Learn how to extract data for analysis. Join now and learn under an expert data analyst.

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32 Hrs.

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2 Project

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  • Data Analytics helps out the organization for analyzing data from a different perspective which consists out real-time, historical, unstructured, etc. It helps out in connecting the automated decisions for increasing our business intelligence throughout the organization. But today there is the best solution support given to the organizations for increasing their end-to-end encryption. Resultantly, the demand for Data Analytics Training in Gurgaon spending up.
  • However, it helps out organizations digitally transform their activities for emerging as more productive. Additionally, it assists in optimizing operations related to daily business activities. Increasing collaboration between different businesses also makes out things easier to adapt. Data scientists can also work with the customers to help them in handling their real-time problems. But it is important to gather information regarding the Data Analytics Training Institute in Gurgaon.

Data Analytics Training in Gurgaon


  • The course offers an overview of Big Data and helps out the candidates in assisting with different complicated procedures. Moreover, emphasizes a variety of Analytics things needed to make out organization efficient. Additionally, Data Analyst Training in Gurgaon increasing its demand;
    • Explaining the various realms of Data Analytics.

      Differentiating between various data roles.

      Understanding the different components of Data analytics.

      Handling out different types of data structures.

      Getting well-adapted to various roles in the industry.

  • After completing the Data Analyst Course in Gurgaon the salary gets out in the range of Rs 1.8 lakh to Rs 11.9 lakh. Moreover, as per the reports of the ambition box, the average salary gets struck out at the rate of 15 lakh per annum.

  • Getting out of a job in Data Analytics is the first step in growing out of the career in the domain of Data Analytics. In case you have no previous experience in this domain then you can go through the below-mentioned course prospects after Data Analytics Training in Gurgaon;
    • Data Analytics gets highly leveraged by startups as well as large organizations. It assists in improving customer experience, targeted marketing & optimizing resources.

      If we talk about DataOps which defines the streamlining of operations. Thus, managing a large amount of data becomes important for getting out actionable insights.

      Due to the high demand for data, it is becoming the term for new service. It offers widespread solutions for purposes of data integration, analyzing data, etc.

      Overall the data analysts have multiple sets of skills as they are good at working with the numbers & reports. Moreover, they use strong presentation skills which can easily appease the customers.

      Technology companies change out rapidly with the organizational procedures taking place. Departments are constantly adapting to the new changes which are taking place inside the industrial verticals.

  • Undoubtedly, everyone heard about Data Analytics in modern businesses because they are responsible for handling organizational functions. Simply, data analytics is critical to handling organizational projects. As the Data Analyst training in Gurgaon gets completed you have to overcome the reasons behind its popularity:
    • The latest research performed by indeed.com and others proves out there is an increasing trend in the domain of Data Analytics which proves to increase the number of job opportunities.

      The current for Data Science is seeking out increase due to the advent of new technologies in ongoing businesses. Moreover, businesses are adopting cloud infrastructure & workloads.

      Moreover, automation of the data analysts gets considerable useful for businesses when organization deals with big data. Organizations are adopting new techniques.

      Data Analytics contributes out through multiple processes of businesses with the help of data exploration, data preparation, and data automation as well among others.

      Data analytics takes out the operational control of businesses moreover with the help of continuous involvement of data in business processes it is getting acceleration.

  • Data analysts are the professionals who get responsible for the senior leadership in the organization who make out strategic decisions. After completing the course from the Data Analytics training institute in Gurgaon you have to follow the below-mentioned primary duties:
    • Using the statistical methods for analyzing data & generating business reports.

      Working with the management team to create a prioritized list of the business segment.

      Identifying & recommending new ways for saving money as well as streamlining business operations.

      Using out the data trends in the customer base as well as consumer population for the whole.

      Working with the departmental managers for outlining the specific data according to the business method.

  • After completing the Data Analyst training in Gurgaon multiple organizations will get to hire out from the pool of candidates. If we talk regarding the organizations then it is Infogain Solutions Pvt Ltd, PubMatic, Serving Skill, Reliance Retail, Huquo Consulting Pvt Ltd, Wipro, CBRE, etc.

  • While completing the Data Analytics Course in Gurgaon you get out personalized training with specialized faculty members. Moreover, you also get out 100% globally recognized training certificate which will boost the chances of getting a job in a competitive market.

Why should you learn Data Analytics?

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TrackWeek DaysWeekendsFast Track
Course Duration 40-45 Days 7 Weekends 8 Days
Hours 1 Hrs. Per Day 2 Hrs. Per Day 6+ Hrs. Per Day
Training ModeClassroom/OnlineClassroom/OnlineClassroom/Online
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Data Analytics Certification Training

  • With our AZ-900 “Microsoft Azure fundamentals” certification Training you will learn foundational knowledge of cloud services and how those services are provided with Microsoft Azure. The exam is intended for candidates who are just beginning to work with cloud-based solutions and services or are new to Azure.
  • Things you will learn:
    • Python Statistics for Data Science

      Data Analytics Overview

      Statistics Essentials For Analytics

      SQL For Data Analytics

      Analytics with Excel

      Analytics with Tableau

      Data Analytics with Power BI

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  • 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.






  • 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


      • 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


      • 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)


      • 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


      • 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
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  • Data Analytics Overview
    • 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

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  • 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

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  • 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

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  • 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


      • 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

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  • 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



      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


      Calculations - String, Basic, Date & Logic


      Working with Dates

      Table Calculation

      Discrete vs Continuous measures

      Grouping Data





      Combined Fields

  • Formatting
    • Size

      Updating Axis




      Chart Lines

      Trend Line


      Reference Line

      Mark Labels


  • 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





      Complex Joins


      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



      Lollipop Chart


      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

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  • 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





      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

  • BI and Azure ML Integrating Power
    • Extracting data out of Azure SQL using R

      Using R, call the Azure ML web service and send it the un-scored data

      Writing the output of the Azure ML model back into SQL

      read scored data into Power BI using R

      Publishing the Power BI file to the Power BI service

      Scheduling a refresh of the data using the Personal Gateway

  • 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

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Mock Interviews

Prepare & Practice for real-life job interviews by joining the Mock Interviews drive at Croma Campus and learn to perform with confidence with our expert team.Not sure of Interview environments? Don’t worry, our team will familiarize you and help you in giving your best shot even under heavy pressures. Our Mock Interviews are conducted by trailblazing industry-experts having years of experience and they will surely help you to improve your chances of getting hired in real.
How Croma Campus Mock Interview Works?


Validate your skills and knowledge by working on industry-based projects that includes significant real-time use cases.Gain hands-on expertize in Top IT skills and become industry-ready after completing our project works and assessments.Our projects are perfectly aligned with the modules given in the curriculum and they are picked up based on latest industry standards. Add some meaningful project works in your resume, get noticed by top industries and start earning huge salary lumps right away.
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  • Descriptive.
  • Diagnostic.
  • Predictive.
  • Prescriptive.
  • Cognitive.

  • An ISO Certified.
  • End-to-end session.
  • Personalized sessions.

There are multiple tools get out for completing the processes of Data Analytics.

  • Surveys.
  • Transactional tracking.
  • Interview or Focus Groups.
  • Observation.
  • Online Tracking.

In an SQL server, there are various rows, columns, expressions as well as a parameters.

Career Assistancecareer assistance
  • - Build an Impressive Resume
  • - Get Tips from Trainer to Clear Interviews
  • - Attend Mock-Up Interviews with Experts
  • - Get Interviews & Get Hired
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Adding the scope of improvement and fostering the analytical abilities and skills through the perfect piece of academic work.

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