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Data Analytics Course

Data Analytics Course in Gurgaon – With Placement Focused Training Course

Our Data Analytics Course in Gurgaon is designed for students, fresher, and working professionals who want to understand how the data works in real-world jobs. The course begins with basic concepts and then progresses towards advanced tools and techniques.

Duration: 20 - 22 Week | Mode: Live + Recorded Sessions

Watch Data Analytics Demo Class

Attend our Data Analytics Training in Gurgaon demo session to understand how live classes work before you join the course and enroll

Our Recently Placed Students

Gita Singh

Placed at Accenture

Suman Sharma

Placed at TCS

Manoj Prasad

Placed at Deloitte

Vinod Sharma

Placed at Wipro

Pawan Kumar

Placed at IBM

Gopal Singh

Placed at Infosys

Ankit Mishra

Placed at Microsoft

Rohit Shah

Placed at Capgemini

About the Data Analytics Training

The Data Analytics Training in Gurgaon is structured in such a manner that it will provide you with the necessary tools as well as the logic necessary for any data-related task. The training will cover data collection, processing, analysis, and subsequent use in generating valuable reports. You will be able to understand the use of data in decision-making in companies.

Training Highlights
  • Live classes by professional trainers
  • Practice on actual data sets
  • Industry-Specific Data Analytics Training in Gurgaon
  • Working with real examples – Case Studies
  • Enhanced and job-related course material
  • Resume creation and interview preparation
  • Dedicated placement support

What You Get

  • Live instructor-led sessions with recordings
  • Practice datasets for learning
  • Real-time project experience
  • Resume and interview guidance

Course Design & Approved By

Nasscom & Wipro

What Will You Learn in Data Analytics Training

Our Data Analytics Training in Gurgaon teaches each tool step by step in a very simple manner so that even beginners can follow easily.

Core Modules Covered

  • Basics of Data Analytics
  • Excel for Data Analysis
  • SQL for working with databases
  • Data cleaning and data preparation
  • Python for data analysis
  • Statistics needed for data analysis

Advanced Topics & Projects

  • Data visualization using Power BI
  • Exploratory Data Analysis (EDA)
  • Business dashboards and reports
  • Live Data Analytics projects
  • Case studies based on real companies

Download Curriculum

Get a peek through the entire curriculum designed that ensures Placement Guidance

Course Design By

nasco wp

Course Offered By

Why Choose Our Data Analytics Training Material & Resources?

  • Training videos
  • Simple notes for every topic
  • Real business case studies
  • Practice assignments
  • Interview and certification question

Benefits of Joining Our Data Analytics Course

  • Work on real datasets
  • Lifetime access to LMS
  • Recorded videos for revision
  • Live doubt-clearing sessions
  • Certification support
  • 100% placement assistance
Learners Reviews

“Joining this course was the best decision for my career change as they not only completed the course but helped me with the interviews also.”

— Aastha, MIS Analyst

“Recorded videos helped me revise topics anytime. The support team was very supportive.”

— Ravinder, Data Analytics Consultant

“Projects were based on real business data, which gave me confidence to work in real jobs.”

— Amit, Data Visualization Analyst

“Live online classes were very helpful. All doubts were cleared properly.”

— Pooja, Reporting Analyst

“I had no technical background earlier, but the training made it easy to understand data analysis. Practice tasks helped a lot.”

— Kavita Joshi, Junior Data Scientist

“This Data Analytics Course helped me understand data concepts from the beginning. The trainer explained everything clearly and slowly.”

— Ankit, SQL & Data Visualization
Data Analytics - Country-Wise Job Profiles & Salary

Top Job Profiles:

  • Data Analyst
  • Data Scientist
  • Senior Data Scientist
  • ML Engineer

Average Salary Range:

  • INR 4 LPA - INR 8 LPA (Entry Level)
  • INR 8 LPA - INR 18 LPA (Mid Level)
  • INR 18 LPA - INR 35+ LPA (Senior)

Top Job Profiles:

  • Data Analyst
  • Data Scientist
  • Senior Data Scientist
  • ML Engineer:

Average Salary Range:

  • $70,000 - $95,000 (Entry Level)
  • $110,000 - $150,000 (Mid Level)
  • $120,000 - $170,000+ (Senior)

Top Job Profiles:

  • Data Analyst
  • Data Scientist
  • Senior Data Scientist
  • ML Engineer

Average Salary Range:

  • CAD 60,000 - CAD 85,000 (Entry Level)
  • CAD 90,000 - CAD 130,000 (Mid Level)
  • CAD 100,000 - CAD 160,000+ (Senior)

Top Job Profiles:

  • Data Analyst
  • Data Scientist
  • Senior Data Scientist
  • ML Engineer

Average Salary Range:

  • £35,000 - £50,000 (Entry Level)
  • £55,000 - £80,000 (Mid Level)
  • £80,000 - £120,000+ (Senior)

Enroll Today

Join our Data Science Course in Noida with Placement support and start your career with live projects, expert trainers, and full interview help.

About the Trainer

The best thing about our courses is that you will be trained under experts having years of experience. They are industry trainers with years of practical experience in Data Science, Machine Learning, Artificial

  • Industry-certified Data Analytics professional
  • 8+ years of hands-on experience in analytics projects
  • Expertise in Excel, SQL, Power BI, Python, and Tableau
  • Delivered training to freshers and working professionals
  • Real-time project–based teaching approach
Frequently Asked Questions

  • 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.

CURRICULUM & PROJECTS

Data Analytics Training Program

    NA

    • Introduction
      • What is Data Analytic
      • Common Terms in Data Analytics
      • 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 Analytics tools & their popularity.
      • Data Analytics Methodology & problem-solving framework.
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    NA

    • 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
      • Subtotal, Multi-Level Subtotal
      • Grouping Features
      • Consolidation With Several Worksheets
      • Filter
      • Printing of Raw & Column Heading on Each Page
      • Workbook Protection and Worksheet Protection
      • Specified Range Protection in Worksheet
      • Excel Data Analysis
      • Data Table
      • Pivot Table
      • Generating MIS Report In Excel
      • Text Functions
      • Lookup & Reference Function
      • Logical Functions & Date and Time Functions
      • Database Functions
      • Statistical Functions
      • Financial Functions
      • Functions for Calculation Depreciation
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    NA

    • 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
      • SCHEMA BINDING and ENCRYPTION
      • 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
      • SCHEMABINDING and ENCRYPTION
      • INPUT and OUTPUT parameters
      • System level Stored Procedures
      • Dynamic SQL and parameterization
    • System functions and Usage
      • Scalar Valued Functions
      • Types of Table Valued Functions
      • SCHEMABINDING and ENCRYPTION
      • 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
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    NA

    • 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
      • 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
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    NA

    • 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
    • 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
    • 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
    • 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 folders details using OS
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    NA

    • Data Analysis and Visualization using Pandas.
      • Statistics
      • Pandas
    • Data Analysis and Visualization using NumPy and MatPlotLib
      • NumPy
      • MatPlotLib
    • Introduction to Data Visualization with Seaborn
      • Introduction to Seaborn
      • Visualizing Two Quantitative Variables
      • Visualizing a Categorical and a Quantitative Variable
      • Customizing Seaborn Plots
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    NA

    • Capstone Project
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+ More Lessons

Course Design By

naswipro

Nasscom & Wipro

Course Offered By

croma-orange

Croma Campus

Our Students' Projects
1767961932.webp
Zomato – Restaurant Rating Analysis

Scenario: Understanding rating trends.

Live Work:
  • Data cleaning
  • Visualization
  • Business insights

Outcome: Better restaurant suggestions

1767961514.webp
Tech Mahindra – HR Data Analysis Specialist

Scenario: Studying employee attrition

Live Work:
  • Excel and SQL analysis
  • Power BI reports

Outcome: Improved workforce planning

1767961263.webp
Flipkart – Customer Behavior Analysis

Scenario: Understanding customer buying habits

Live Work:
  • Data preparation
  • EDA using Python
  • Visualization dashboards

Outcome: Improved customer targeting

1767961081.webp
Amazon – Sales Performance Analyst

Scenario: Studying monthly sales performance across regions

Live Work:
  • Data cleaning using Excel and Python
  • SQL queries for analysis
  • Power BI dashboards

Outcome: Better sales tracking and planning

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