Transform your Career with
Data Analytics Training in Hyderabad
Want to build a career in Data Analytics?
Join Data Analytics Training in Hyderabad at Sri Vinay Tech House and learn the essential tools used for data analysis and business reporting.
Learn the complete Data Analytics toolkit — Excel, SQL, Power BI, Tableau, Python, Power Query, Statistics, Data Visualization and Generative AI — with hands-on practice, business case studies, real-world datasets and portfolio projects.
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What Is Data Analytics?
Data Analytics is the process of examining data to discover:
Trends
Patterns
Relationships
Problems
Opportunities
Business insights
A Data Analyst takes raw information and turns it into useful answers
Students can Expect
When you join our Data Analytics Training in Hyderabad, you can expect:
Recorded Sessions
Revisit sessions anytime access to recorded classes (1.5 years)
Why Choose Our Data Analytics Training?
Learning Data Analytics is not only about watching videos or studying theory. You need to practice with data and solve business problems.
Our training approach focuses on:
Training Feature What You Get
Trainer Support Learn concepts step by step
Hands-on Practice Work with datasets
Dashboard Practice Create Power BI reports
SQL Practice Solve business queries
Python Practice Analyze datasets
Projects Build portfolio projects
Interview Preparation Technical & scenario questions
Doubt Support Get help while practicing
Career Opportunities
After developing the required skills and experience, learners can explore roles such as:
Job Role Common Skills
Data Analyst (Excel, SQL, Power BI)
BI Analyst (Power BI, SQL, DAX)
Reporting Analyst (Excel, Power BI)
MIS Analyst (Excel, Reporting)
Business Analyst (SQL, Excel, Business Analysis)
Junior Data Analyst (Excel, SQL, Visualization)
Power BI Analyst (Power BI, SQL)
Note: Job eligibility and requirements vary by company and experience level.
Why Choose Sri Vinay Tech House?
Practical Learning
Practice concepts with datasets and exercises.
Trainer Guidance
Get guidance while learning and practicing.
Project-Based Learning
Work on business-oriented analytics projects.
Multiple Analytics Tools
Learn Excel, SQL, Power BI, Tableau, Python, Power Query, Statistics, Data Visualization and Generative AI
Interview Preparation
Practice technical and scenario-based questions.
Beginner-Friendly
Start from fundamentals and progress step by step.
Students
Freshers
Career Change
Available Modes
For only Recorded Sessions / Corporate Training, Contact us at +91 9859 831 831
Data Analytics Training Features
Expert Trainers
Comprehensive Curriculum
Hands-On Real-Time Projects
Placement Assistance
Certification
Lifetime Support
Data Analytics Training in Hyderabad
CURRICULUM
CURRICULUM
Excel Course Content With AI Integration
⭐ Description of the Interface
⭐ The Menu System
⭐ The Fundamentals of Spreadsheets
⭐ Changing Excel’s Default Options using Autocorrect and Customizing It
⭐ Customizing the Ribbon
⭐ Using Functions – Sum, Average, Max, Min, Count
⭐ Customizing the Ribbon
⭐ SumIf, SumIfs CountIf, CountIfs AverageIf, AverageIfs
⭐ Upper, Lower, Proper
⭐ Left, Mid, Right
⭐ Trim, Len, Exact
⭐ Concatenate
⭐ Find, Substitute
⭐ Formatting Cells with Number formats, Font formats, Alignment, Borders, etc
⭐ Basic conditional formatting
⭐ File level Protection
⭐ Workbook, Worksheet protection
⭐ A description of the interface
⭐ Customizing Headers & Footers
⭐ Designing the structure of a template
⭐ Designing the structure of a template
⭐ Paste Formulas, Paste Formats
⭐ Transpose Tables
⭐ Paste Validations
⭐ Today, Now
⭐ Date, Date if, DateAdd
⭐ Day, Month Year
⭐ Month, weekday
⭐ New Charts – Tree map & waterfall
⭐ Combo Charts – Secondary Axis
⭐ Sunburst, Box, and Whisker charts
⭐ Using Power Map and Power View
⭐ Adding slicers Tool in Pivot & Tables
⭐ sparklines – Line, column & Win/Loss
⭐ Forecast Sheet
⭐ Smart Lookup and manage Store
⭐ New Controls in pivot Table – Field, Items, and Sets
⭐ Using 3-D Map
⭐ Autocomplete a data range and list
⭐ Filtering on Text, Numbers & Colors
⭐ Sorting Options
⭐ Advanced Filters on 15-20 different criteria(s)
⭐ Setting Print Area
⭐ Print Titles – Repeat Rows/Columns
⭐ Designing the structure of a template
⭐ customizing headers & Footers
⭐ Goal Seek
⭐ Data Tables (PMT Function)
⭐Solver Tool
⭐Scenario Analysis
⭐ TODAY, NOW, DATE, YEAR, MONTH, DAY
⭐ NETWORKDAYS, WORKDAY, WEEKDAY, EDATE, EOMONTH
⭐ DATEDIF, DATEVALUE, TIME, TIMEVALUE, TEXT, YEARFRAC
⭐ PMT, FV, PV, RATE, NPER
⭐ IPMT, PPMT, NPV, IRR
⭐ ISERROR, ISNA, ISBLANK, ISNUMBER, ISTEXT, ISLOGICAL
⭐ New Charts: Tree Map, Waterfall, Sunburst, Box & Whisker
⭐ Combo Charts with Secondary Axis
⭐ 3-D Maps, Power Map, Power View
⭐ Pivot Enhancements: Slicers, Timelines, Fields, Items, Sets
⭐ Quick Analysis Tool, Smart Lookup
⭐ AutoComplete & Forecast Sheet
⭐ Sparklines (Line, Column, Win/Loss)
⭐ Filter by Text, Numbers, Colors
⭐ Advanced Filters (15–20 criteria)
⭐ Array Formulas
- TRANSPOSE, FREQUENCY, LARGE, SMALL
- RAND, RANDBETWEEN, SEQUENCE, UNIQUE
- FILTER, SORT, SORTBY, TEXTJOIN, CONCAT
⭐ Goal Seek
⭐ Data Tables
⭐ Solver Tool
⭐ Scenario Analysis
⭐ Number, Date & Time Validation
⭐ Dynamic Dropdown List Creation using Data Validation -— Dependency List
⭐ Custom validations based on a formula for a cell
⭐ Text and List Validation
⭐ If Function
⭐ Complex if and or functions
⭐ Nested If
⭐ How to Fix Errors — iferror
⭐ What are the Array Formulas, Use of the Array Formulas?
⭐ Array with if, len, and mid functions formulas.
⭐ Basic Examples of Arrays (Using ctrl+shifttenter).
⭐ Advanced Use of formulas with Array.
⭐ Array with Lookup functions
⭐Vlookup / HLookup
⭐ Vlookup with Helper Columns
⭐ Creating Smooth User Interface
⭐ Index and Match
⭐ Reverse Lookup using Choose Function
⭐ Nested VLookup
⭐ Worksheet linking using Indirect
⭐ Creating Simple Pivot Tables
⭐ Classic Pivot table
⭐ Basic and Advanced Value Field Setting
⭐ Calculated Field & Calculated Items
⭐ Grouping based on numbers and Dates
⭐ Using SLICERS, Filter data with Slicers
⭐ Various Charts i.e. Bar Charts / Pie Charts / Line Charts
⭐ Manage Primary and Secondary Axis
⭐Planning a Dashboard
⭐ Adding Dynamic Contents to Dashboard
⭐ Adding Tables and Charts to Dashboard
POWER BI Course Content
MANDATORY Topic 1: POWER BI DEMO AND DATA WAREHOUSE FUNDAMENTALS
⭐ Data, Data Availability types in IT
⭐ IT data storage areas [File stream, Database]
⭐ Data warehouse, BI definitions and layers
⭐ Power BI existence in IT, Product services
⭐ Power BI Tools and Components
⭐ On-Premise vs. Cloud
⭐ OLAP vs. OLTP vs. Insights and Analytics
MANDATORY Topic 1: POWER BI DEMO AND DATA WAREHOUSE FUNDAMENTALS
⭐ Data warehouse, Data Mart and differences
⭐ Types of data marts and real-time usage
⭐ DWH Life Cycles DWH principles
⭐ DWH Approaches (INMON and KIMBALL)
⭐ Data Granularity, Data movement stages
MANDATORY Topic 2: DATA MODEL THEORY AND PRACTICAL
⭐ Data model, use of data model in Power BI
⭐ Business, Conceptual, Logical, Physical models
⭐ Dimension, Dimension table & types
(Conformed, Roleplay, Degenerated, Junk, SCD)
⭐ Measures & Types
(fully additive, semi additive, non additive)
⭐ Fact table & Types
( Snapshot, Incremental, Factless fact)
⭐ Schemas (Star, Snow Flake, Galaxy & Hybrid)
⭐ Surrogate key and usages in real time
⭐ 1:1, 1: Many, Many: Many relationships
⭐ Active, Inactive relationships
⭐ Single and Bi-directional, Cross filter
⭐ Advanced properties of fields
(sort order, date category, aggregators)
⭐ Snow Flake schema exmaples
TOPIC 1: INSTALLATION [POWER BI DESKTOP, GATEWAY, SERVER, REPORT BUILDER & DAX STUDIO]
⭐ Power BI Cloud / Report Server Desktop installation
⭐ DAX Studio installation, Gateway Installation
⭐ Report Server Installation
⭐ Paginated Report Builder Installation
TOPIC 2: WORKING WITH POWER BI DESKTOP AND CONNECTING TO MULTIPLE DATA SOURCES
⭐ Desktop over view and building blocks of
PBI Desktop
⭐ Connecting to various sources and
retrieving data
1. Flat, CSV, JSON, Excel files
2. Databases (SQL Server & Oracle)
3. Web
4. Multiple files load at a time
5. Azure SQL database and DWH
6. Analysis Services Models
(Tabular & ulti-dimensional)
7. Python Script
8. M-Retrieval
TOPIC 3: WORKING WITH DATABASES, CUBES, MODES & CHANGING MODES
⭐ Working on data modes[very detailed]
a)Import b)Direct Query c)Connect Live d) Mixed mode e) Chanding modes
⭐ Differences between Load and Edit options.
⭐ Retrive date from related data bases and cube
TOPIC 4: WORKNG ON POWER BI DESKTOP COMPONENTS
Power Query, Power Pivot, Power View, Power Map
TOPIC 5: POWER VIEW MENU TAB OPTIONS-RIBBON OPTIONS & REAL-TIME
a)New, Open, Save report
b)Export Power BI Template (PBIT), PDF
c)Import Power BI Template, Power Query,
View, Model, Visual files
d)Options and Settings & About
a)External Data:
Get Data, Recent Sources, Enter Data
b) Transform data & edit parameters
c) Publishing to cloud service
d) Prop data AI (2025)
e) Quick sources
f) Sensitivity
⭐ Report Phone Layout / Desktop View
Show Gridlines, Snap Objects to Grid
Locking Objects on the surface
⭐ Bookmarks creation, viewing, & using in
Dashboard Selection Pane
[Hiding and showing objects]
⭐ Slicer and Sync Slicers explanation with
practical Performance analyzer test run
⭐ Scale to fit: Page view, Mobile: Mobile
layout, Page options
⭐ Show panes: Filters, Bookmarks, Selection,
Performance Analyzer, Sync slicers
⭐ Visuals: New Page, New Visual, More Visuals
⭐ AI Visuals: Key Influencers, Decomposition
Tree, Smart Narrative, Q & A
⭐ Elements: Text box, Buttons, Shapes, Image
⭐ Power Platform: Paginated Report,
Power Apps, Power Automate
⭐ Sparklines: Add a Sparkline
a) Edit interactions for the visual for
cross highlighting, cross filtering, and
none options.
b) Bring forward, send backward etc…
visual option (Z-Order)
a) Drill down report creation
b) Drill one level down, multiple levels, &
data drill
c) Visual table, Data point table
a) Relationships: Manage Relationsips
b) Create roles and implement row-level
security & dynamic row-level security
Page Refresh: Change detection
Parameters: New parameter
Security: Manage roles, View as
Calculations: New measure, Quick measure,
New column, New table
Q & A: Language, Lunguistic Schema
a) Guided learning, documentation, training videos, blogs, communities, Power BI for developers, support and consulting services
⭐ DAX studio
⭐ Refresh, Pause, Preset, Optimization
TOPIC 6: POWER BI DATASET PROPERTIES
⭐ Create hierarchies and analyzing data
⭐ Create Groups (List, Bin)
⭐ Hide / Unhide columns usage in real-time
⭐ Consider table as Date Table
⭐ Expand and collapse columns
⭐ Incremental refresh & manage aggregations
TOPIC 7: POWER QUERY PROPERTIES [ 8 types of properties and M-Language ]
a) Practical on duplicates & references
b) Create groups to classify tables
c) Refreshing table data, Creating reusable functions
a) Data transformations by adding, removing & retaining columns, duplicates, error
b) Working on reusable functions
c) Merging & Joining multiple queries
⭐ Column Properties
⭐ Numeric, text, date tranforms
⭐ Numeric, text & date tranforms
⭐ Working on Any, List & Query parameters
⭐ Working on multiple & cascading parameters
⭐ Transpose rows, reverse rows, Pivot & Unpivot
⭐ Quality Transforms [profiling, quality, distribution]
⭐ Syntax, protocols, variables & conventions
⭐ Let, In, semicolon, and other functions
TOPIC 8: POWER BI VIEWS, FILTERS, VISUALS AND VISUAL FORMATING OPTIONS
a) Report
b) Data
c) Relationship
d) DAX Query
⭐ Visualization
⭐ Page
⭐ Report
⭐ Card, Multi-row, Table, Matrix, New -card (2024)
⭐ KPI, Gauge, Bullet Chart
⭐ Charts
⭐ Rounded charts : Pie, donut
⭐ Bar: Stacked, Clustered, 100% Stacked
⭐ Trend: Line, Area, Stacked Area, Ribbon, Mixed, Treemap,
Funnel, Scatter, Waterfall
⭐ Maps (Bubble, Filled, Shape, ARCGis, Azure map 2024)
⭐ AI Visuals: Key-influencer, De-composition, Smart Narrative, QA
⭐ Others: Sparklines, Metric, Paginated Report, R, Python
a) Bullet Chart
b) Chiclet Slicer
c) Hierarchy Slicer
d) Gantt Chart
e) Histogram Chart
f) Dual KPI
g) Scroller
a) Image
b) Textbox
c) Shapes
d) Button actions
Actions: Bookmark, page navigation, back , QA, URL, apply slicers & clear slicers
a) Slicers: types, usages, sync slicer
b) Filers & types
c) Edit Interactions
d) Parameters & What-if parameters
TOPIC 9: POWER BI CLOUD SERVICE (app.powerbi.com)
1) Navigation pane, Portal URL
2) App Launcher, Settings, Help & Support
3) Feedback, Account Information
⭐ My workspace and properties
⭐ User app workspaces and properties
⭐ Workspace Settings and Roles
⭐ Workspace Access
⭐ Create dashboards
⭐ Add the below tiles
⭐ Image, Textboc, Video, Streaming,
⭐ Live page, Visual, Bookmark, Workbook,
⭐ Insight, Usage metrics
⭐ Share and Subscribe dash board
⭐ Refresh Dashboard
⭐ Manage Alerts
⭐ Create, modify, and delete appspace
⭐ Add content (show or hide)
⭐ Audience (organization or users)
⭐ Additional Settings (copy, build)
⭐ Publish and Update app
⭐ Browse App in Web, Desktop and Mobile
⭐ END USER EXPERIENCE PROVIDED
⭐ Report Server Desktop and Report Server install and practice.
TOPIC 10: DAX - The below are the DAX categories, a few important functions covered for 6-7 hours
⭐ Syntax and usage
⭐ Naming conventions, parameters
⭐ Operators, functions (15 categories)
⭐ Context transition (row, column, filter, multi-row)
⭐ DAX Queries, DAX Studio and SSMS
⭐ DAX FAQS, Optimization & Standards
Good material provided for all the functions
a) Date
b) Filter
c) Math & Trig
d) Statistical
e) Table Manipulated
f) Informational
g) Relationship
h) Logical
i) Parent Child
j) Time Intelligence
k) INFO Function
l) Windows Functions (2022 Dec)
NEW Functions (2024)
Statements: Evaluate, Define, Var, Order By
Calendar, Calendarauto, Day, Month, Year YearFrac, Date, Time, DateValue, TimeValue, Edate, EOMonth, Weekday, WeekOfMonth Today, UTCToday, Now, UTCNow, DateDiff
DatesBetween, DatesInPeriod, ClosingBalanceofmonth, Quarter, Year OpeningBalanceOfMonth, Quater, Year Datesytd, Datesqtd, Datesmtd, Totalqtd, Totalmtd, Totalytd ,FirstDate, LastDate, FirstNonBlankDate, Lastnonblankdate, NextDay, NextMonth, NextYuarter, NextYear, ParallelPeriod, SamePeriodLastYear, PreviousDay, PreviousMonth, Quarter, Year StartOfMonth, Quarter Year
And, Or, Not, Iferror, If, Switch, In, True , False, Coalesce
Path, Pathlength, Pathcontains, Pathreverse, PathItem
INFO- View.Measures, View.Columns, View.Tables
Index, Offset, Partition, Window
Addmissingitems, Filter, Filters,Distinct, Values, Countrows, All, Allexcept, Earlier, Earliest, KeepFilters, Remove filters, Calculate, Calculatetable, AllSelected, AllNoBlankRow
Related, Relatedtable, UseRelationship, Crossfilter, ContainsStringExact
ContainsRow, Contains, ContainsString, Iseven, Isodd, Istext, UserName, UserPricipalName, LookUpValue, IsError, IsNonText, IsNumber, IsLogical, Isinscope, Isonorafter,Hasonefilter, IsCrossFiltered
Sum, Sumx, Average, AverageX, AverageA, Max,MaxX,MaxA, Min, Minx,MinA,Count, CountX, CountA, CountBlank, DistantCount, DistantCountNoBlank, Product, ProductX, Courtrows, Approximate, Distinctcount
Round, MRound, Roundup, Rounddown, Rand, RandBetween , Sqrt, Trunc, ln, Ceiling, Floor, Gcd, Lcd, Exp, Fact, Even, Odd, ABS, Convert, Currency, Sign
Permut, Rank.eq, Rankx, Sampl,
Statistical data, Operation functions
More DA Statical functions covered
AddColumns, AddMissingItems, CrossJoin, CurrentGroup, DataTable, DetailRows, DistinctColumn, DistinctTable, Except,Filters, Generate, GenerateSeries, Groupby, DataTable, Except, Intersect, Union, Symmarize, Summarizecolumn, GenerateSeries, NaturalInnerJoin, NaturalLeftOuterJoin, Crossjoin, Treatas, Isempty, Row SelectColumns, Values,
Blank, Code,Unichar,Concatenate, Combinevalues, Cacatinatex, Trim, Rept, Replace, Substitute, Find, Search, Format, Mid, Left, Right, Value, Upper, Lower
First, Last, Previous, Next, Rank, Rownumber, Lineast, Match
Python for Data Analytics
⭐ What is Python
⭐ What are the popular programming languages
⭐ Why choose python over other programming languages
⭐ How is python perfect for Data Analytics
⭐ Job opportunities after completion of the python course
⭐ Different types of IDEs used for Python programming
⭐ Python Installation
⭐ The print statement
⭐ Comments
⭐ Keywords
⭐ Operators
⭐ Variables
⭐ Data types
⭐ Sequences, mutable and immutable objects
⭐ Type casting
⭐ String operations
⭐ Indexing and Slicing
⭐ Simple programs with user input
⭐ Indentation
⭐ Simple if statement
⭐ If else statement
⭐ Elif statement
⭐ Nested if condition
⭐ One line if conditions or short hand if conditions
⭐ List
⭐ Tuple
⭐ Set
⭐ Dictionary
⭐ Nested collections
⭐ Comprehension programming on Lists and Dictionaries
⭐ Real time examples using Collections
⭐ Built in functions
⭐ User defined functions
⭐ Function with no arguments and no return value
⭐ Function with arguments but no return value
⭐ Types of arguments
⭐ Local and global variables
⭐ Recursive functions
⭐ Lambda function
⭐ Mapping, filter and reduce
⭐ Built in modules
⭐ User defined modules
⭐ Built in functions
⭐ User defined functions
⭐ Function with no arguments and no return value
⭐ Function with arguments but no return value
⭐ Types of arguments
⭐ Local and global variables
⭐ Recursive functions
⭐ Lambda function
⭐ Mapping, filter and reduce
⭐ Built in modules
⭐ User defined modules
⭐ Types of Errors
⭐ Exception handling
⭐ Try, except, finally
⭐ Raise
⭐ Findall
⭐ Tuple
⭐ Split
⭐ Sub
⭐ Regex Functions with meta characters
⭐ Open(), read(), write(), close()
⭐ Export data to text files
⭐ Basic concepts of OOP
⭐ Difference between general programming and OOP
⭐ Classes and Objects
⭐ init () method
⭐ Self parameter
⭐ Single inheritance
⭐ Multilevel inheritance
⭐ Multiple inheritance
⭐ Hierarchical inheritance
⭐ Polymorphism
⭐ Data structures in Pandas : Series and DataFrames
⭐ Import and Export data
⭐ Working with Excel, CSV, Json, Delimited data files
⭐ Sorting data (Ascending/Descending)
⭐ Loc[] and iloc[]
⭐ Search and Filter data
⭐ Value_Counts
⭐ Grouping data
⭐ Pivot tables
⭐ Merging data
⭐ Concatenation of data
⭐ Data cleaning
⭐ Remove null values
⭐ Fill null values with mean(), median() and mode()
⭐ Identify duplicate records
⭐ Remove duplicate records
⭐ Matplotlib Pyplot
⭐ Matplotlib Plotting
⭐ Matplotlib Markers
⭐ Matplotlib Line
⭐ Matplotlib Labels
⭐ Matplotlib Grid
⭐ Matplotlib Subplot
⭐ Matplotlib Scatter
⭐ Matplotlib Bars
⭐ Matplotlib Histograms
⭐ Matplotlib Pie charts
⭐ Creating Arrays
⭐ Array indexing
⭐ Array slicing
⭐ 1D Arrays
⭐ 2D Arrays
⭐ 3D Arrays
⭐ Array join
⭐ Array Search
⭐ Array filter
⭐ Table creation in Oracle
⭐ Oracle database connection in Python
⭐ Collections and documents in NoSQL
⭐ NoSQL database connection in Python
⭐ Creation of Database and Tables in MySQL
⭐ MySQL database connection with Python
⭐ Near real time examples with DB Connections
SQL Server
MANDATORY Topic 1: DATA, DATABASE AND DATAWAREHOUSE FUNDAMENTAL
⭐ Data and Data Availability in IT
⭐ Database, Data Warehouse, and RDBMS
⭐ Data storage areas [structured, semi and unstructured
⭐ RDBMS real-time projects and areas
⭐ Components of RDBMS
⭐ Normalized and de-normalized databases
⭐ [BI and non-BI]
⭐ SQL Versus T-SQL [MS SQL]
⭐ Other popular database in IT [ORACLE
⭐ and TERADATA] and differences
⭐ SQL Server Job Market and Opportunities
⭐ Power BI History, releases and blogs
⭐ Data warehouse, Data Mart and differences
⭐ Types of data marts and real-time usage
⭐ ODS, Stage, EDW, and DW definitions
⭐ Data Lake and Blob Storages
⭐ DWH Life Cycles
⭐ Data Granularity, Data movement stages
⭐ Installing SQL Server Instance and multiple Instances
⭐ SSMS Installation, Azure Data Studio, SQL Developer and Operations Studio
⭐ SQL Server Service Starting
⭐ Server name or Instance name & authentication
⭐ Versions and Editions in SQL Server
⭐ Connections [Local and Remote]
⭐ Editions of SQL Server – Enterprise Edition, Standard Edition, Developer Edition,
⭐ Work Group Edition, Express Edition
⭐ System Defined Databases & usages – Master, MSDB, TEMPDB and others real-time usage
⭐ User defined databases and usages
⭐ Database creation GUI and Code [MDF & LDF files]
⭐ Differences between command and query
⭐ Query and command execution in SQL Server
⭐ Parser, Compiler, Syntaxer, Optimizer, and CLRA
⭐ Storage Engine [SQL Engine]
⭐ SQL Server Data Definition Language [DDL]
⭐ SQL Server Data Manipulation Language [DML]
⭐ SQL Server Data Control Language [DCL]
⭐ SQL Server Transaction Dictionary Language [TCL]
⭐ SQL Server Data Retrieval Language [DRL]
⭐ Inserting data into table [SELECT, INSERT and SELECT]
⭐ Single Insert and Multiple Inserts
⭐ Modifying table data [UPDATE]
⭐ Removing table data [DELETE]
⭐ BULK INSERT and BCP [Bulk Copy Program]
⭐ MERGE command operation and Incremental Load [SCD and CDC]
⭐ SQL Server Data Types
⭐ Insert table from another table
⭐ Providing privileges [GRANT]
⭐ Removing privileges [REVOKE AND DENY]
⭐ Saving work [COMMIT]
⭐ Restore work [ROLLBACK]
⭐ Saving period of work [SAVEPOINT]
⭐ Working on SELECT statement
⭐ Working on WHERE, GROUP BY, HAVING, and ORDER BY
⭐ Column and Table Aliases usage
⭐ =, !=, <>, >, <, <=, =< etc… comparison Operators
⭐ AND, OR, NOT Logical operators
⭐ +, -, *, / , Mod, Exp etc…Mathematical Operators
⭐ Order By, Top, Where, From and Like
⭐ Group by and Having
⭐ IN, NOT IN, BETWEEN and NOT BETWEEN
⭐ ISNULL and NOT ISNULL
⭐ Simple Sub Query
⭐ Correlated Sub Query
⭐ Differences between simple and correlated
⭐ Nested Sub Query
⭐ Working on TOP, MAX, and MIN real time queries
⭐ Set theory generic protocols
⭐ INTERSECT
⭐ UNION
⭐ UNION ALL
⭐ EXCEPT
⭐ Working on incremental loading
⭐ JOINS real time usage
⭐ CROSS JOIN and CROSS APPLY
⭐ INNER JOIN [EQUI, NON EQUI]
⭐ NATURAL JOIN
⭐ SELF JOIN
⭐ INNER Vs. OUTER JOIN
⭐ LEFT OUTER JOIN
⭐ RIGHT OUTER JOIN
⭐ FULL OUTER JOIN
⭐ Working on ON and WHERE clauses
⭐ MERGE JOIN
⭐ LOOP JOIN
⭐ HASH JOIN
⭐ Unmatched data retrieval
⭐ Incremental load in real time using Joins
⭐ CHECK, NOT NULL, AND DEFAULT – Domain Integrity
⭐ Primary Key usage and limitations
⭐ Unique Key usage and limitations
⭐ Referential Integrity and FORGINE KEY
⭐ Candidate key and Alternate key
⭐ Normal column and Identity column
⭐ Surrogate key and Identity column usage
⭐ CASCADING OPTIONS
⭐ ON UPDATE SET NULL, ON UPDATE SET NO ACTION(Default)
⭐ ON DELETE CASCADE, ON UPDATE CASCADE, ON DELETE SET NULL,
⭐ Clustered Index Design and Structures
⭐ Nonclustered Index Design and Structures
⭐ Unique Index Design
⭐ Index with Included Columns
⭐ Column storage index
⭐ Full-Text Index population
⭐ Filtered Index Design
⭐ Covering Index Design
⭐ B-Tree and Online Indexes
⭐ Indexed views Vs. Materialized views
⭐ Fill Factor, TEMPDB, Pat_Index
⭐ Clustered Index Design and Structures
⭐ Nonclustered Index Design and Structures
⭐ Unique Index Design
⭐ Index with Included Columns
⭐ Column storage index
⭐ Full-Text Index population
⭐ Filtered Index Design
⭐ Covering Index Design
⭐ B-Tree and Online Indexes
⭐ Indexed views Vs. Materialized views
⭐ Fill Factor, TEMPDB, Pat_Index
⭐ Differences between GROUP BY and DISTINCT and performance impact
⭐ GROUP BY and HAVING usages to identify and eliminate duplicates
⭐ ROLLUP and CUBE usages
⭐ Generating FULL TOTALS and SUB TOTALS
⭐ Comparing ROLLUP, CUBE and GROUP functions
⭐ Advantages of Views in SQL
⭐ Tables Vs. Views
⭐ Simple View (Updatable View)
⭐ Complex View (Non-Updatable View)
⭐ Materialized View and real time usage
⭐ Encrypted views Vs. Cascading views
⭐ Limitations of Views
⭐ Date Functions – DATEADD, DATE DIFF, DATE PART, FLOOR, CEILING, GETUTCDATE,
, GETDATE, CURRENT_TIMESTAMP, SYSDATETIME, DATE NAME, ISDATE, WEEKDAY,
MONTHNAME, WEEKDAYNAME, SECOND, MINUTE, HOUR, ISDATE
⭐ Other Generic Functions – COALESCE, NULL IF, CURRENT USER, IIF, COALESCE,
NULL IF, CURRENT USER, IIF, SESSIONPROPERT, SYSTEM_USER, USER_NAME, FORMAT,
INSTR, CONCAT
⭐ Cast and Convert Functions
⭐ IF, ELSE, CASE, WHEN & END
⭐ PIVOT & UNPIVOT
⭐ ANALYTICAL FUNCTIONS – ROW_NUMBER () and real time examples, RANK () and real
time examples, DENSE RANK () and OVER () usages, NTILE advantage, PARTITION BY
advantage, Using Group BY along with Analytical Partition
⭐ User Defined Functions Create
⭐ User Defined Functions Calling
⭐ Differences between Function &Procedure
⭐ Use in Real Time and Types
⭐ System Defined and User Defined Procedures
⭐ Dynamic SQL Queries in Procedures
⭐ IN, OUT, INOUT Parameters
⭐ Compare Procedures and Functions
⭐ READONLY Parameters
⭐ Dynamic Data Insertions with Procedures
⭐ Table Variables, Cloning & Data Inserts
⭐ Using TEMP tables in procedures
⭐ Stored Procedure inside Stored Procedure
⭐ Optimizing tips for procedure
⭐ Local variables vs Global variables with examples
⭐ Local variables Vs. Temp variables and real time usage
⭐ TEMPORARY table usages in real time
⭐ Inline View Vs. Normal View
⭐ CTE: Common Table Expressions
⭐ CTE usage in real time
⭐ Multiple examples using CTE
⭐ ROW_NUMBER () with CTE Queries
⭐ Recursive CTE
⭐ IF, IIF, CASE
⭐ Error Handling in T-SQL
⭐ WHILE, WHEN
⭐ Try, Catch, Throw
⭐ Creating Dynamic SELECT statement
⭐ Passing dynamic table names
⭐ Create a procedure with dynamic table names and variables
⭐ Normal SQL Vs. Dynamic SQL
Tableau
⭐ Introduction to Tableau
⭐ Tableau Products & Architecture
⭐ Tableau Installation & Overview
⭐ Tableau Interface & Workflow
⭐ Data Connections (Excel, CSV, SQL Server)
⭐ Tableau Data Types
⭐Dimensions & Measures
⭐Sorting & Filtering
⭐Groups & Sets
⭐Sets
⭐Hierarchies
⭐ Calculated Fields
⭐ Number Functions
⭐ String Functions
⭐ Date Functions
⭐ Logical Functions
⭐ Aggregate Functions
⭐ Bar Chart
⭐ Line Chart
⭐ Pie Chart
⭐ Area Chart
⭐ Scatter Plot
⭐ Tree Map
⭐ Heat Map
⭐ Histogram
⭐ Box Plot
⭐ Packed Bubble Chart
⭐ Dual Axis Chart
⭐ Table Calculations
⭐ Trend Lines
⭐ Dashboard Creation
⭐ Story Creation
⭐ Dashboard Actions And Formatting
⭐ Dual Axis Chart
⭐ Table Calculations
⭐ Trend Lines
⭐ Dashboard Creation
⭐ Story Creation
⭐ Dashboard Actions
⭐ Dashboard Formatting
Statistics
⭐ Introduction to Statistics
⭐ Types of Data
⭐ Types of Variables
⭐ Levels of Measurement
⭐ Data Collection Methods
⭐ Measures of Central Tendency
⭐ Measures of Dispersion
⭐ Percentiles & Quartiles
⭐ Data Distribution
⭐ Skewness & Kurtosis
⭐ Outlier Detection
⭐ Basics of Probability
⭐ Probability Distributions
⭐ Sampling & Sampling Distribution
⭐ Confidence Intervals
⭐ Hypothesis Testing
⭐ Type I & Type II Errors
⭐ Correlation Analysis
⭐ Trend Analysis
⭐ Statistical Insights for Business
⭐ KPI Analysis
⭐ Data Interpretation
⭐ Real-Time Business Case Studies
Gen AI
⭐ Introduction to Generative AI
⭐ AI vs Machine Learning vs Generative AI
⭐ Introduction to Large Language Models (LLMs)
⭐ OpenAI & Open-Source Models
⭐ Popular AI Tools (ChatGPT, Microsoft Copilot, Gemini, Claude)
⭐ Introduction to Prompt Engineering
⭐ Types of Prompts
⭐ Effective Prompt Writing Techniques
⭐ Prompt Templates for Data Analytics
⭐ Best Practices for Prompt Engineering
⭐ AI for Microsoft Excel
⭐ AI for SQL Query Generation
⭐ AI for Python Code Generation
⭐ AI for Power BI & DAX
⭐ AI for Data Cleaning & Transformation
⭐ AI for Data Visualization
⭐ AI for Dashboard & Report Generation
⭐ AI for Business Insights & Decision Making
Advantages of Learning Data Analytics Training in Hyderabad
Learning Data Analytics Training in Hyderabad can help students, freshers, working professionals, and career switchers develop practical skills for working with business data. Hyderabad is a major technology and business hub, making data-related skills valuable across IT, finance, healthcare, e-commerce, retail, marketing, and other industries. Through practical training, learners can develop skills in Excel, SQL, Power BI, Tableau, Python, Power Query, Statistics, Data Visualization, and Generative AI. Training with real-world datasets and business scenarios helps students understand how to clean, transform, analyze, and visualize data effectively. Learners can also build interactive dashboards, write SQL queries, perform data analysis using Python, and present meaningful business insights. Project-based learning provides an opportunity to create portfolio projects that demonstrate practical knowledge. Training can also include interview preparation, resume guidance, technical question practice, and business case studies. Learning in Hyderabad provides access to classroom and online training options, depending on the learner’s requirements. By developing technical, analytical, problem-solving, and communication skills, learners can prepare for roles such as Data Analyst, BI Analyst, Reporting Analyst, MIS Analyst, and Junior Data Analyst. With continuous practice and project experience, Data Analytics Training can provide a structured path for building a strong foundation in modern data analytics.
Why Choose Data Analytics Training in Hyderabad?
Hyderabad is an important technology and business hub with opportunities across IT, financial services, healthcare, e-commerce and other sectors.
For learners in Hyderabad, practical classroom training can provide direct interaction with trainers, while online training can provide flexibility for working professionals and learners outside the city.
Choose the learning format that fits your schedule.
Classroom | Live Online | Weekend | Fast Track
Suitable for Beginners
You don’t need to know everything before starting.
Start with:
Excel
↓
SQL
↓
Power BI
↓
Python
↓
Tableau
↓
GenAI
↓
Projects
↓
Interview Preparation
You can progressively build your skills instead of trying to learn everything at once.
Industry-Ready Data Analytics Training in Hyderabad
Build practical and industry-relevant skills with our Data Analytics Training in Hyderabad, designed for students, freshers, working professionals, and career switchers. Learn the complete Data Analytics workflow, from fundamentals to advanced concepts, including Excel, SQL, Power Query, Power BI, Tableau, Python, Statistics, Data Visualization, and Generative AI. Gain hands-on experience by working with real-world datasets, business scenarios, dashboards, and industry-based projects. Our trainer-led sessions focus on practical learning, analytical thinking, data cleaning, data transformation, business intelligence, and insight generation. Learners also receive guidance for resume building, portfolio development, technical interview preparation, mock interviews, and relevant job opportunities. With flexible learning options, practical exercises, project-based training, learning resources, and continued doubt support, you can build a strong foundation for a career in Data Analytics. Whether you are looking for Data Analytics Classes in Hyderabad, Data Analyst Training in Hyderabad, or a practical Data Analytics Institute in Hyderabad, our structured training approach helps you develop the technical and business skills needed to work confidently with data.
Frequently Asked Questions
Yes. Visitors can contact the institute to check the availability of the next FREE Demo Class.
Data Analytics Training in Hyderabad
Get certified by VinayTech
Upon successful completion of the course, you will receive a Vinay Tech Course Completion Certificate. This certification enhances your resume and can significantly boost your chances of securing top job roles in leading multinational companies (MNCs). The certificate is available both as a digital copy and a printable hard copy, providing flexibility based on your needs.
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