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Data Analytics Training in Hyderabad

Transform Data into Powerful Insights. Transform Your Future.

Data surrounds us. Every time you click, shop, or even scroll, you’re leaving behind a small digital trail. And data analysts? They’re like detectives with laptops — joining the dots, finding patterns most of us never notice, and turning all that into insights that actually help businesses take smarter calls. If you want to be one of those game-changers, our Data Analytics Training in Hyderabad is the right step toward a high-impact career in a data-driven world.

Why Data Analytics Is The Smartest Career Move Right Now

The digital economy is booming. Businesses are generating vast amounts of data daily, but very few know how to extract value from it. According to recent industry reports, India alone faces a shortage of over 2 million skilled data professionals in the next 5 years.
This means huge opportunity.

Data analytics isn’t only about crunching numbers — it’s about turning them into decisions that actually move the needle. It’s the reason brands today can guess what their customers want, cut down on wasted effort, and stay a step ahead of the competition.

With our Data Analytics Online Training in Hyderabad, you won’t just learn tools—you’ll gain the ability to think critically, identify business problems, and deliver actionable solutions.

Who Should Enroll in This Data Analytics Course?

This course is for those who want to ride the data wave.
Are you:

A fresh graduate unsure of where to start your career?
A marketing, IT, or finance professional wanting to upskill?
An operations manager struggling to make data-backed decisions?
Someone eager to switch to a lucrative and in-demand analytics role?

This is exactly where you should be. Just forget coding expertise or analytics degrees—what matters is a curious mind and the drive to pick up new skills.

What Makes Our Data Analytics Training in Hyderabad Stand Out?

Many courses offer fragmented lessons on tools or concepts. We go beyond that.

End-to-End Learning Experience
From data collection to visualization, you’ll experience the full project life cycle, simulating what top companies expect of data analysts.

Real-World Projects Designed by Industry Experts
You won’t just work on sample datasets. Expect projects like:

Predicting customer churn for telecom companies
Visualizing sales trends to guide retail decisions
Analyzing website traffic to improve digital strategy

Comprehensive Job Support System

Resume building workshops that help your profile stand out
Mock interview preparation to boost your confidence
Direct industry tie-ups for placement assistance

We don’t just teach. We empower you to land the job you deserve.

The Data Analyst Project Life Cycle You’ll Master

It’s not just theory—it’s actionable practice. Our course ensures you understand how data flows in a business environment:

Data Collection & Extraction
Figure out how to pull data from all kinds of places — APIs, databases, CSV files, even web scraping.

Data Cleaning & Preprocessing
Let’s be honest — messy data is what you’ll deal with 90% of the time, not some rare case. You’ll get hands-on with filling in missing info, fixing strange date formats, and handling those weird outliers that don’t seem to belong, all using practical tools like Python and Excel.

Data Exploration & Visualization
Numbers alone don’t tell the full story. You’ll dig into datasets, spot hidden patterns with stats, and then bring them to life using Tableau or Power BI.

Data Modeling & Predictive Analytics
Get the basics of regression, clustering, and classification so you can actually predict what might happen next.

Reporting & Dashboarding
Build dashboards that don’t just look good but also make decision-making easy by showing the right metrics clearly.

Tools & Technologies You’ll Become Fluent In

Our training emphasizes the most in-demand industry tools:

Microsoft Excel: Data manipulation and advanced functions
SQL: Powerful queries to extract insights from large datasets
Python: Automate data analysis, write efficient scripts
Tableau & Power BI: Build dashboards that do more than just show data. You’ll create visuals that people can understand in seconds.
Google Analytics: Get a clear picture of how users behave

Real-World Projects That Boost Your Career

Forget theoretical assignments. Our projects mirror challenges faced by data analysts in the field:

Customer Segmentation for Retailers: Break down customers by their buying habits so brands can run smarter, targeted campaigns.

Web Traffic Analysis for Digital Marketing: Dive into Google Analytics to figure out what’s really working on a website and how to keep visitors hooked.

Sales Performance Dashboard for FMCG: Build dashboards that track sales numbers and KPIs in real time—giving managers the data they need to make quick, solid decisions.

Predictive Market Trend Analysis: Apply basic machine learning models to forecast demand or even spot customers who might drop off.

These projects don’t just teach you—they build a portfolio that impresses employers.

Flexible Learning Options Tailored For You

We understand life’s demands. That’s why our Data Analytics Training Course in Hyderabad offers multiple learning modes:

In-Class Training (Hyderabad)
Experience hands-on, live interactive sessions in our modern labs where you can collaborate and get real-time doubt clearing.

Online Live Training
Hop into the live virtual classes, learn directly from experts, and get instant support whenever you’re stuck, that too all without worrying about travel or long commutes.

The Career Path After Data Analytics Training

Completing this course opens a world of high-paying job roles:

Data Analyst
Business Intelligence Analyst
Reporting Analyst
Market Research Analyst

The best part? Data Analytics roles often offer clear career progression into Data Science, Business Intelligence, and Machine Learning roles.

Why Version IT Is Your Best Choice for Data Analytics Training

When it comes to data training, Version IT isn’t just another institute.

Industry-Aligned Curriculum Designed by Experts
Certified Trainers with Years of Practical Experience
Project-Based Learning You Can Showcase
24/7 Lab Access to Practice Anytime
Job Support Including Resume & Interview Prep

We deliver skills that translate directly to business value.

Who Can Learn Data Analytics?

Our course is designed to be accessible.

Fresh Graduates
Marketing, Finance, Operations Professionals
IT Employees Seeking Analytical Skills
Managers looking to make Data-Driven Decisions

No technical background is required—only a logical mindset and a desire to grow.

Topics You will Learn

MODULE 1: PYTHON FOR DATA ANALYTICS

What You Will Learn in Python

By the end of this module, you will be able to:

✔ Write clean and structured Python programs
✔ Perform data cleaning and transformation using Pandas
✔ Conduct Exploratory Data Analysis (EDA)
✔ Create professional data visualizations
✔ Build predictive machine learning models
✔ Automate repetitive data tasks
✔ Connect Python with SQL databases
✔ Develop end-to-end analytics workflows

Core Python

PYTHON PROGRAMMING

01 – Foundations

Introduction to Python
Installation of Python

02 – Data Types & Variables

Data types
Variables
Input

03 – Operators & Conditions

Operators
if, elif, else statements

04 – Loops

for loops
while loops

05 – Data Structures

Lists, Tuples
Dictionaries, Sets
String Handling

06 – Functions

Function Definition
Parameters & Return

07 – Modules & Files

Date and Time Module
File Handling

08 – Error Handling

Errors & Debugging
Exception Handling

OBJECT-ORIENTED PROGRAMMING

(OOP) – The 4 Pillars

OOPS Principles

Encapsulation

Bundling data and methods together

Data Hiding
Private/Public Access
Controlled Access

Abstraction

Hiding complexity, showing essentials

Hide Implementation
Show Interface
Simplify Complexity

Polymorphism

Objects can take multiple forms

Method Overloading
Method Overriding
Runtime Flexibility

Inheritance

Class hierarchy and reusability

Parent-Child Classes
Code Reusability
IS-A Relationship

Key Benefits: Modularity • Reusability • Maintainability • Scalability • Security

Python Libraries for Data Analytics

Part 1: NumPy, Pandas & EDA

KEY LEARNING OUTCOMES

Master NumPy for numerical computing and array operations
Use Pandas for data manipulation and cleaning
Perform exploratory data analysis with visualization
Create professional visualizations using Matplotlib
Create advanced visualizations using Seaborn
Understand univariate, bivariate, and multivariate analysis
Prepare data for machine learning applications
Build complete data analysis pipelines
Handle real-world messy data effectively

SECTION 1: NUMPY – NUMERICAL COMPUTING

Introduction to NumPy: Framework and advantages
NumPy Attributes: Shape, size, dtype, ndim
Array Creation: zeros, ones, arange, linspace
Indexing & Slicing: Accessing array elements
Iteration over Arrays: Looping through elements
Array Manipulations: Reshape, flatten, transpose
Mathematical Operators: Addition, subtraction, multiplication
Relational Operators: Comparison operators
Mathematical Functions: sin, cos, sqrt, exp, log
Broadcasting: Operating on arrays of different shapes

SECTION 2: PANDAS – DATA MANIPULATION

Introduction to Pandas: Data analysis library
Series: One-dimensional labeled arrays
DataFrames: Two-dimensional labeled arrays
Creating DataFrames: From lists, dicts, numpy arrays
Column Operations: Selection, addition, deletion
Row Operations: Selection, adding, deletion
Merging DataFrames: Join, merge, concatenate
Importing Data: CSV, Excel, SQL, JSON formats
Basic Dataset Insights: head, tail, info, describe
Summarizing Data: Aggregation and statistics

SECTION 3: EXPLORATORY DATA ANALYSIS (EDA)

Univariate Analysis: Analyzing single variables
Bivariate Analysis: Relationship between two variables
Multivariate Analysis: Relationship among multiple variables
Matplotlib Plots: Histogram, Box, Scatter, Line, Pie, Bar
Matplotlib Subplots: Multiple plots in one figure
Seaborn Bar Plot: Categorical data visualization
Seaborn Count Plot: Category frequency visualization
Seaborn Box Plot: Distribution comparison

Python Libraries for Data Analytics

Part 2: Data Cleaning & AI/ML

KEY LEARNING OUTCOMES (Advanced)

Clean and prepare messy data for analysis
Handle missing values and outliers effectively
Understand machine learning fundamentals
Build supervised learning models
Build unsupervised learning models
Implement linear and logistic regression
Apply decision trees and clustering
Evaluate and optimize machine learning models
Prevent overfitting and underfitting
Build complete end-to-end ML pipelines

SECTION 4: DATA CLEANING & PREPARATION

Dealing with Wrong Data Types: Type conversion
Type Checking: Validating data types
Type Conversion: Converting between types
Treating Duplicates: Identifying and removing duplicates
Handling Missing Values: NaN detection and treatment
Missing Value Imputation: Fill, forward fill, backward fill
Handling Outliers: Detection and treatment methods
Statistical Methods: IQR, z-score for outlier detection
Drop Unnecessary Columns: Feature selection
Data Validation: Ensuring data quality

SECTION 5: INTRODUCTION TO AI & MACHINE LEARNING

Machine Learning Basics: Concepts and types
Supervised vs Unsupervised Learning: Key differences
Regression Analysis: Predicting continuous values
Linear Regression: Simple and multiple regression
Logistic Regression: Binary and multiclass classification
Decision Trees: Tree-based classification and regression
Tree Splitting: Gini index and entropy
K-Means Clustering: Unsupervised learning
Model Evaluation Metrics: Accuracy, precision, recall
Confusion Matrix: Classification performance analysis
Cross-Validation: Training and validation strategies
Train-Test Split: Data partitioning for evaluation

MODULE 2: SQL SERVER FOR DATA ANALYTICS

SQL Server for Data Analytics – What You Will Learn

KEY LEARNING OUTCOMES

Master SQL fundamentals, database design, and normalization concepts
Design and create robust relational databases with proper data modeling
Write efficient and optimized SQL queries for complex business problems
Perform advanced data retrieval using subqueries, CTEs, and window functions
Implement ranking, partitioning, and aggregate window functions
Create and manage views, stored procedures, and user-defined functions
Optimize database performance through indexing and query optimization
Conduct comprehensive exploratory data analysis and perform customer segmentation, RFM analysis, and sales analytics
Build CI dashboards, performance reports, and business intelligence summaries

SECTION 1: DATABASE FUNDAMENTALS

Introduction to Databases, DBMS, RDBMS vs NoSQL
MySQL Installation, Configuration & Environment Setup
Data Types (INT, VARCHAR, DATE, FLOAT, DECIMAL, TEXT, BLOB, etc.)
Primary Keys, Foreign Keys, Composite Keys & Candidate Keys
Constraints: NOT NULL, UNIQUE, CHECK, DEFAULT, AUTO_INCREMENT
CRUD Operations: (Create, Read, Update, Delete)
SQL Commands: DDL, DML, DCL, TCL – Understanding Differences
SELECT, Commands with WHERE clause & Filtering Techniques
INSERT, UPDATE, DELETE Commands with Examples
ORDER BY (ASC/DESC), GROUP BY, HAVING Clauses
Logical, Comparison & Arithmetic Operators
Aggregate Functions: SUM, AVG, COUNT, MAX, MIN with GROUP BY

SECTION 2: ADVANCED SQL TECHNIQUES

JOINs: INNER, LEFT, RIGHT, FULL OUTER, CROSS join with real examples
Self Joins & Multiple Table Joins for Complex Queries
Subqueries: Scalar, Row, Table Subqueries in SELECT, WHERE, FROM
Correlated Subqueries & Nested Subqueries for Advanced Filtering
Common Table Expressions (CTE) with WITH clause
Recursive CTEs for Hierarchical Data Processing
Database Normalization (1NF, 2NF, 3NF, BCNF Concepts)
Data Manipulation Strategies for Performance Optimization
Entity-Relationship (ER) Modeling & ER Diagram Creation
Window Functions: ROW_NUMBER, RANK, DENSE_RANK, NTILE
Aggregate Window Functions: SUM OVER, AVG OVER with PARTITION BY
Views: Simple & Complex Views, Stored Procedures & User-Defined Functions

Master SQL Server for Advanced Data Analytics
Build a strong SQL foundation and master advanced techniques!

SQL SERVER FOR DATA ANALYTICS

Part 2: Analytics & Applications

SECTION 3: SQL FOR DATA ANALYTICS

(Key Performance Indicator (KPI) Calculations & Dashboard Metrics)

Revenue Analysis: Total Sales, Growth Rate, Trends, Forecasting
Sales Performance Reports: Top Products, Sales by Region/Category
Customer Segmentation: RFM (Recency, Frequency, Monetary) Analysis
Cohort Analysis: Customer Lifetime Retention, Month-on-Month
Customer Lifetime Value (CLV): Calculation & Segmentation
Product Performance: Sales Volume, Margins, Inventory Levels
Year-over-Year (YoY) Comparisons & Growth Analysis
Month-over-Month (MoM) Trends & Seasonality
Pivot Tables & Cross-tabulation for Data Summarization
Time-Series Data Analysis: Moving Averages, Trends, Seasonality
Anomaly Detection: Identifying Outliers in Data

SECTION 4: REAL-WORLD APPLICATIONS

E-commerce Analytics: Order analysis, Customer Behavior, Conversion Rates
Financial & Banking Analytics: Transaction Analysis, Risk Assessment
Supply Chain Optimization: Inventory Management, Supplier Performance
HR Analytics: Employee Performance, Payroll, Retention Analysis
Marketing Campaign Analysis: ROI, Customer Acquisition, Attribution
Social Media Analytics: Engagement, Reach, Sentiment Analysis
Integration with Python: Pandas, SQLAlchemy, Database Connectivity
Integration with Power BI & Tableau: Query Optimization for BI Tools
Building Automated Reports & Dashboards with Scheduled Queries
Database Security: User Permissions, Role Management, Encryption

Master SQL and Transform Data into Actionable Business Intelligence
Build a Career in Data Analytics with Industry-Ready SQL Skills

MODULE 3: ADVANCED EXCEL FOR ANALYTICS

Excel Basics & Formulas – Part 1

KEY LEARNING OUTCOMES

Master Excel interface, formatting, and data entry techniques
Perform complex calculations using advanced formulas and functions
Create dynamic pivot tables and data summaries
Build interactive dashboards and KPI reports
Use what-if analysis for scenario planning
Analyze data with conditional formatting and validation
Create professional visualizations and interactive charts
Integrate Excel with SQL and Power BI for end-to-end analytics
Develop automated analytical workflows and business reports

SECTION 1: EXCEL BASICS & INTERFACE

Ribbon Interface & Workbook Navigation
Excel Interface and Toolbar Customization
Cell References: Absolute, Relative, Mixed References
Number Formatting: Currency, Percentage, Date Formats
Cell Formatting: Borders, Fill Colors, Font Styles
Data Entry & Input Validation Rules
Sorting & Filtering Techniques
Autofill, Flash Fill & Quick Analysis Tools
Freeze Panes & Splitting Windows
Creating and Managing Worksheets

SECTION 2: ESSENTIAL FUNCTIONS & FORMULAS

Basic Math Functions: SUM, AVERAGE, MIN, MAX
Conditional Functions: IF, Nested IF Statements
COUNT & SUM IF Conditional Counting/Summing
COUNTIFS & SUMIFS for Multiple Criteria
VLOOKUP & HLOOKUP Functions
XLOOKUP: Modern Lookup Function
INDEX-MATCH: Advanced Lookup Replacement
Logical Functions: AND, OR, NOT for Complex Scenarios
IFERROR & IFNA for Error Handling
CHOOSE Function for Multiple Value Selection
INDIRECT Function for Dynamic References

Master Excel Fundamentals for Data Analytics!
Build a strong Excel foundation and grow your analytics skills!

Excel Dashboards & Analysis

Part 2

KEY LEARNING OUTCOMES (Advanced)

Master text and date functions for data manipulation
Clean messy data using Text to Columns & Flash Fill
Create and customize pivot tables for complex analysis
Build dynamic charts and KPI dashboards
Implement data validation and conditional formatting
Perform what-if analysis using Goal Seek & Solver
Create scenario analysis models for decision making
Design interactive reports with slicers and filters
Build professional business intelligence dashboards

SECTION 3: TEXT & DATE FUNCTIONS

Text Functions: LEFT, RIGHT, MID for String Extraction
LEN Function for String Length & TRIM for Cleanup
UPPER, LOWER, PROPER for Text Case Conversion
CONCAT & CONCATENATE for String Joining
FIND & SEARCH for Substring Location
SUBSTITUTE for Text Replacement
TODAY Function for Current Date
NOW Function for Current Date & Time
DATE Function for Creating Specific Dates
DATEDIF for Date Difference Calculations
YEAR, MONTH, DAY Extraction Functions
WEEKDAY & WEEKNUM for Date Analysis

SECTION 4: DATA CLEANING & PIVOT TABLES

Remove Duplicates from Large Datasets
Text to Columns: Delimiter-based Separation
Flash Fill for Pattern Recognition & Auto-fill
Conditional Formatting: Color Scales, Data Bars, Icons
Creating Pivot Tables from Raw Data
Pivot Table Grouping: By Date, Category, Range
Calculated Fields & Calculated Items in Pivots
Pivot Slicers for Dynamic Filtering
Timeline Slicers for Date-based Filtering
Pivot Charts for Visual Data Representation

SECTION 5: ADVANCED FEATURES & DASHBOARDS

Data Validation Rules: List, Number Range, Custom Formulas
What-If Analysis: Goal Seek for Single Cell Optimization
Solver Add-in for Complex Optimization Problems
Scenario Manager for Multiple Scenario Planning
Dynamic Charts: Charts that Update with Data
KPI Dashboards: Creating Business Intelligence Dashboards
Interactive Reports with Slicers & Filters
Sparklines: Mini Charts within Cells
Chart Types: Line, Bar, Pie, Scatter, Combo Charts
Excel Integration: SQL Queries, Power Query, Power BI Connection

Master Advanced Excel & Dashboard Development
Create powerful analytics dashboards and business intelligence reports!

MODULE 4: STATISTICS & PROBABILITY

What You Will Learn

KEY LEARNING OUTCOMES

Master descriptive statistics and data summarization techniques
Conduct hypothesis testing and inferential statistics analysis
Apply probability concepts and distributions to real-world problems
Perform correlation and regression analysis for predictive modeling
Understand sampling techniques and statistical significance
Apply Bayesian reasoning and conditional probability
Analyze normal distributions and use Central Limit Theorem
Build simple and multiple regression models
Interpret statistical results and communicate findings
Make data-driven decisions using statistical evidence

SECTION 1: DESCRIPTIVE STATISTICS

Mean, Median, Mode: Calculating and interpreting central tendency
Range: Understanding data spread
Variance & Standard Deviation: Measuring data dispersion
Coefficient of Variation: Comparing variability across datasets
Percentiles & Quartiles: Analyzing distribution segments
Skewness: Understanding distribution asymmetry
Kurtosis: Analyzing tail behavior and distribution
Summary Statistics: Creating statistical profiles
Data Visualization: Histograms, box plots, density plots
Outlier Detection: Identifying and handling anomalies
Data Summarization: Aggregate statistics and reporting
Distribution Shapes: Normal, skewed, bimodal distributions

SECTION 2: INFERENTIAL STATISTICS & HYPOTHESIS TESTING

Sampling Techniques: Random, stratified, systematic, cluster sampling
Population vs Sample: Understanding parameters and statistics
Sampling Distribution: Behavior of sample statistics
Standard Error: Measuring sampling uncertainty
Confidence Intervals: Constructing confidence bounds
Hypothesis Testing Framework: Null vs alternative hypotheses
Type I & II Errors: Understanding statistical errors
p-value: Interpretation and significance testing
Z-test: Testing means with known population variance
t-test: One-sample, two-sample, and paired comparisons
Chi-Square Test: Testing categorical associations
ANOVA: Analyzing variance across multiple groups

MODULE 5: POWER BI FOR BUSINESS INTELLIGENCE

Power BI for Business Intelligence – Part 1: Fundamentals & Data Modeling

KEY LEARNING OUTCOMES

Master Power BI Desktop and Power BI Service platforms
Connect to and import data from multiple sources
Clean and transform data using Power Query effectively
Design star schema and snowflake schema models
Create relationships between tables for analysis
Optimize data models for performance and clarity
Build foundation for advanced BI solutions
Understand Power BI architecture and components
Implement best practices in data preparation
Prepare data for advanced DAX calculations and visualizations

SECTION 1: POWER BI INTRODUCTION & DATA SOURCES

Power BI Desktop: Installation, interface, workspace
Power BI Service: Cloud platform and collaboration
Power BI Architecture: Desktop, Service, Mobile
Data Sources: Excel, SQL, Azure, Cloud Services
Importing Data: Various import methods and options
Data Connectors: Native and custom sources
Query Settings: Loading and refresh options
Power BI Licensing: Pro, Premium, Per User
Workspace Management: Organization and structure
Gateway Configuration: On-premises connectivity

SECTION 2: POWER QUERY – DATA CLEANING

Power Query Editor: Interface and functionality
Data Cleaning: Removing duplicates and errors
Data Type Conversion: Changing data types
Text Functions: Extracting and splitting text
Date Functions: Formatting and calculations
Conditional Columns: Creating new columns
Merge Queries: Joining tables with join types
Append Queries: Combining rows from tables
Pivot & Unpivot: Reshaping data structures
Replace Values: Finding and replacing data

Master Power BI Foundations and Data Preparation
Build a strong foundation for advanced BI solutions!

Power BI for Business Intelligence

Part 2: DAX, Visualizations & Advanced

KEY LEARNING OUTCOMES (Advanced)

Master DAX (Data Analysis Expressions) language
Write complex formulas and calculated columns
Create advanced measures with time intelligence
Design professional data visualizations
Build interactive dashboards with drill-through
Implement row-level security for data access
Use AI visuals for predictive analytics
Deploy and publish reports to Power BI Service
Optimize performance and identify bottlenecks
Implement collaboration and sharing strategies

SECTION 3: DATA MODELING – SCHEMAS & RELATIONSHIPS

Star Schema Design: Fact tables with dimensions
Snowflake Schema: Multi-level normalized hierarchies
Fact Tables: Transactional and measure data
Dimension Tables: Descriptive attributes
Creating Relationships: One-to-many, many-to-many
Primary & Foreign Keys: Data integrity
Bridge Tables: Handling complex relationships
Model Validation: Ensuring consistency
Calendar Tables: Time dimension creation
Role-Playing Dimensions: Multiple relationships

SECTION 4: DAX (DATA ANALYSIS EXPRESSIONS)

DAX Syntax: Functions, operators, expressions
Calculated Columns: Adding computed columns
Measures: Creating aggregations and KPIs
Context Functions: Row and filter context
Aggregation Functions: SUM, AVERAGE, COUNT
CALCULATE Function: Complex calculations
SUMX & Similar Functions: Iterative calculations
RELATED & RELATEDTABLE: Cross-table relationships
Time Intelligence: YTD, MTD, QTD, YoY, MoM
Advanced DAX: Complex nested formulas

SECTION 5: VISUALIZATIONS, DASHBOARDS & ADVANCED

Chart Types: Bar, column, line, area, scatter
KPI Cards: Key metric displays and gauges
Table & Matrix Visuals: Detailed data representation
Maps: Geographic visualization
Treemaps & Waterfalls: Hierarchical and sequential
Slicers: Single and multi-select filters
Drill-Through: Navigation between pages
Tooltips: Custom information on hover
Row-Level Security: Data access control
AI Visuals & Forecasting: Predictive analytics

SECTION 6: PUBLISHING, SHARING & COLLABORATION

Publishing to Power BI Service: Desktop to cloud
Report Scheduling: Automated refresh settings
Alert Configuration: Data threshold notifications
Sharing Reports: Permissions and access control
Applications: Packaged content distribution
Dashboards: Pinning visuals and metrics
Collaboration: Comments and sharing notes
Power BI Mobile: Mobile app deployment
Email Subscriptions: Scheduled report delivery
Performance Analyzer: Identifying bottlenecks

Module 6: TABLEAU

Basic Module

Introduction to Tableau
Connections
Visual Analytics
Basic Charts
Sorting
Filtering
Grouping
Sets
Built-in Functions (Number, String, Date, Logical and Aggregate)
Operators and Syntax Conventions
Table Calculations

Advance Module

Types of Calculations
Trend lines
Reference lines
Forecasting
Advance Plots
Dashboard

GIT HUB

Integration with Microsoft Fabric

MINI PROJECTS

✔ Retail Sales Dashboard
✔ HR Analytics Dashboard
✔ SQL Customer Database Project
✔ Excel KPI Dashboard
✔ Python Sales Prediction Model

MAJOR PROJECTS

End-to-End Retail Analytics
Banking Loan Prediction Model
Customer Churn Analysis
E-commerce Business Dashboard

Let Your Certificates Speak

Start Your Data Analytics Journey Today

Our Data Analytics Online Training in Hyderabad isn’t about stuffing theory—it’s about getting you job-ready. Learn by doing. Build real-world projects. You’ll learn by doing, working on real-world projects that actually prepare you for the workplace. Get certified. And get placed.
Ready to transform your career?
Ready to turn data into your strongest asset?
Enroll Now and Step Into The Future of Data Analytics.