a big leap in mastering the domain.

Differentiate yourself with a Masters Certificate

The knowledge and skill you've gained working on projects, simulation, case studies will set you ahead of competition.

Share your achievement

Talk about it on Linkedin, Twitter, Facebook, boost your resume or frame it- tell your friend and colleagues about it.

Industry Project


Real-life Case Studies

Work on case studies based on top industry frameworks and connect your learning with real-time industry solutions right away.


Best Industry-Practitioners

All of our trainers and highly experienced, passionate about teaching and worked in the similar space for more than 3 years.


Acquire essential Industrial Skills

Wisely structured course content to help you in acquiring all the required industrial skills and grow like a superstar in the IT marketplace.


Hands-on Practical Knowledge

Case studies based on top industry frameworks help you to relate your learning with real-time based industry solutions.


Collaborative Learning

Take your career at the top with collaborative learning at the Croma Campus where you could learn and grow in groups.


Assignment & Quizzes

Practice different assignments and quizzes on different topics or at the end of each module to evaluate your skills and learning speed.

Placement & Recruitment Partners

Placement Assistance

We provide 100 percent placement assistance and most of our students are placed after completion of the training in top IT giants. We work on your resume, personality development, communication skills, soft-skills, along with the technical skills.

CAREERS AND SALARIES IN Masters in Data Science

With the global focus shifting towards the data; Data Science has become one of the most desired titles and many aspirants are looking to learn and grow their careers in it. Below are the best data science courses and the salary slabs that will help you to explore the various job titles included and the salary estimates.


The job role of a data engineer is to easily extract, transform the data into easy to use format for further business analysis. The job role involves the management of the structure, sources, storage, and quality of the data. This makes it easy to analyze and use for other analysts.
The Salary slab for the fresher to experts in this career falls from 5 – 13lakhs per annum.

The data scientist and business analyst's main job role relates to data mining and understanding the problems of the business to extract perfect solutions through data. Data mining is a process of application of statistics that easily understands the need of the data and collects the predictive models to reveal the latest trend in data. Through this process, the data scientists are able to analyze the business problem and translate the data question into predictive models.
The Salary slab for the fresher to experts in this profession falls from 3 lakhs to 9 lakhs per annum.

Cloud architect and engineer manages work process that relates to the designing and executing enterprise platform and infrastructure required for cloud and distributed computing. The main role of a cloud architect or engineer is to analyze the computing requirement and makes sure that the systems are securely incorporated with the current application and business use.
The Salary slab for the fresher to experts in this profession falls from 16 lakhs to 50 lakhs per annum

The database analyst and database Developer handle the work process that involved designing, maintaining, and deploying the database; handling the high volume data, and assisting with the complex data transaction to offer a specific group of services.
The Salary slab for the fresher to experts in this profession falls from 3 lakhs to 15 lakhs per annum

BI engineer or BI developer can also be known as BI analyst or data strategist offers the core responsibilities that include bringing improvement in handling the back end source. This helps the organization to gain increased accuracy and simplicity. They mainly assist with identifying the upcoming opportunities and aid with the best practices to report and analyze data integrity, validation, test designing, analysis, and much more. The Salary slab for the fresher to experts in this profession falls from 6 lakhs to 15 lakhs per annum


On the completion of the course, you may work in various domains like manufacturing, It, healthcare, telecom, and more. Also, most of the students get 200 percent hike after completing this course. The average you will get 6 lac p.a. and for a little more efforts you may acquire salary packages up to 12 lacs p.a.

Aon Hewitt
Tech Mahindra
Walmart Labs

Admission Process

date timeImportant Date & Time

You can apply for the master program online at our site. Mark the important date and time related to the program and stay in touch with our team to get the information about the program in detail.

enrollEnrollment Criteria

Once you submit your profile online, it will be reviewed by our expert team closely for the eligibility like graduation degree, basic programming skills, etc. Eligible candidates can move to the next step quickly.

finalFinal Enrollment Process

Eligible candidates have to appear for the online assessment based on your graduation and basic programming knowledge. Candidates who clear the exam will appear for the interview and finally they can join the program.

Get a chance to win a scholarship up to
₹ 63,750 (Excluding of GST)

Frequently Asked Questions

The course aims to provide expert knowledge in statistical modeling, data processing, management, machine learning, data visualization, research design, building user interfaces, and software engineering. The need of the Data Scientist is mainly to promote the organization's work and to bring in the change for the desired growing needs.

Data Science offers multiple courses so it depends upon the data science domains that you are in. With our Data Science courses, one can learn skills in a respective module which helps you to complete the master program and get a perfect salary from the top organizations like Google, TCS, Accenture, and Amazon.

Croma Campus being one of the top institutes in Noida aims to provide updated course content. Providing practical and theoretical implementation on live industry projects and offer multiple training processes to provide hands-on knowledge for obtaining a good job and learning. We assist with video tutorials and provide professional trainers that help to provide the knowledge needed in the organizations today.

If you have a passion for working for data-driven sources and the organizations that need data-driven processes. Then you should opt for the data science master's program. The data science program will help you to understand the patterns and analyze the insights extracted from the data to bring in the desired change. Data science will help you to understand the work process of data involved with machine learning and artificial intelligence promoting your career in working for the top technology.

The basic skills that every data science enthusiast should have are related to Math, statistics, programming language, and understanding the data sets. Other than these technical skills a data scientist must possess intellectual curiosity and communication.

As today's industries are looking for certified resources to implement a cost-effective and fast working process. To start your career in top organizations it is important to need to get certified and trained. For certification, you can start by enrolling for the demo sessions that are free of cost and help you to know more about the course, and select the best time to get your training done. We at Croma Campus help you in all such procedures and provide you the certificate that is important to prove your eligibility.

After selecting the course, you can select the perfect service provided by the Croma Campus institute. These services are specially tailored according to the need of the aspirant’s preference. Well, these services can also be customized so that no learner finds it hard to learn and schedule the classes according to the preferred time. The additional benefits offer real-time case studies, exposure through practical and theoretical classes, various training services, instructor LED-based online training, and much more.

Well, there are as such no prerequisites to learn the course. One with no prior knowledge of programming language and statistics can enroll for the course. The average time to complete the professional certificate in data science would take around 6 to 12 months.

If you like our Curriculum

What You will get Benefit
from this Program

  • Simulation Test Papers
  • Industry Case Studies
  • 61,640+ Satisfied Learners
  • 140+ Training Courses
  • 100% Certification Passing Rate
  • Live Instructor Online Training
  • 100% Placement Assistance
I’m interested in the program

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