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Learn Data Analytics in Visakhapatnam with hands-on projects, tools, and skills to excel in analytics careers.
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- This course helps you learn how to work with data step by step. You start from the basics and go up. It does not matter if you are a student or a fresher. Even IT professionals can join. The Data Analytics Course in Visakhapatnam teaches you how to collect data. You learn how to clean data. You learn how to study data. You learn how to find useful results for good business plans.
- Many students choose Data Analytics Training in Visakhapatnam. They want to build a good profile. They want to get better jobs. They want to earn good money. Some people also change their field. They move into analytics. This course helps them get ready for this new role. You get support and real examples. You learn skills that companies need today.
Data Analytics Course in Visakhapatnam
- The main goal of this course is to make you confident in using data analytics tools to solve practical business problems. It is not just theory. The Data Analytics Course Online focuses on practical knowledge by giving you real-world projects to work on. You also learn how to crack data analytics interviews and pass technical rounds confidently
- This Data Analysis Course will teach you how to collect, clean, study, and present data. Companies use this data to make smart choices. You will learn step by step. You will do real industry tasks. This will help you become a confident and job-ready data analyst.
- This Data Analyst Placement Course Visakhapatnam helps you develop skills that companies really need, not just theory.
How to use Excel and SQL to work with data
Using Python for data cleaning and analysis
Data visualization with Power BI and Tableau
How to interpret data using statistics
Building dashboards and reports for decision-making
Basics of business intelligence and reporting
Real-world projects to prepare you for actual company work
How data analytics helps in sectors like finance, marketing, and healthcare
- The Data Analytics Courses in Vizag give you live projects and practical tasks. You will not just watch videos. You will solve real problems with real data.
- You will start with basic tools. Step by step, you will move to advanced reports and dashboards. Each module has tasks, exercises, and case studies. You will attend live classes. You will also get videos to revise anytime. You will keep your study material, quizzes, and tasks forever with lifetime LMS access.
- You will work with tools like Excel, SQL, Python, Tableau, and Power BI. The course includes help for job placements. You will get resume help, mock interviews, and job referrals. You can pick weekday, weekend, or fast-track batches. You will finish with a capstone project. This project will use real industry data. It will show your skills to employers.
- Eligibility Criteria For Data Analytics Course:
- This Data Analysis Training in Visakhapatnam is open for anyone who wants a career as a data analyst. You do not need a special degree. Basic computer skills and an interest in numbers are enough.
- Who Can Join This Course:
- Students or freshers from B.Tech, B.Sc, B.Com, MBA, or MCA backgrounds who want a job in analytics. Working professionals in IT support, testing, or non-tech roles who want to move to analytics. Entrepreneurs who want to use data for better choices. Career switchers from sales, HR, or admin roles who wish to shift to tech jobs. Freelancers who want to offer data reports or dashboard services.
- When you finish your Data Analytics Training in Visakhapatnam, many paths open for you. You can work in different roles. Some roles are Data Analyst, MIS Analyst, Business Analyst, or Reporting Executive. All companies need people who can read data well. Good data work helps companies grow.
- What You Can Do After the Course:
- Expected Freshers Salary:
- After finishing the Data Analytics Courses in Vizag, freshers can expect decent salaries if they have good practical skills.
- Salary depends on:
Job: You can apply for jobs like Data Analyst, Business Analyst, or MIS Executive in many fields.
Freelancing: You can work on your own. You can make reports, build dashboards, or do small projects for clients.
Higher Studies: This course is a good base for more learning. You can go for AI, ML, or Business Analytics next.
Startups: You can use your skills to build your own service or small business. Many people now start data-based startups.
Data Analyst: 4 to 6 LPA
MIS Analyst: 3.5 to 5.5 LPA
Junior BI Analyst: 4 to 6 LPA
Intern (Data): 12,000 to 25,000
The number of projects in your portfolio
Your command of tools like SQL, Excel, and Power BI
Your performance in technical rounds
- This course not only helps you get a first job but also gives you a strong foundation for growth. With experience and further learning, you can reach senior positions in 2 to 4 years
- Career Paths:
- Extra Skills That Help You Grow:
Data Analyst Senior Analyst Business Analyst Analytics Manager
Data Analyst BI Developer BI Manager
Data Analyst Data Scientist Senior Data Scientist
Cloud data handling (AWS, Azure)
Advanced BI tools
Big data basics (Hadoop, Spark)
NoSQL databases
Strong portfolio with GitHub projects
- In Vizag, companies now want more data analysts. They use more digital tools. They use more business intelligence. The Data Analytics Training Institute in Visakhapatnam is trusted. Many students and working people choose it. The trainers have good skills. The course is not costly. It gives real projects.
Your mentors have more than ten years of hands-on experience in the field.
You work on real projects made by experts.
You get help for your career.
You get help to write your resume.
You practice for interviews.
The group helps with job contacts.
You join a strong alumni group.
You learn top tools. Some tools are SQL, Power BI, and Python.
You get full support while you learn.
The training makes you job-ready.
This is why many people trust this course in Vizag.
- Well-organized modules guide you from beginner to advanced level step by step. Each module has practical tasks so you know how to use tools in real life.
- When you complete the Data Analytics Course, you will get certificates. These show your training and skills. They make your CV stronger. They give you an edge when applying for jobs. This course will also prepare you for other top certifications. These include exams by Microsoft, IBM, or Google.
Introduction to Data Analytics
Advanced Excel for Data Analysis
SQL for Data Management
Python Basics for Analytics
Data Visualization with Tableau and Power BI
Business Intelligence Basics
Statistics for Analytics
Reporting and Dashboarding
Capstone Project with Live Data
- You May Also Read:
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CURRICULUM & PROJECTS
Data Analytics Training Program
- 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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- 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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- 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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- 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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- 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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- 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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- Capstone Project
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