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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.

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INR 58000

Excluding GST

Placement
100% Placement

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Ranked#2 among Top Full-Time

Masters in Data Analytics in India- 2010-2022

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

₹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.

Jobs

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.

analytics

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.

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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 58000 + 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
certificate

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

Numpy
Python
Scipy
Tableau
Power BI
Excel
jenkins
Panda
MatPlotLib
Machen Learning
Seaborn
SQL

Masters in Data Analytics Curriculum

Course 1
Python Statistics for Data Analytics

You will first learn the basic statistical concepts for Data Analytics using Python.

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Course Content

    • Things you will learn:
    • 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 2
Data Analytics Overview

Data analytics helps individuals and organizations to make meaningful data by analyzing raw data. Data analysts typically analyzing raw data in order to make meaningful & actionable Data by using various tools.

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Course Content

    • Things you will learn:
      • Dealing with Different Types of Data
      • Data Visualization for Decision making
      • Data Science, Data Analytics, and Machine Learning
      • Data Science Methodology
      • Data Analytics in Different Sectors
      • Analytics Framework and Latest trends
Course 3
Statistics Essentials For Analytics

Statistics Essentials for Data Analytics provides complete knowledge about the Statistical Techniques. In Statistics Essentials For Analytics you will learn how every technique is employed on a real world data set to analyse and conclude insights.

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Course Content

    • Things you will learn:
    • Introduction to Statistics for Analytics
      • Sample or Population Data
      • The Fundamentals of Descriptive Statistics
      • Measures of Central Tendency, Asymmetry, and Variability
      • Practical Example: Descriptive Statistics
    • Distributions
      • Estimators and Estimates
      • Confidence Intervals: Advanced Topics
      • Practical Example: Inferential Statistics
    • Hypothesis Testing
      • Introduction
      • Hypothesis Testing: Let’s Start Testing!
      • Practical Example: Hypothesis Testing
    • The Fundamentals of Regression Analysis
      • Subtleties of Regression Analysis
      • Assumptions for Linear Regression Analysis
      • Dealing with Categorical Data
      • Practical Example: Regression Analysis
Course 4
SQL For Data Analytics

SQL for Data Analysis is a very useful programming language that helps data analysts to interact with data stored in databases. SQL is the most used data analysis tool for data analysts and data scientists because the majority of the world's data is stored in databases.

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Course Content

    • Things you will learn:
    • Introduction
      • Overview of Oracle Database 11g and related products
      • Overview of relational database management concepts and terminologies
      • Introduction to SQL and its development environments
      • The HR schema and the tables used in this course
      • Oracle Database documentation and additional resources
    • Retrieve Data using the SQL SELECT Statement
      • List the capabilities of SQL SELECT statements
      • Generate a report of data from the output of a basic SELECT statement
      • Use arithmetic expressions and NULL values in the SELECT statement
      • Invoke Column aliases
      • Concatenation operator, literal character strings, alternative quote operator, and the DISTINCT keyword
      • Display the table structure using the DESCRIBE command
    • Usage of Single-Row Functions to Customize Output
      • List the differences between single row and multiple row functions
      • Manipulate strings using character functions
      • Manipulate numbers with the ROUND, TRUNC, and MOD functions
      • Perform arithmetic with date data
      • Manipulate dates with the DATE functions
    • Conversion Functions and Conditional Expressions
      • Describe implicit and explicit data type conversion
      • Describe the TO_CHAR, TO_NUMBER, and TO_DATE conversion functions
      • Nesting multiple functions
      • Apply the NVL, NULLIF, and COALESCE functions to data
      • Usage of conditional IF THEN ELSE logic in a SELECT statement
    • Aggregated Data Using the Group Functions
      • Usage of the aggregation functions in SELECT statements to produce meaningful reports
      • Describe the AVG, SUM, MIN, and MAX function
      • How to handle Null Values in a group function
      • Divide the data in groups by using the GROUP BY clause
      • Exclude groups of date by using the HAVING clause
    • Display Data from Multiple Tables
      • Write SELECT statements to access data from more than one table
      • Join Tables Using SQL:1999 Syntax
      • View data that does not meet a join condition by using outer joins
      • Join a table to itself by using a self join
      • Create Cross Joins
    • Usage of Sub-queries to Solve Queries
      • Use a Sub-query to Solve a Problem
      • Single-Row Sub-queries
      • Group Functions in a Sub-query
      • Multiple-Row Sub-queries
      • Use the ANY and ALL Operator in Multiple-Row Sub-queries
      • Use the EXISTS Operator
    • SET Operators
      • Describe the SET operators
      • Use a SET operator to combine multiple queries into a single query
      • Describe the UNION, UNION ALL, INTERSECT, and MINUS Operators
      • Use the ORDER BY Clause in Set Operations
    • Data Manipulation
      • Add New Rows to a Table
      • Change the Data in a Table
      • Use the DELETE and TRUNCATE Statements
      • How to save and discard changes with the COMMIT and ROLLBACK statements
      • Implement Read Consistency
      • Describe the FOR UPDATE Clause
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.

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Course Content

    • Things you will learn:
    • Ms Excel Basic
      • 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 with Tableau

Tableau is an end-to-end data analytics platform that allows you to prepare and analyze your data insights. Tableau helps people see and understand data.

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Course Content

    • Things you will learn:
    • Introduction to Tableau2018
      • What is Tableau
      • Features of Tableau
      • Top Chart Types in Tableau
      • Introduction to the various File Types
      • 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
    • Tableau Architecture & User Interface
      • Architecture of Tableau
      • Installation of Tableau Desktop
      • The interface of Tableau (Layout, Toolbars, Data Pane, Analytics Pane etc.)
      • How to Start with Tableau
    • Data Preparation
      • Connecting to Different Data Sources
      • Excel
      • CSV
      • Microsoft Access
      • SQL server
      • Google Sheets
      • Live vs. Extract Connection
      • Creating Extract
      • Refreshing Extract
      • Incremental Extract
      • Refreshing Live
      • Data Source Editor
      • Managing Metadata and Extracts
      • Pivoting & Splitting
      • Data Interpreter : Clean dirty data
      • TWB vs. TWBX
    • Data Visualization Principles
      • What is Data Visualization
      • Why Visualization came into the picture
      • Importance of Visualizing Data
      • Poor Visualizations versus Perfect Visualizations
      • Principles of Visualizations
      • Tufte’s Graphical Integrity Rule
      • Tufte’s Principles for Analytical Design
      • Visual Rhetoric
      • Goal of Data Visualization
      • Data Interpretation
      • Pivot Tables
      • Split Tables
      • Responsive Tool Tips
      • Radial & Lasso Selection
      • Right Click Filtering
      • Creating Calculated Fields
      • Logical functions
      • Case-if functions
      • ZN function
      • Else-if function
      • Ad-Hoc Calculations
      • Manipulating Text-Left and Right Functions
    • Basic Data Visualization
      • Pivot Table & Heat Map
      • Highlight Table
      • Bar Charts
      • Line Charts
      • Pie Chart
      • Scatter Plot
      • Word Cloud
      • Tree Map
      • Blended Axis
      • Dual Axis
    • Managing Your Data
      • Filters
      • Types of Filters
      • Dimension Filters
      • Measure Filters
      • Condition based Filters
      • Advanced filters using wildcards
      • Top & Bottom N Filtering
      • Filtering order of operations
      • Extract Filter
      • Data Source Filter
      • Context Filter
      • Other Filters etc
      • Sorting
      • Calculations - String, Basic, Date & Logic
      • Parameters
      • Working with Dates
      • Table Calculation
      • Discrete vs Continuous measures
      • 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
    • Advanced Data Preparation
      • Join
      • Inner
      • Left
      • Right
      • Full
      • Complex Joins
      • Union
      • Data Blending & when it is required
    • Advance Data Visualization
      • Bar Chart
      • Stack Bar Chart
      • Bar in Bar Chart
      • Combo Chart
      • Line Chart
      • Single Axis
      • Blended Axis
      • Dual Axis
      • Dual Axis Chart
      • Line
      • Bar
      • Lollipop Chart
      • Donut
      • Pareto Chart
      • Motion Charts
      • Other Advanced Charts
    • Advanced Filtering & Actions
      • Action Filters
      • Action Jumps
    • Sharing Your Dashboards
      • Publishing to PDF
      • Exporting to Pivot Tables and Images
      • Exporting Packaged Workbooks
      • Publishing to Tableau Server
Course 7
Data Analytics with Power BI

Power BI is a cloud-based business analytics tool that provides rapid insight and is used to analyze, extract and visualise data.

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Course Content

    • Things you will learn:
    • 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
      • 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
      • ROW Context and Filter Context in DAX
      • Operators in DAX - Real-time Usage
      • Quick Measures in DAX - Auto validations
      • Power Pivot x Velocity & Vertipaq Store
      • In-Memory Processing: DAX Performance
    • Modelling with Power BI
      • Introduction to Modelling
      • Optimize Data Models
      • Setup and Manage Relationships
      • Cardinality and Cross Filtering
      • Default Summarization & Sort by
      • Creating Calculated Columns
      • Creating Measures & Quick Measures
    • 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 Q&A and Data Insights
      • Why Dashboard and Dashboard vs Reports
      • Creating Dashboards
      • Configuring a Dashboard: Dashboard Tiles, Pinning Tiles
      • Quick Insights in Power BI
      • Power BI embedded and REST API
    • Direct Connectivity
      • Custom Data Gateways
      • Exploring live connections to data with Power BI
      • Connecting directly to SQL Azure, HD Spark, and SQL Server Analysis Services/ My SQL
      • Introduction to Power BI Development API
      • 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 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
    • Refreshing Datasets
      • Understanding Data Refresh
      • Personal Gateway (Power BI Pro and 64-bit Windows)
      • Replacing a Dataset and Troubleshooting Refreshing
Course 8
Data Analytics with R Proramming

Data Analytics with R Proramming is an open-source language used for statistical computing or graphics. Data Analytics with R Proramming is commonly used in statistical analysis and data mining.

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Course Content

    • Things you will learn:
    • Overview
      • History of R
      • Advantages and disadvantages
      • Downloading and installing
      • How to find documentation
    • R Programming Basics
      • Using the R console and R Studio
      • Getting help
      • Learning about the environment
      • Writing and executing scripts
      • Object oriented programming
      • Introduction to vectorised calculations
      • Introduction to data frames
      • Installing and loading packages
      • Working directory
      • Saving your work
    • Variable types and data structures in base R
      • Variables and assignment
      • Data types
      • Numeric, character, Boolean, and factors
      • Data structures
      • Vectors, matrices, arrays, data frames, lists
      • Indexing, sub-setting
      • Assigning new values
      • Viewing data and summaries
      • Naming conventions
      • Objects
    • Getting data into the R environment
      • Built-in data
      • Reading data from structured text files
      • Reading data using ODBC
    • Data frame manipulation
      • Introduction to tables, enhanced data frames
      • Renaming columns
      • Adding new columns
      • Binning data (continuous to categorical)
      • Combining categorical values
      • Transforming variables
      • Handling missing data
      • Merging datasets together
      • Stacking datasets together (concatenation)
    • Handling dates in R
      • Date and date-time classes in R
      • Formatting dates for modeling
    • Exploratory Data Analysis (Descriptive Statistics)
      • Continuous data
      • Distributions
      • Quantiles, mean
      • Bi-modal distributions
      • Histograms, box-plots
      • Categorical data
      • Tables
      • Bar plots
      • Group by calculations
      • Split-apply-combine
      • Reshaping and pivoting data in R (long to wide with aggregation)
      • Melt and cast
    • Working with text data
      • Finding and matching patterns in text
      • Stringer package for text manipulation
      • Introduction to regular expressions in R
      • Categorical data wrangling with forcats
    • Control flow & functions
      • Truth testing
      • Branching
      • Looping
      • Functions
      • Parameters
      • Return values
      • Variable scope
      • Exception handling
      • Applying functions across dimensions
      • Sapply, lapply, apply
      • Programming with map and purr
    • Graphics in R Overview
      • Base graphics system in R
      • Scatterplots, histograms, bar charts, box and whiskers, dot plots
      • Labels, legends, titles, axes
      • Exporting graphics to different formats
    • Advanced R graphics
      • Understanding the grammar of graphics
      • Quick plots (qplot function)
      • Building graphics by pieces (ggplot function)
      • Understanding geoms (geometries)
      • Linking chart elements to variable values
      • Controlling legends and axes
      • Exporting graphics
    • Inferential Statistics
      • Bivariate correlation
      • T-test and non-parametric equivalents
      • Chi-squared test
    • General Linear Regression Models in R
      • Understanding formulas
      • Linear and logistic regression models
      • Regression plots
      • Confounding / interaction in regression
      • Evaluating residuals
      • Scoring new data from models (prediction)
      • Useful plots from regression models
CertificatesMaster's Program Certificate

You will get certificate after completion of program

Course Structure
  • - 8 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_Time_Case

Real-life Case Studies

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

Practitioners

Best Industry-Practitioners

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

Industrial

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.

Knowledge

Hands-on Practical Knowledge

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

Collaborative

Collaborative Learning

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

Assigment_Quiz

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.

placement

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.

RECRUITMENT PARTNERS

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.

Accenture
Birlasoft
Ebix
Equafix
EXL
Infosys
Qualix
Tech Mahindra
Wipro
Crediwatch

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
₹ 49,300 (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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