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.

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    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.

    Certification

    72% of Vnet Academy students appear for global certifications and 100% of them clear it.

    LIVE Project

    Get the opportunity to work on real-time projects that will provide you with deep experience.

    Affordable Fees

    At Vnet Academy, the course fee is not only affordable, but you can also pay it in installments

    Placement Support

    Dedicated placement support to bridge the gap between learning and earning.

    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.

    Who wants to become a Data Analyst?

    Aspiring data professionals looking to work with data collection, analysis, and visualization should pursue a Data Analyst course. This role is ideal for individuals who enjoy working with data to generate insights, influence decisions, and improve business operations.

    Students and Recent Graduates in Tech Fields

    Students and recent graduates in tech fields can gain valuable skills in data analysis. Learning tools like Excel, SQL, and Python opens up job opportunities in various industries such as finance, marketing, and healthcare, where data-driven decisions are key.

    Freelancers Looking to Expand Their Service Offerings

    Freelancers can benefit from data analysis skills by offering data visualization, reporting, and insights services. With the rise of data-driven decision-making, becoming a proficient data analyst expands freelance opportunities in diverse sectors like business consulting and market research.

    Tech Enthusiasts Looking to Learn Data Skills

    Tech enthusiasts interested in data and analytics can start with a Data Analyst course. It covers foundational data management, visualization, and statistical analysis, allowing them to gain the skills necessary to make sense of large datasets and generate actionable insights.

    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

    About-classroom-training-Vnet-technology-academy-in-coimbatore

    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

    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

    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.

    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

    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

    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 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