Best Software Training Institute in Hyderabad – Version IT

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📅 Course Duration

5 Months

💰 Course Price

₹ 30000

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Overview of Data Analytics Training in Mumbai

With the current Data Revolution, information is the new oil, yet unrefined data is merely oil worth nothing without knowledge of how to refine it. Currently, businesses are awash with information, but most of them fail to transform the information into working strategies. It has led to the unmatched need to seek the services of ingenious Data Analysts who can help to bridge the gap between voluminous figures and business development.

In case you seek to pursue a high growth career in one of the most stable and best-paid industries related to IT, the Data Analytics Training practiced at Version IT in Mumbai will be your ultimate instrument of success.

Why Data Analytics in 2026?

Data Analysis is no longer a niche unit; it is the bloodline of today decision-making. Whether it is predicting how consumers interact with Amazon or it is an effort to fight the supply chain and financial fraud, data is omnipresent.

  • Expansion of mass jobs: Data Analysts are going to be one of the highest-demanded jobs by 2026, as projected by the World Economic Forum.
  • Profitable Payrolls: Experienced analysts have one of the best entry-level and mid-level wages in the corporate environment of Mumbai.
  • Flexibility: You are not restricted to technology firms. There are retail, Healthcare, Finance, Sports and Entertainment that require data experts.

Why Choose Version IT for Data Analytics Training in Mumbai?

Mumbai is a competitive center and your training has to be prominent. Version IT offers a curriculum tailored specifically to its industry and shapes beginners to professionals able to work in the job market.

  1. Practical, Work-based Curriculum.

We don’t just teach theory. Our course has LBD as its foundation. The datasets you will be working with will simulate real life business situations meaning that by the time you graduate, you will have a compilation of work that you can present to recruiters.

  1. Expert Mentorship

Our trainers are people of industry experience years of expertise in data modeling, statistical analytical procedures and business intelligence. They introduce the corporate world into the classroom creating not only knowledge of how but also knowledge of why behind each analysis.

  1. Good Infrastructure

Version IT is in the center of the city of Mumbai, which provides a learning setting full of the newest software and high-speed labs. You like face-to-face communication or like the freedom of hybrid learning, we meet your demands.

  1. Niche Placement Putting.

We don’t just stop at training. Our committed placement cell is relentless in ensuring that our students are placed in the best MNCs and start-ups in Mumbai as well as elsewhere. We will remain with you until you find the job of your dreams, through resume building up to the mock interviews.

The Version IT Edge: The "Project-First" Approach

To us, at Version IT, a certificate is nothing more than what the skills are. Your training will include a Capstone Project in a discipline of your choice done in Mumbai:

  • Finance: Evaluate the stock market trends or credit risks trend.
  • E-commerce: Do a Customer Churn Study to understand the reasons people give up using a service.
  • Healthcare: The historical data can be used to predict patient readmission rates.
  • Marketing: Determine the ROI of Internet advertising campaigns.

Who Should Enroll in Data Analytics Course?

Our Data Analytics program is designed to fit a wide spectrum of applicants:

  • Fresh Graduates: Needing to join the IT industry with a specialty.
  • Professionals: As the high-ranking world
  • Business owners: Desire to utilize data to develop their own businesses.
  • IT Enthusiasts: All those who possess analytical mind and like to solve problems.

Version IT’s Career Support & Placement Journey

We know that the final product is that of a successful career. We are the best Data Analytics institute in Mumbai, and this is because of our 3 step placement process:

Step 1: Profile Optimization We assist you in creating a LinkedIn profile and a resume, which passes through ATS (Applicant Tracking Systems) and meets the attention of recruiters.

Step 2: Mock Interview Bootcamps We have intensive technical and HR mock interviews. We give your response on the areas of communication, technical precision and your body language.

Step 3: Career-Branded Job Portal Version Students in the IT program have a unique entry into our system of hiring firms, including boutique data companies to fortune 500 companies.

Enroll Today and Transform Your Career

The world is digitally heading towards a data-centric future. Don’t be left behind. Be the part of the community of successful professionals who can change their lives with Version IT.

Make the first step in becoming a Data Expert. The beginning of your bright future in insights within Version IT Mumbai.

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

FAQ's

No. Although we are aided by coder, our course begins at the very bottom. We learn python and SQL without needing to use libraries.
In most cases, we have a 3 or 4 month long program, and it varies according to the pace of the batches (weekday or weekend).
Yes, once you complete successfully the course and the capstone project successfully, you shall be awarded with a globally recognized certification by Version IT.
We offer 100% placement services. Although the last interview performance is in the hands of the student, we guarantee you the best opportunities and the appropriate preparations to ensure that you perform.

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