Masters in Data Analytics

Master's Program 4.9 out of 5 rating votes 9563

Master the fundamentals of data analytics and learn how to use it for retrieval of business insights.

INR 90000

Excluding GST

100% Placement


Ranked#2 among Top Full-Time

Masters in Data Analytics in India- 2010-2023

star date
Start Date : 27th Mar 2023Duration : 9 Months
Format : Live Online /Self-Paced/Classroom
Future of Masters in Data Analytics

₹3 LPA to ₹12 LPA

A data analyst, on average, can make around ₹3 Lakh PA. On the other hand, an experienced analyst can make around ₹12 Lakh PA.


Job Opportunities

As per the reports of IBM, the number of data analyst jobs in the United States of America will cross the mark of 2,720,000 in the upcoming years.


Future Analytics

As per a survey, most of the companies, by the year 2023, will start storing their data for analysis in the public cloud warehouses.


Masters in Data Analytics

4.9 out of 5 rating vote 9563

Master the fundamentals of data analytics and learn how to use it for retrieval of business insights..

INR 90000 + GST
100% Placement Assistance

Program Overview

The data analytics training program will help you gain in detail knowledge of vital data analytics technologies. Moreover, the course will help you master the skills that are necessary to become a confident data analyst. For example, you will master skills like Tableau, R, Python, SQL, etc. The main aim of this training program is to help students develop skills that are essential for marketing themselves as skilled data analytics professionals. After going through this course, you can get a job as a:

  • Junior Data Analyst
  • Junior Data Scientist
  • Finance Analyst
  • Business Intelligence Analyst
  • Data Engineer

There are lots of excellent opportunities from which you can choose after completing the data analytics training program. This is because today, a large number of organizations and government agencies rely upon data analytics to improve their company's performance. As a result, today, every organization is searching for data experts such as data scientists, data engineers, etc. that can help them transform their data into powerful business insights. Thus, pursuing your career in the data analytics industry can be very beneficial for you.

  • Web_IconAccording to a survey of IBM, the number of data analyst jobs in the United States of America will cross the mark of 2,720,000 in the upcoming years.
  • branAmple of excellent opportunities are available for students who pursue their careers in the data analytics industry. For example, you can get a job as a data analyst, data scientist, etc.
  • polyAs per a survey, most of the companies, by the year 2023, will start storing their data for analysis in the public cloud warehouses.
  • analyticsAs per a survey, the data analytics market is expected to grow with a compound annual growth rate of 25% till the year 2030.

The popularity of data analytics is growing with every passing day. This course will help you gain in detail knowledge of vital data analytics technologies. Furthermore, the main aim of the training program is to help students become familiar with the advantages of using data analytics.

m IconWith project-based training and lots of quizzes and assignments, you will gain the knowledge and skills that are essential for getting placed as an analyst in a reputed company.

 m IconAfter completing the data analytics training program, you can easily get placed in a respected company or firm with handsome remuneration of ₹ 3,00,000-₹12,00,000.

icon 3As per a survey, 2,720,000 job opportunities will be created in the data analytics industry in the upcoming years.

The main aim of the data analytics training program is to make students familiar with all the concepts of data analytics and various tools used for performing data analytics. Besides this, you will learn to use data analytics to help a firm grow and improve its performance and profits.
Things you will learn:

  • Data fundamentals
  • How to work with different data tools?
  • Importance of data analytics
  • Data analysis

The objective of this data analytics course is to give students quality data analytics training to students and make them experts in working with different data analytics technologies. The course is developed in partnership with data experts and keeping in mind the changing demands of the data analytics industry.

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Tools Covered of Masters in Data Analytics

Power BI
Cloud Computing

Masters in Data Analytics Curriculum

Course 1
Python for Data Analyst

Data Ananyst 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

    • Introduction To Python
      • Installation and Working with Python
      • Understanding Python variables
      • Python basic Operators
      • Understanding the Python blocks.
    • Python Keyword and Identiers
      • Python Comments, Multiline Comments.
      • Python Indentation
      • Understating the concepts of Operators
        • Arithmetic
        • Relational
        • Logical
        • Assignment
        • Membership
        • Identity
    • 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 using Numeric data types
      • Using string data type and string operations
      • Understanding Non-numeric data types
      • Understanding the concept of Casting and Boolean.
      • Strings
      • List
      • Tuples
      • Dictionary
      • Sets
    • Control Structure & Flow
      • Statements – if, else, elif
      • How to use nested IF and Else in Python
      • Loops
      • Loops and Control Statements.
      • Jumping Statements – Break, Continue, 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 Loop in Python
      • Use If and Else in for and While Loop
      • Examples of Looping with Break and Continue Statement
      • How to use IN or NOT IN keyword in Python Loop.
    • Python Function, Modules and Packages
      • Python Syntax
      • Function Call
      • Return Statement
      • Arguments in a function – Required, Default, Positional, Variable-length
      • Write an Empty Function in Python –pass statement.
      • Lamda/ Anonymous Function
      • *args and **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
      • Random functions in python
      • Programming using functions, modules & external packages
      • Map, Filter and Reduce function with Lambda Function
      • More example of Python Function
    • Python Date Time and Calendar
      • Day, Month, Year, Today, Weekday
      • IsoWeek day
      • Date Time
      • Time, Hour, Minute, Sec, Microsec
      • Time Delta and UTC
      • StrfTime, Now
      • Time stamp and Date Format
      • Month Calendar
      • Itermonthdates
      • Lots of Example on Python Calendar
      • Create 12-month Calendar
      • Strftime
      • Strptime
      • Format Code list of Data, Time and Cal
      • Locale’s appropriate date and time
    • 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
    • Tuple
      • What is Tuple
      • Tuple Creation
      • Accessing Elements in Tuple
      • Changing a Tuple
      • Tuple Deletion
      • Tuple Count
      • Tuple Index
      • Tuple Membership
      • TupleBuilt in Function (Length, Sort)
    • 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
      • What is Set
      • Set Creation
      • Add element to a Set
      • Remove elements from a Set
      • PythonSet Operations
      • Frozen Sets
    • Strings
      • What is Set
      • Set Creation
      • Add element to a Set
      • Remove elements from a Set
      • PythonSet Operations
    • Python Exception Handling
      • Python Errors and Built-in-Exceptions
      • Exception handing Try, Except and Finally
      • Catching Exceptions in Python
      • Catching Specic 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 Database Interaction
      • SQL Database connection using
      • Creating and searching tables
      • Reading and Storing cong information on database
      • Programming using database connections
    • Contacting user Through Emails Using Python
      • Installing SMTP Python Module
      • Sending Email
      • Reading from le and sending emails to all users
    • Reading an excel
      • Working With Excel
      • Reading an excel le using Python
      • Writing to an excel sheet using Python
      • Python| Reading an excel le
      • Python | Writing an excel le
      • Adjusting Rows and Column using Python
      • ArithmeticOperation in Excel le.
      • Play with Workbook, Sheets and Cells in Excel using Python
      • Creating and Removing Sheets
      • Formatting the Excel File Data
      • More example of Python Function
    • Complete Understanding of OS Module of Python
      • Check Dirs. (exist or not)
      • How to split path and extension
      • How to get user prole detail
      • Get the path of Desktop, Documents, Downloads etc.
      • Handle the File System Organization using OS
      • How to get any les and folder’s details using OS
Course 2
Data Analysis and Visualization

Data visualization is the graphical way to representation of information and data. By using visual elements like graphs, maps and charts. Data visualization tools provide an accessible easy way to see and understand the data.


Course Content

    • Data Analysis and Visualization using Pandas.
      • Statistics
        • Categorical Data
        • Numerical Data
        • Mean
        • Median
        • Mode
        • Outliers
        • Range
        • Interquartile range
        • Correlation
        • Standard Deviation
        • Variance
        • Box plot
      • Pandas
        • Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to les
        • How to get record specic 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 les 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 Aggre gate Function
        • Complete Understanding of Pivot Table Data Slicing using iLoc and Loc property (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 Data Frame and 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)
    • Data Analysis and Visualization using NumPy and MatPlotLib
      • NumPy
        • Introduction to NumPy Numerical Python
        • Importing NumPy and Its Properties
        • NumPy Arrays
        • Creating an Array from a CSV
        • Operations an Array from a CSV
        • 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’s Mean and Axis
        • NumPy’s Mode, Median and Sum Function
        • NumPy’s Sort 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 Data Visualization with Seaborn
      • 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 whisk
        • 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
        • Well done! What’s next
Course 3
Databases – MS SQL and SQL Queries

Microsoft SQL Server is a relational database management system (RDBMS) that supports a wide variety of transaction processing, business intelligence and analytics applications in corporate IT environments. In order to experiment with data through the creation of test environments, data scientists make use of SQL as their standard tool, and to carry out data analytics with the data that is stored in relational databases like Oracle, Microsoft SQL, MySQL, we need SQL.


Course Content

    • SQL Server Fundamentals
      • SQL Server 2019 Installation
      • Service Accounts & Use, Authentication Modes & Usage, Instance Congurations
      • SQL Server Features & Purpose
      • Using Management Studio (SSMS)
      • Conguration Tools & SQLCMD
      • Conventions & Collation
    • SQL Server 2019 Database Design
      • SQL Database Architecture
      • Database Creation using GUI
      • Database Creation using T-SQL scripts
      • DB Design using Files and File Groups
      • File locations and Size parameters
      • Database Structure modications
    • SQL Tables in MS SQL Server
      • SQL Server Database Tables
      • Table creation using T-SQL Scripts
      • Naming Conventions for Columns
      • Single Row and Multi-Row Inserts
      • Table Aliases
      • Column Aliases & Usage
      • Table creation using Schemas
      • Basic INSERT
      • UPDATE
      • DELETE
      • SELECT queries and Schemas
      • Use of WHERE, IN and BETWEEN
      • Variants of SELECT statement
      • ORDER BY
      • GROUPING
      • HAVING
      • ROWCOUNT and CUBE Functions
    • Data Validation and Constraints
      • Table creation using Constraints
      • NULL and IDENTITY properties
      • UNIQUE KEY Constraint and NOT NULL
      • PRIMARY KEY Constraint & Usage
      • CHECK and DEFAULT Constraints
      • Naming Composite Primary Keys
      • Disabling Constraints & Other Options
    • Views and Row Data Security
      • Benets of Views in SQL Database
      • Views on Tables and Views
      • Issues with Views and ALTER TABLE
      • Common System Views and Metadata
      • Common Dynamic Management views
      • Working with JOINS inside views
    • Indexes and Query tuning
      • Need for Indexes & Usage
      • Indexing Table & View Columns
      • Index SCAN and SEEK
      • INCLUDED Indexes & Usage
      • Materializing Views (storage level)
      • Composite Indexed Columns & Keys
      • Indexes and Table Constraints
      • Primary Keys & Non-Clustered Indexes
    • Stored Procedures and Benets
      • Why to use Stored Procedures
      • Types of Stored Procedures
      • Use of Variables and parameters
      • INPUT and OUTPUT parameters
      • System level Stored Procedures
      • Dynamic SQL and parameterization
    • System functions and Usage
      • Scalar Valued Functions
      • Types of Table Valued Functions
      • System Functions and usage
      • Date Functions
      • Time Functions
      • String and Operational Functions
      • ROW_COUNT
      • GROUPING Functions
    • Triggers, cursors, memory limitations
      • Why to use Triggers
      • DML Triggers and Performance impact
      • INSERTED and DELETED memory tables
      • Data Audit operations & Sampling
      • Database Triggers and Server Triggers
      • Bulk Operations with Triggers
    • Cursors and Memory Limitations
      • Cursor declaration and Life cycle
      • STATIC
      • DYNAMIC
      • SCROLL Cursors
      • FORWARD_ONLY and LOCAL Cursors
      • KEYSET Cursors with Complex SPs
    • Transactions Management
      • ACID Properties and Scope
      • EXPLICIT Transaction types
      • IMPLICIT Transactions and options
      • AUTOCOMMIT Transaction and usage
      • SAVEPOINT and Query Blocking
Course 4
Statistics for Data Analyst

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 Data Analytics
      • What is Analytics & Data Science
      • Common Terms in Data Science
      • What is data
      • Classication 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 Data Science tools & their popularity.
      • Data Science Methodology & problem-solving framework.
      • List of steps in Data Science projects
      • Identify the most appropriate solution design for the given problem statement
      • Project plan for Data Science project & key milestones based on effort estimates
      • Build Resource plan for Data Science 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, ltering, duplicates, merging, append ing, sub setting, derived variables, sampling, Data type conversions, renaming, formatting.
      • Normalizing data
    • Feature Engineering in Data Science
      • 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/ densi ty 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
      • Spread and Dispersion
      • 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
    • EDA (Exploratory Data Analysis)
      • 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
Analytics with Excel

Excel is one of the most popular data analysis tool, to help visualize and gain insights from your data. Analytics with Excel helps you to boost your Microsoft Excel skills.


Course Content

    • Understanding Concepts of Excel
      • Creation of Excel Sheet Data
      • Range Name, Format Painter
      • Conditional Formatting, Wrap Text, Merge & Centre
      • Sort, Filter, Advance Filter
      • Different type of Chart Creations
      • Auditing, (Trace Precedents, Trace Dependents)Print Area
      • Data Validations, Consolidate, Subtotal
      • What if Analysis (Data Table, Goal Seek, Scenario)
      • Solver, Freeze Panes
      • Various Simple Functions in Excel(Sum, Average, Max, Min)
      • Real Life Assignment work
    • Ms Excel Advance
      • Advance Data Sorting
      • Multi-level sorting
      • Restoring data to original order after performing sorting
      • Sort by icons
      • Sort by colours
      • Lookup Functions
        • Lookup
        • VLookup
        • HLookup
      • Subtotal, Multi-Level Subtotal
      • Grouping Features
        • Column Wise
        • Row Wise
      • Consolidation With Several Worksheets
      • Filter
        • Auto Filter
        • Advance Filter
      • Printing of Raw & Column Heading on Each Page
      • Workbook Protection and Worksheet Protection
      • Specified Range Protection in Worksheet
      • Excel Data Analysis
        • Goal Seek
        • Scenario Manager
      • Data Table
        • Advance use of Data Tables in Excel
        • Reporting and Information Representation
      • Pivot Table
        • Pivot Chat
        • Slicer with Pivot Table & Chart
      • Generating MIS Report In Excel
        • Advance Functions of Excel
        • Math & Trig Functions
      • Text Functions
      • Lookup & Reference Function
      • Logical Functions & Date and Time Functions
      • Database Functions
      • Statistical Functions
      • Financial Functions
      • Functions for Calculation Depreciation
    • MIS Reporting & Dash Board
      • Dashboard Background
      • Dashboard Elements
      • Interactive Dashboards
      • Type of Reporting In India
        • Reporting Analyst
        • Indian Print Media Reporting
      • Audit Report
      • Accounting MIS Reports
      • HR Mis Reports
      • MIS Report Preparation Supplier, Exporter
      • Data Analysis
        • Costing Budgeting Mis Reporting
        • MIS Report For Manufacturing Company
        • MIS Reporting For Store And Billing
      • Product Performance Report
      • Member Performance Report
      • Customer-Wise Sales Report
      • Collections Report
      • Channel Stock Report
      • Prospect Analysis Report
      • Calling Reports
      • Expenses Report
      • Stock Controller MIS Reporting
      • Inventory Statement
      • Payroll Report
      • Salary Slip
      • Loan Assumption Sheet
      • Invoice Creation
Course 6
Analytics 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
      • Interacting with your Dashboards
      • Sharing Dashboards and Reports
    • Power BI Desktop
      • Power BI Desktop
      • Extracting data from various sources
      • Workspaces in Power BI
    • Power BI Data Transformation
      • Data Transformation
      • Query Editor
      • Connecting Power BI Desktop to our Data Sources
      • Editing Rows
      • Understanding Append Queries
      • Editing Columns
      • Replacing Values
      • Formatting Data
      • Pivoting and Unpivoting Columns
      • Splitting Columns
      • Creating a New Group for our Queries
      • Introducing the Star Schema
      • Duplicating and Referencing Queries
      • Creating the Dimension Tables
      • Entering Data Manually
      • Merging Queries
      • Finishing the Dimension Table
      • Introducing the another DimensionTable
      • Creating an Index Column
      • Duplicating Columns and Extracting Information
      • Creating Conditional Columns
      • Creating the FACT Table
      • Performing Basic Mathematical Operations
      • Improving Performance and Loading Data into the Data Model
    • 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
      • Measures and Calculated Columns
      • ROW Context and Filter Context in DAX
      • Operators in DAX - Real-time Usage
      • Quick Measures in DAX - Auto validations
      • In-Memory Processing DAX Performance
    • Power BI Desktop Visualisations
      • How to use Visual in Power BI
      • What Are Custom Visuals
      • Creating Visualisations and Colour Formatting
      • Setting Sort Order
      • Scatter & Bubble Charts & Play Axis
      • Tooltips and Slicers, Timeline Slicers & Sync Slicers
      • Cross Filtering and Highlighting
      • Visual, Page and Report Level Filters
      • Drill Down/Up
      • Hierarchies and Reference/Constant Lines
      • Tables, Matrices & Conditional Formatting
      • KPI's, Cards & Gauges
      • Map Visualizations
      • Custom Visuals
      • Managing and Arranging
      • Drill through and Custom Report Themes
      • Grouping and Binning and Selection Pane, Bookmarks & Buttons
      • Data Binding and Power BI Report Server
    • Introduction to Power BI Dashboard and Data Insights
      • Why Dashboard and Dashboard vs Reports
      • Creating Dashboards
      • Conguring a Dashboard Dashboard Tiles, Pinning Tiles
      • Power BI Q&A
      • Quick Insights in Power BI
    • Direct Connectivity
      • Custom Data Gateways
      • Exploring live connections to data with Power BI
      • Connecting directly to SQL Server
      • Connectivity with CSV & Text Files
      • Excel with Power BI Connect Excel to Power BI, Power BI Publisher for Excel
      • Content packs
      • Update content packs
    • Publishing and Sharing
      • Introduction and Sharing Options Overview
      • Publish from Power BI Desktop and Publish to Web
      • Share Dashboard with Power BI Service
      • Workspaces (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
      • 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 7
Analytics with Tableau

Tableau is an end-to-end data analytics platform that allows you to prep, analyze, collaborate, and share your data insights. Tableau excels in self-service visual analysis, allowing people to ask new questions of governed big data and easily share those insights across the organization.


Course Content

    • Introduction to Data Preparation using Tableau Prep
      • Data Visualization
      • Business Intelligence tools
      • Introduction to Tableau
      • Tableau Architecture
      • Tableau Server Architecture
      • VizQL Fundamentals
      • Introduction to Tableau Prep
      • Tableau Prep Builder User Interface
      • Data Preparation techniques using Tableau Prep Builder tool
    • Data Connection with Tableau Desktop
      • Features of Tableau Desktop
      • Connect to data from File and Database
      • Types of Connections
      • Joins and Unions
      • Data Blending
      • Tableau Desktop User Interface
    • Basic Visual Analytics
      • Visual Analytics
      • Basic Charts Bar Chart, Line Chart, and Pie Chart
      • Hierarchies
      • Data Granularity
      • Highlighting
      • Sorting
      • Filtering
      • Grouping
      • Sets
    • Calculations in Tableau
      • Types of Calculations
      • Built-in Functions (Number, String, Date, Logical and Aggregate)
      • Operators and Syntax Conventions
      • Table Calculations
      • Level of Detail (LOD) Calculations
      • Using R within Tableau for Calculations
    • Advanced Visual Analytics
      • Parameters
      • Tool tips
      • Trend lines
      • Reference lines
      • Forecasting
      • Clustering
    • Level of Detail (LOD) Expressions in Tableau
      • Count Customer by Order
      • Profit per Business Day
      • Comparative Sales
      • Profit Vs Target
      • Finding the second order date
      • Cohort Analysis
    • Geographic Visualizations in Tableau
      • Introduction to Geographic Visualizations
      • Manually assigning Geographical Locations
      • Types of Maps
      • Spatial Files
      • Custom Geocoding
      • Polygon Maps
      • Web Map Services
      • Background Images
    • Advanced charts in Tableau
      • Box and Whisker’s Plot
      • Bullet Chart
      • Bar in Bar Chart
      • Gantt Chart
      • Waterfall Chart
      • Pareto Chart
      • Control Chart
      • Funnel Chart
      • Bump Chart
      • Step and Jump Lines
      • Word Cloud
      • Donut Chart
    • Dashboards and Stories
      • Introduction to Dashboards
      • The Dashboard Interface
      • Dashboard Objects
      • Building a Dashboard
      • Dashboard Layouts and Formatting
      • Interactive Dashboards with actions
      • Designing Dashboards for devices
      • Story Points
    • Get Industry Ready
      • Tableau Tips and Tricks
      • Choosing the right type of Chart
      • Format Style
      • Data Visualization best practices
    • Exploring Tableau Online
      • Publishing Workbooks to Tableau Online
      • Interacting with Content on Tableau Online
      • Data Management through Tableau Catalog
      • AI-Powered features in Tableau Online (Ask Data and Explain Data)
      • Understand Scheduling
      • Managing Permissions on Tableau Online
      • Data Security with Filters in Tableau Online
Course 8
Cloud: AWS(Amazon Web Services)

AWS allows you to easily move data between the data lake and purpose-built data services. For example, AWS Glue is a serverless data integration service that makes it easy to prepare data for analytics, machine learning, and application development.


Course Content

    • Introduction to Cloud Computing
      • In this module, you will learn what Cloud Computing is and what are the different models of Cloud Computing along with the key differentiators of different models. We will also introduce you to virtual world of AWS along with AWS key vocabulary, services and concepts.
        • A Short history
        • Client Server Computing Concepts
        • Challenges with Distributed Computing
        • Introduction to Cloud Computing
        • Why Cloud Computing
        • Benefits of Cloud Computing
    • Amazon EC2 and Amazon EBS
      • In this module, you will learn about the introduction to compute offering from AWS called EC2. We will cover different instance types and Amazon AMIs. A demo on launching an AWS EC2 instance, connect with an instance and host ing a website on AWS EC2 instance. We will also cover EBS storage Architecture (AWS persistent storage) and the concepts of AMI and snapshots.
        • Amazon EC2
        • EC2 Pricing
        • EC2 Type
        • Installation of Web server and manage like (Apache/ Nginx)
        • Demo of AMI Creation
        • Exercise
        • Hands on both Linux and Windows
    • Amazon Storage Services S3 (Simple Storage Services)
      • In this module, you will learn how AWS provides various kinds of scalable storage services. In this module, we will cover different storage services like S3, Glacier, Versioning, and learn how to host a static website on AWS.
        • Versioning
        • Static website
        • Policy
        • Permission
        • Cross region Replication
        • AWS-CLI
        • Life cycle
        • Classes of Storage
        • AWS CloudFront
        • Real scenario Practical
        • Hands-on all above
    • Cloud Watch & SNS
      • In this module, you will learn how to monitoring AWS resources and setting up alerts and notifications for AWS resources and AWS usage billing with AWS CloudWatch and SNS.
        • Amazon Cloud Watch
        • SNS - Simple Notification Services
        • Cloud Watch with Agent
    • Scaling and Load Distribution in AWS
      • In this module, you will learn about 'Scaling' and 'Load distribution techniques' in AWS. This module also includes a demo of Load distribution & Scaling your resources horizontally based on time or activity.
        • Amazon Auto Scaling
        • Auto scaling policy with real scenario based
        • Type of Load Balancer
        • Hands on with scenario based
    • AWS VPC
      • In this module, you will learn introduction to Amazon Virtual Private Cloud. We will cover how you can make public and private subnet with AWS VPC. A demo on creating VPC. We will also cover overview of AWS Route 53.
        • Amazon VPC with subnets
        • Gateways
        • Route Tables
        • Subnet
        • Cross region Peering
    • Identity and Access Management Techniques (IAM)
      • In this module, you will learn how to achieve distribution of access control with AWS using IAM.
      • Amazon IAM
        • add users to groups,
        • manage passwords,
        • log in with IAM-created users.
      • User
      • Group
      • Role
      • Policy
    • Amazon Relational Database Service (RDS)
      • In this module, you will learn how to manage relational database service of AWS called RDS.
        • Amazon RDS
        • Type of RDS
        • RDS Failover
        • RDS Subnet
        • RDS Migration
        • Dynamo DB (No SQL DB)
        • Redshift Cluster
        • SQL workbench
        • JDBC / ODBC
    • Multiple AWS Services and Managing the Resources' Lifecycle
      • In this module, you will get an overview of multiple AWS services. We will talk about how do you manage life cycle of AWS resources and follow the DevOps model in AWS. We will also talk about notification and email service of AWS along with Content Distribution Service in this module.
        • Cloud Trail,
    • AWS Architecture and Design
      • In this module, you will cover various architecture and design aspects of AWS. We will also cover the cost planning and optimization techniques along with AWS security best practices, High Availability (HA) and Disaster Recovery (DR) in AWS.
        • AWS High Availability Design
        • AWS Best Practices (Cost +Security)
        • AWS Calculator & Consolidated Billing
    • Migrating to Cloud & AWS
    • Router S3 DNS
      • Public DNS
      • Private DNS
      • Routing policy
      • Records
      • Register DNS
      • Work with third party DNS as well
    • Cloud Formation
      • Stack
      • Templet
      • Json / Ymal
        • Installation of Linux
        • Configuration
        • Manage
        • Installation of app on Linux (apache / Nginx etc)
        • AWS cli configuration on Linux
        • Complete hands-on on Linux.
        • Scenario based lab and practical
        • Each topic and services will be cover with lab and theory.
    • Elastic Beanstalk
    • EFS / NFS (hands-on practice)
    • Hands-on practice on various Topics
    • Linux
Course 9
Cloud: Microsoft Azure Fundamentals

Data Scientists know how to train Predictive Models. So, by enabling them to work together, Microsoft Azure Data Science ensures high-quality models at scale in production. With MLOps incorporated as a part of the Microsoft Azure Data Science platform, Data Scientists can create a discrete pipeline for each model


Course Content

    • Describe Cloud Concepts
      • Identify the benefits and considerations of using cloud services Cloud Computing Basics.
        • Identify the benefits of cloud computing, such as High Availability,Scalability, Elasticity,
        • Agility, and Disaster Recovery
        • Identify the differences between Capital Expenditure (Cap Ex) and Operational.
        • Expenditure (Op Ex)
        • Describe the consumption-based model
      • Describe the differences between categories of cloud services
        • Describe the shared responsibility model
        • Describe Infrastructure-as-a-Service (IaaS),
        • Describe Platform-as-a-Service (PaaS)
        • Describe server less computing
        • Describe Software-as-a-Service (SaaS)
        • Identify a service type based on a use case
      • Describe the differences between types of cloud computing
        • Define cloud computing
        • Describe Public cloud
        • Describe Private cloud
        • Describe Hybrid cloud
        • Compare and contrast the three types of cloud computing Describe Core Azure Services
    • Manage Azure identities and governance (15-20%)
      • Manage Azure AD objects
        • create users and groups
        • manage user and group properties
        • manage device settings
        • perform bulk user updates
        • manage guest accounts
        • configure Azure AD Join
        • configure self-service password reset
        • NOTE Azure AD Connect; PIM
      • Manage role-based access control (RBAC)
        • create a custom role
        • provide access to Azure resources by assigning roles
        • subscriptions
        • resource groups
        • resources (VM, disk, etc.)
        • interpret access assignments
        • manage multiple directories
      • Manage subscriptions and governance
        • configure Azure policies
        • configure resource locks
        • apply tags
        • create and manage resource groups
        • move resources
        • remove RGs
        • manage subscriptions
        • configure Cost Management
        • configure management groups
    • Implement and Manage Storage (10-15%)
      • Manage storage accounts
        • configure network access to storage accounts
        • create and configure storage accounts
        • generate shared access signature
        • manage access keys
        • implement Azure storage replication
        • configure Azure AD Authentication for a storage account
      • Manage data in Azure Storage
        • export from Azure job
        • import into Azure job
        • install and use Azure Storage Explorer
        • copy data by using AZ Copy
      • Configure Azure files and Azure blob storage
        • create an Azure file share
        • create and configure Azure File Sync service
        • configure Azure blob storage
        • configure storage tiers for Azure blobs
    • Deploy and Manage Azure Compute Resources (25-30%)
      • Configure VMs for high availability and scalability
        • configure high availability
        • deploy and configure scale sets
      • Create and configure VMs
        • configure Azure Disk Encryption
        • move VMs from one resource group to another
        • manage VM sizes
        • add data discs
        • configure networking
        • redeploy VMs
      • Create and configure Web Apps
        • create and configure App Service
        • create and configure App Service Plans
    • Configure and Manage Virtual Networking (30-35%)
      • Implement and manage virtual networking
        • create and configure VNET peering
        • configure private and public IP addresses, network routes, network interface,
        • subnets, and virtual network
      • Configure name resolution
        • configure Azure DNS
        • configure custom DNS settings
        • configure a private or public DNS zone
      • Secure access to virtual networks
        • create security rules
        • associate an NSG to a subnet or network interface
        • evaluate effective security rules
        • deploy and configure Azure Firewall
        • deploy and configure Azure Bastion Service
        • NOT Implement Application Security Groups; DDoS
      • Configure load balancing
        • configure Application Gateway
        • configure an internal load balancer
        • configure load balancing rules
        • configure a public load balancer
        • troubleshoot load balancing
        • NOT Traffic Manager and Front Door and Private Link
    • Monitor and Back up Azure Resources (10-15%)
      • Implement backup and recovery
        • configure and review backup reports
        • perform backup and restore operations by using Azure Backup Service
        • create a Recovery Services Vault
        • use soft deletes to recover Azure VMs
Course 10
Data Analyst - 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 analytics projects based on a high-level perspective helping you to understand and articulate the innovative solutions.


Course Content

    • Here is the project list you will going to work on
      • 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
  • - 9 Months Online Program
  • - 120+ Hours of Intensive Learning
  • - 10+ Assigments & 4+ Projects
  • - 4 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 Analytics

Demand for data experts is expected to grow a lot in the market in the coming years. According to a survey, around 2,720,000 data analytics job opportunities will be created in the US job market. Moreover, a data expert can also get a hefty paycheck for his services. As per the Glassdoor website, data analytics can make approximately ₹3,00,000-₹12,00,000 PA. In simple words pursuing your career in the data analytics industry can be very beneficial for students.


Data analysts help a firm in interpreting and representing its data in different forms for extracting or deriving actionable insights. Moreover, an analyst also helps a firm in segregating and simplifying data from various systems. A competent analyst must be proficient in working with different Microsoft products like MS Excel, MS Access, MS SQL, etc. Besides this, he should have adequate knowledge about data mining, data modeling, and various data visualization tools/software. On average, a data analyst can make approximately ₹3,00,000-₹12,00,000 PA.

Data scientist helps a company to collect/analyze data that can be communicated to the stakeholders as actionable insights. A data scientist often works with complex data and requires expertise in data analytics, R programming, various data visualization tools, etc. On average, a data scientist can make approximately ₹5,00,000-₹21,00,000 PA.

A financial analyst is a data analyst who is an expert in the finance domain and uses financial data to provide actionable insights to a firm. This role is perfect for expert data analysts that have a background in finance. Besides this, a financial analyst must be an expert in working with different financial assets such as bonds, stocks, etc. On average, a financial analyst can make approximately ₹3,00,000 to 12,00,000 PA.

A business intelligence analyst uses BI tools for converting raw data of an organization into insights. A business intelligence analyst must be an expert in working with different BI tools and data visualization tools. Furthermore, he should be proficient in creating charts, graphs, and reports. On average, a business intelligence analyst can make approximately ₹4,00,000-₹14,00,000 PA.

A data engineer has to work with massive data sets. Moreover, he is responsible for optimizing the infrastructure as well as the data analytics process of an organization. Data engineers must be proficient in data visualization and programming. Plus, he should also have some sort of experience in developing/testing solutions. On average, a data engineer can make approximately ₹4,00,000-₹16,00,000 PA.


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.


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
₹ 85,500 (Excluding of GST)

Frequently Asked Questions

  • Passion for learning
  • Go-getter attitude
  • Basic computer knowledge
  • Basic knowledge about data analytics

No, you don't need to know how to code for enrolling yourself in this course.

  • Lots of job opportunities
  • Enhances firm's productivity Helps in preventing frauds Gives access to important data insights

  • ISO certified training institute
  • Project-based training
  • Industry recognized certification
  • Learn under a data analytics expert

8 Months.

You can expect to earn ₹3,00,000-₹12,00,000 PA as a data analyst.

Following are the services that you will get from Croma Campus:

  • 100% placement support
  • Resume building
  • Personality development
  • Soft-skills development
  • Mock interviews
  • Technical skills development

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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