Data Analyst Course In Coimbatore

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The Data Analyst course in Coimbatore is designed for individuals who wish to build a strong foundation in data analysis and decision-making skills. This course covers essential topics like data cleaning, data visualization, statistical analysis, and predictive modeling using tools like Excel, SQL, Python, and Tableau. Through hands-on projects, students will learn how to extract, manipulate, and analyze data to derive actionable insights. The course also introduces key concepts in business intelligence, machine learning, and data storytelling. Upon completion, you’ll be equipped to handle real-world data analysis tasks and pursue roles in various industries such as finance, healthcare, and marketing.

Data Analyst Course In Coimbatore – Why Vnet academy?

VNET Academy’s Data Analyst course in Coimbatore offers a comprehensive Data Analyst course tailored to meet the growing demand for skilled professionals in the field. The program spans a range of essential topics, including data analysis techniques, statistical methods, and tools such as Python and R programming. VNET Academy’s industry-relevant curriculum is designed and delivered by experienced instructors, ensuring a practical and immersive learning experience. Enrolling in the Data Analytics courses in coimbatore at VNET Academy provides a unique opportunity for individuals in Coimbatore to enhance their analytical skills, opening doors to diverse career opportunities in data-driven industries. Whether you are a recent graduate or a professional seeking to upskill, this program promises to empower you with the knowledge and expertise required to excel in the dynamic field of data analysis.

Data Analyst Overview

VNET Academy’s Data Analyst training in Coimbatore is a comprehensive program designed to cultivate proficient data professionals. Geared towards bridging the gap between theoretical knowledge and practical application, this course equips participants with the skills demanded in today’s data-driven landscape. The curriculum encompasses core concepts such as data manipulation, statistical analysis, and data visualization, using industry-standard tools like Python, R, and advanced Excel. The training is enriched with hands-on projects and case studies, ensuring that learners gain a deep understanding of real-world applications. VNET Academy’s seasoned instructors bring a wealth of industry experience, providing invaluable insights and guidance throughout the program. Participants will not only acquire technical proficiency but also develop a strategic mindset for leveraging data to drive business decisions. The course is ideal for aspiring data analysts, recent graduates, and professionals seeking to transition into analytics roles. By the program’s conclusion, graduates emerge well-prepared for the challenges of the dynamic data analytics field, making VNET Academy a prime choice for those seeking a comprehensive and practical Data Analyst course in Coimbatore.

Our Training Methology

Methology 1

Engage in dynamic classroom sessions with expert instructors.

Methology 2

Apply theoretical knowledge through practical, real-world projects.

Methology 3

Analyze industry-specific case studies for a holistic understanding.

Methology 4

Learn using up-to-date content aligned with market demands.

Methology 5

Evaluate progress through regular quizzes and assessments.

Methology 6

Benefit from insights shared by industry professionals and guest speakers.

Methology 7

Receive guidance on career paths and industry trends for informed decision-making.

What are the Career Benefits of Learning Data Analyst Certification Course at Vnet Academy

Benefit 1

Acquire highly sought-after skills in data analysis, making you a valuable asset in the job market.

Benefit 2

Boost your career prospects by gaining a recognized certification from VNET Academy, enhancing your professional credibility.

Benefit 3

Gain hands-on experience through real-world projects, ensuring practical application of theoretical concepts.

Benefit 4

Stay updated with the latest industry trends and technologies, ensuring your skills are aligned with market needs.

Benefit 5

Connect with industry professionals and peers, expanding your professional network for future career opportunities.

Benefit 6

Increase your earning potential with a Data Analyst certification, as employers often recognize and reward specialized skills in the data domain.

Benefit 7

Benefit from VNET Academy's job placement support, increasing your chances of securing a role in the data analytics field.

Who can join this course? Immerse Yourself in World-Class Learning at Vnet Academy

This course is suitable for anyone interested in data analysis, whether you are a beginner or have some experience with data-related tasks. Graduates in fields like Computer Science, Mathematics, Business, or Economics will find this course valuable, but it is also open to professionals seeking to transition into data analytics. No prior knowledge of coding is required, as the course starts from the basics. However, individuals with a keen interest in working with data, analyzing trends, and helping businesses make informed decisions will benefit greatly from this course.

An inexperienced fresher in Coimbatore can seamlessly transition into a proficient Data Analyst through the specialized course at VNET Academy. The program, designed for beginners, covers fundamental concepts, statistical techniques, and practical tools like Python and R. With a focus on hands-on learning, participants work on real-world projects, honing their analytical skills. VNET Academy’s seasoned instructors provide mentorship, guiding inexperienced individuals in developing a strong foundation in data analysis. The curriculum not only imparts technical expertise but also emphasizes the application of data-driven insights in business contexts. Graduates emerge equipped to navigate the demands of the industry, making VNET Academy’s Data Analyst course an ideal launchpad for inexperienced individuals aspiring to embark on a successful career in data analytics.

Inexperienced Fresher Becomes an Data Analyst Professionals

About Classroom Training

Journey into the realm of data analytics with VNET Academy’s Data Analyst Classroom Training, the premier institute in Coimbatore for cultivating analytical expertise. Our comprehensive program, tailored for aspiring data analysts, unfolds within the dynamic setting of our classrooms, blending theoretical knowledge with hands-on application. Led by seasoned industry professionals, our classroom sessions cover essential topics such as data manipulation, statistical analysis, and data visualization using cutting-edge tools like Python and R. What sets VNET Academy apart is our commitment to providing a conducive learning environment, fostering collaboration and skill development. With a focus on real-world projects, participants gain practical insights, preparing them for the challenges of the data analytics landscape. Join VNET Academy’s Data Analyst Classroom Training to not only acquire technical proficiency but also to build a robust foundation for a successful career in data analytics. Elevate your skills and prospects at the forefront of data analysis with our esteemed institute in Coimbatore.

Data Analyst Syllabus

Ms Excel

Module: 1 Excel Introduction

An Overview Of The Screen, Navigation And Basic
Spreadsheet Concepts
Various Selection Techniques
Shortcut Keys Understanding Rows And Columns,
Naming Cells
Working With Excel Workbook And Sheets

Module: 2 Customizing Excel

Customizing The Ribbon
Using And Customizing Autocorrect
Changing Excel’s Default Options

Module: 3 Using Basic Functions

Sum, Average, Max, Min ,count, Counta, Absolute

Module: 4 Formatting And Proofing

Currency Format
Format Painter
Formatting Dates
Custom And Special Formats
Formatting Cells With Number Formats,
Font Formats, Alignment, Borders, Etc
Basic Conditional Formatting

Module: 5 Mathematical Functions

Sumif, Sumifs, Countif, Countifs , Averageif,
Averageifs

Module: 6 Protecting Excel

File Level Protection
Workbook, Worksheet Protection

Module: 7 Text Functions

Upper, Lower, Proper
Left, Mid, Right • Trim, Len, Exact
Concatenate
Find, Substitute

Module: 8 Date And Time Functions

Today, Now
Day, Month, Year
Date, Date If, Dateadd
Eomonth, Weekday

Module: 9 Advanced Paste Special Techniques

Paste Formulas, Paste Formats
Paste Validations
Transpose Tables

Module: 10 Sorting And Filtering

Paste Formulas, Paste Formats
Paste Validations
Transpose Tables

Module: 11 Printing Workbooks

Setting Up Print Area
Customizing Headers & Footers
Designing The Structure Of A Template
Print Titles –repeat Rows / Column

Advance Excel

Module: 1 Advance Excel What If Analysis

Goal Seek
Scenario Analysis
Data Tables (pmt Function)
Solver Too

Module: 2 Logical Functions

If Function
Howto Fix Errors – If Error
Nested If

Module: 3 Data Validation

Number, Date & Time Validation
Text And List Validation
Customvalidations Based On Formula For A Cell
Dynamic Dropdown List Creation Using Data
Validation – Dependency List
Complex If And Or Functions

Module: 4 Lookup Functions

Vlookup / Hlookup
Index Andmatch
Creating Smooth User Interface Using Lookup
Nested Vlookup
Reverse Lookup Using Choose Function
Worksheet Linking Using Indirect
Vlookupwith Helper Column

Module: 5 Pivot Tables

Creating Simple Pivot Tables
Basic And Advanced Value Field Setting
Classic Pivot Table
Choosing Field
Filtering Pivottables
Modifying Pivottable Data
Grouping Based On Numbers And Dates
Calculated Field & Calculated Items
Arrays Functions
What Are The Array Formulas, Use Of The Array
Formulas?
Basic Examples Of Arrays (using Ctrl+shift+enter)
Arraywith If, Len Andmid Functions Formulas.
Arraywith Lookup Functions.
Advanced Use Of Formulaswith Array. Charts And
Slicers
Various Charts I.e. Bar Charts / Pie Charts / Line
Charts
Using Slicers, Filter Datawith Slicers
Manage Primary And Secondary Axis

Module: 6 Charts And Slicers

Various Charts I.e. Bar Charts / Pie Charts / Line
Charts
Using Slicers, Filter Datawith Slicers
Manage Primary And Secondary Axis

Module: 8 Vba Macro

8.1 Introduction To Vba

What Is Vba?
What Can You Dowith Vba?
Recording A Macro
Procedure And Functions In Vba

8.1 Introduction To Vba

What Is Vba?
What Can You Dowith Vba?
Recording A Macro
Procedure And Functions In Vba

8.2 Variables In Vba

What Is Variables?
Using Non-declared Variables
Variable Data Types
Using Const Variables

8.3 Message Box And Input Box Functions

Customizingmsgboxes And Inputbox
Reading Cell Values Intomessages
Various Button Groups In Vba

8.4 If And Select Statements

Simple If Statements
The Elseif Statements
Defining Select Case Statements

8.5 Looping In Vba

Introduction To Loops And Its Types
The Basic Do And For Loop
Exiting Froma Loop
Advanced Loop Examples

8.6 Mail Functions – Vba

Using Outlook Namespace
Send Automatedmail
Outlook Configurations,mapi
Worksheet /workbook Operations
Mergeworksheets Usingmacro
Mergemultiple Excel Files Into One Sheet
Splitworksheets Using Vba Filters
Worksheet Copier

SQL

Module 1: Introduction To Sql

Overview Of Databases And Sql

What Is A Database?

Importance Of Sql In Data Management

Basic Sql Syntax And Queries

Structure Of Sql Queries (select, Insert, Update,
Delete).
Understanding Sql Statements And Their
Components.

Data Types And Null Values

Different Data Types In Sql (e.g., Integer, Varchar,
Date).
Handling Null Values In Sql.

Module 2: Querying Data

Select Statement And Filtering (where Clause)

Retrieving Specific Data From Tables.

Filtering Data Using Conditions With Where
Clause.
Sorting Data (order By)

Sorting Query Results In Ascending Or Descending Order

Sorting By Multiple Columns.
Limiting And Paging Results (limit, Offset)
Limiting The Number Of Rows Returned.
Paging Through Large Result Sets.

Module 3: Aggregate Functions And Grouping

Using Aggregate Functions (count, Sum, Avg, Etc.)
Calculating Aggregate Values Across Rows.
Applying Aggregate Functions To Subsets Of Data.
Grouping Data (group By)
Grouping Data Based On One Or More Columns.
Understanding The Group By Clause And Its Use
Cases.
Filtering Groups (having Clause)
Filtering Groups Based On Aggregate Conditions.
Difference Between Where And Having Clauses

Module 4: Joins And Subqueries

Understanding Joins (inner Join, Left Join, Etc.)
Joining Tables To Combine Related Data.
Types Of Joins And Their Purposes.
Using Subqueries (correlated Subqueries)
Writing Subqueries Within Select, Insert, Update,
Delete Statements.
Understanding Correlated Subqueries And Their
Performance Implications.
Set Operations (union, Intersect, Except)
Combining Results From Multiple Queries.

Module 5: Data Manipulation

Insert, Update, Delete Statements
Adding, Modifying, And Deleting Data In Tables.
Transactional Control And Data Integrity.

Module 6: Indexes And Performance
Optimization

Creating Indexes For Performance
Understanding Index Types (e.g., B-tree, Hash).
Analysing Query Performance And Index Usage.
Query Optimization Strategies
Optimizing Sql Queries For Speed And Efficiency.
Using Explain And Query Plans To Improve
Performance.

Module 7: Advanced Topics

Handling Json Data In Sql
Storing, Querying, And Manipulating Json Data.
Using Json Functions And Operators.
Full-text Search Capabilities
Implementing Full-text Search In Sql Databases.
Configuring And Optimizing Search Queries.
Using Regular Expressions
Pattern Matching And Text Manipulation.
Incorporating Regular Expressions In Sql Queries

Module 8: Stored Procedures, Functions,
And Triggers

Creating And Using Stored Procedures
Writing Reusable Procedural Code Blocks.
Passing Parameters And Handling Exceptions.
Implementing Functions
User-defined Functions For Custom Calculations.
Scalar And Table-valued Functions.
Using Triggers For Automation
Defining Triggers For Automatic Actions.
Trigger Types (before, After, Instead Of).

Module 9: Security And Permissions

Managing Security In Sql
Securing Databases, Tables, And Objects.
Role-based Access Control (rbac) And Permissions.
Granting And Revoking Permissions
Granting Privileges To Users And Roles.
Revoking Access And Managing Permissions.

Power Bi

Unit 1: Introduction To Python Programming

Overview Of Python
Features Of Python
Execution Modes: Interactive Mode And
Scriptmode
Writing And Executing “hello World” Program
Python Character Set
Python Tokens: Keyword, Identifier, Literal,
Operator, Punctuator
Variables And Data Types
Comments In Python

Unit 2: Data Types And Operators

Introduction To Data Types
Numbers: Integer, Floating Point,complex
Boolean Data Type
Sequences: String,list, Tuple
Mapping: Dictionary
Mutable And Immutable Data Types
Operators: Arithmetic, Relational, Logical,
Assignment, Augmentedassignment
Identity Operators: Is, Is Not
Membership Operators: In, Not In

Unit 3: Expressions, Statements, And
Input/output

Precedence Of Operators
Expression Evaluation
Type Conversion: Explicitand Implicit
Input/output Operations
Accepting Data As Input
Displaying Output
Errors: Syntax Errors,logical Errors,
Runtimeerrors

Unit 4: Flow Control

Introduction To Flow Control
Indentation In Python
Sequential Flow
Conditional Flow: If, If-else,if-elif-else
Iterative Flow: For Loop, While Loop
Flowcharts For Control Structures
Break And Continue Statements
Nested Loops

Unit 5: Strings

String Introduction
String Operations: Concatenation, Repetition,
Slicing
Traversing Strings
String Methods: Len(), Capitalize(), Title(), Lower(),
Upper(),count(), Find(), Index(),endswith(),
Startswith(), Isalnum(), Isalpha(), Isdigit(),
Islower(), Isupper(), Isspace(), Lstrip(),rstrip(),
Strip(), Replace(), Join(), Partition(), Split()

Unit 6: Lists

List Introduction
List Operations: Concatenation, Repetition, Slicing
Traversing Lists
List Methods: Len(), Append(), Extend(), Insert(),
Count(), Index(), Remove(), Pop(), Reverse(), Sort(),
Sorted(), Min(),max(), Sum()
Nested Lists

Unit 7: Tuples

Tuple Introduction
Tuple Operations: Concatenation, Repetition,
Slicing
Tuple Methods: Len(), Count(),index(), Sorted(),
Min(), Max(), Sum()
Tuple Assignment
Nested Tuples

Unit 8: Dictionaries

Dictionary Introduction
Dictionary Operations
Dictionary Methods:len(), Keys(), Values(), Items(),
Get(), Update(),del(), Clear(), Fromkeys(), Copy(),
Pop(), Popitem(), Setdefault(), Max(), Min(), Sorted()

Unit 9: Python Modules

Introduction To Modules
Importing Modules Usingimport And From
Statements
Examples With Math, Random, And Statistics
Modules

Unit 10: Functions:

Types Of Functions
Creating User-defined Functions
Arguments And Parameters
Default Parameters
Positional Parameters
Returning Values
Flow Of Execution
Variable Scope (global Scope,local Scope)

Unit 11: Exception Handling:

Introduction To Exception Handling
Try-except-finally Blocks

Unit 12: Files:

Introduction To Files
Types Of Files (text File, Binary File, Csv File)
Relative And Absolute Paths

Unit 13: Text Files:

Operations On Text Files
Opening And Closing Text Files
Text File Open Modes
Manipulation Of Data In Text Files

Unit 14: Binary Files:

Operations On Binary Files
Opening And Closing Binary Files
Basic Operations Using Picklemodul

Unit 15: Csv Files:

Operations On Csv Files
Importing Csv Module
Reading From And Writingto Csv Files

Unit 16: Data Structures:

Introduction To Stacks
Operations On Stacks
Implementation Of Stacksusing Lists

Unit 17: Numpy

Introduction To Numpy
Numpy Arrays
Array Operations And Manipulation
Linear Algebra With Numpy
Numpy Functions

Unit 18: Pandas

Introduction To Pandas
Series And Data Frames
Data Manipulation With Pandas
Data Aggregation And Grouping
Data Input And Output With Pandas

Unit 19: Matplotlib

Introduction To Matplotlib
Basic Plotting
Subplots And Layouts
Advanced Plotting Techniques
Plotting With Pandasand Numpy
Customizing Plot Aesthetics

Advance Python

Unit 1: Introduction To Python Programming

Overviewofpython
Featuresofpython
Executionmodes: Interactivemode
Andscriptmode
Writingandexecuting”helloworld”program
Pythoncharacterset
Pythontokens:keyword,identifier,literal,opera
Tor, Punctuator
Variablesanddatatypes

Unit2:data Types And Operators

Introductiontodatatypes
Numbers:integer,floatingpoint,complex
Booleandatatype
Sequences:string,list,tuple
Mapping:dictionary
Mutableandimmutabledatatypes
Operators: Arithmetic, Relational, Logical,
Assignment, Augmentedassignment
Identityoperators:is,isnot
Membershipoperators:in,notin

Unit 3: Expressions, Statements, And
Input/output

Precedence Of Operators
Expression Evaluation
Type Conversion: Explicit And Implicit
Input/output Operations
Accepting Data As Input
Displaying Output
Errors: Syntax Errors, Logical Errors, Runtime
Errors

Unit 4: Flow Control

Introduction To Flow Control
Indentation In Python
Sequential Flow
Conditional Flow: If, If-else, If-elif-else
Iterative Flow: For Loop, While Loop
Flowcharts For Control Structures
Break And Continue Statements
• Nested Loops

Unit 5: Strings

String Introduction
String Operations: Concatenation, Repetition,
Slicing
Traversing Strings
String Methods: Len(), Capitalize(), Title(),
Lower(), Upper(), Count(), Find(), Index(),
Endswith(), Startswith(), Isalnum(), Isalpha(),
Isdigit(), Islower(), Isupper(), Isspace(), Lstrip(),
Rstrip(), Strip(), Replace(), Join(), Partition(), Split()

Unit 6: Lists

Unit 7: Tuples

Tuple Introduction
Tuple Operations: Concatenation, Repetition,
Slicing
Tuple Methods: Len(), Count(), Index(), Sorted(),
Min(), Max(), Sum()
Tuple Assignment
Nested Tuples

Unit 8: Dictionaries

Dictionary Introduction
Dictionary Operations
Dictionary Methods: Len(), Keys(), Values(), Items(),
Get(), Update(), Del(), Clear(), Fromkeys(), Copy(),
Pop(), Popitem(), Setdefault(), Max(), Min(), Sorted()

Unit 9: Python Modules

Introduction To Modules
Importing Modules Using Import And From
Statements
Examples With Math, Random, And Statistics
Modules

Unit 10: Functions

Types Of Functions
Creating User-defined Functions
Arguments And Parameters
Default Parameters
Positional Parameters
Returning Values
Flow Of Execution
Variable Scope (global Scope, Local Scope)

Unit 11: Exception Handling

Introduction To Exception Handling
Try-except-finally Blocks

Unit 12: Files:

Introduction To Files
Types Of Files (text File, Binary File, Csv File)
Relative And Absolute Paths

Unit 13: Text Files

Operations On Text Files
Opening And Closing Text Files
Text File Open Modes
Manipulation Of Data In Text Files

Unit 14: Binary Files

Operations On Binary Files
Opening And Closing Binary Files
Basic Operations Using Pickle Module

Unit 15: Csv Files

Operations On Csv Files
Importing Csv Module
Reading From And Writing To Csv Files

Unit 16: Data Structures

Introduction To Stacks
Operations On Stacks
Implementation Of Stacks Using Lists

Unit 17: Numpy

Introduction To Numpy
Numpy Arrays
Array Operations And Manipulation
Linear Algebra With Numpy
Numpy Functions

Unit 18: Pandas

Introduction To Pandas
Series And Data Frames
Data Manipulation With Pandas
Data Aggregation And Grouping
Data Input And Output With Pandas

Unit 19: Matplotlib

Introduction To Matplotlib
Basic Plotting
Subplots And Layouts
Advanced Plotting Techniques
Plotting With Pandas And Numpy
Customizing Plot Aesthetics

FAQ

What is a Data Analyst?

A Data Analyst collects, processes, and performs statistical analysis on large datasets to identify trends and insights that support business decision-making.

Do I need prior experience to join this course?

No, the course is designed for beginners. Basic knowledge of Excel and an interest in working with data is sufficient to start.

How long does the Data Analyst course take to complete?

The course typically takes around 8 to 12 weeks to complete, depending on the learning pace, including both theoretical and practical sessions.

Will I get hands-on experience with data analysis tools?

Yes, the course includes practical assignments using tools like Excel, SQL, Python, and Tableau, ensuring you gain hands-on experience.

What career opportunities are available after completing this course?

After completing the course, you can pursue roles like Data Analyst, Business Analyst, Data Scientist, and Data Visualization Specialist in various sectors, including finance, healthcare, and marketing.

What Will You Learn

Learn to clean, process, and transform raw data into meaningful insights.
Gain proficiency in using Excel and SQL for data manipulation and analysis.
Master data visualization techniques using tools like Tableau and Power BI.
Understand statistical analysis methods to interpret data trends and patterns.
Learn to create and present data-driven reports and dashboards.
Develop skills in predictive modelling and machine learning techniques for advanced data analysis.

A leading IT training Institute in Coimbatore providing high-quality IT courses

Data Analyst Course In Coimbatore