Do I need to know coding to join the Data Analysis course?

No. The course starts from zero - your first SELECT statement is written in week one, and Power BI is taught click by click. Basic computer literacy and comfort with numbers are enough.

What is the Data Analysis course fee at RST Forum?

The course fee is ₹30,000. There are no hidden charges - lab access and course material are included.

How long is the course?

100 hours of live instructor-led training (lectures + hands-on lab practice), across weekday-evening or weekend batches.

Is the training online or classroom?

Both. Classroom batches run at our Mumbai campus; the same batch streams live for online students across India. Both get identical lab access.

How deep does the SQL half go?

Well past the basics: SELECT, filtering and joins first, then aggregate and window functions (ROW_NUMBER, RANK, LAG, LEAD), subqueries, CTEs including recursive CTEs, views, temp tables, stored procedures and triggers - with assessment tests along the way. You leave able to do a full analysis in SQL alone.

What does the Power BI half cover?

The complete Power BI Desktop workflow: connecting and shaping data with Power Query, building a proper star-schema data model, writing DAX (calculated columns, measures, CALCULATE, time intelligence), and designing interactive reports with slicers, drill-through, bookmarks and AI visuals.

Does this course prepare me for the PL-300 (Microsoft Power BI Data Analyst) exam?

The Power BI half covers the core of what PL-300 tests - Power Query, data modelling, DAX and report design. If you want the Microsoft credential, you finish this course well placed to prepare for the exam.

Do I get a certificate?

Yes - an RST Forum certificate of completion, plus the portfolio of SQL analyses and dashboards you build during the course, which is what analyst interviews actually weigh.

How is this different from a data science course?

Data analysis answers business questions with existing data - SQL, dashboards, storytelling. Data science builds predictive models on top. This course makes you a job-ready analyst first; if you later want the modelling side, our Artificial Intelligence (ML, DL, RAG, Agentic) Engineering course continues the arc.

What comes after this course?

Most students add Python next - it is the bridge from analysis into automation and modelling - and then continue into our AI Engineering track. Some go deeper on the BI side and take Microsoft's PL-300 certification exam.

Data

Data Analysis with SQL, Power BI, Advanced Excel and Tableau

This 100hrs (Lectures + hands-on Lab) Data Analysis training covers the four tools that appear together in almost every data analyst job description: SQL, Microsoft Power BI, Advanced Excel and Tableau. It is built as one continuous path rather than four separate courses. You start by querying a relational database directly in SQL - joins, window functions, CTEs, stored procedures and the indexing that keeps a query fast - because every reliable report begins with getting the right rows out. From there the same data is taken into Power Query and Power BI, modeled as a star schema, measured in DAX and published as an interactive report with row-level security. Advanced Excel then covers the ground analysts actually spend their days on: Tables, data validation, cleaning imported data, PivotTables and the Data Model, lookups and dynamic arrays, and enough VBA to automate the work that repeats. The course closes in Tableau, where the same modeling and calculation ideas are applied again - relationships and blends, level of detail expressions, table calculations - and finish in a published dashboard built from a brief. Every session pairs the concept with time at the keyboard, so each topic is worked on a real dataset before the next one begins. It suits graduates moving into analytics, working professionals in finance, operations, marketing or IT who already live in spreadsheets, and developers and support engineers who want to move towards data roles. No prior SQL or BI experience is assumed; comfort with a computer and basic arithmetic is enough to start.

Duration: 100 hours
Training mode: Classroom & online
Course fees: ₹30,000
Level: Beginner

Course details verified 14 Oct 2026

Please note that the course fees exclude the certification cost.

Overview

Who should attend

Anyone moving into data roles: graduates and career-switchers aiming for their first analyst job, MIS and operations people who already live in spreadsheets and want SQL and Power BI on top, and working professionals who need to make decisions with data. No coding or database background is required - the course starts from zero. SQL and Power BI are the two tools Indian analyst job postings name most often - this course teaches exactly those two, to working depth, on real business datasets.

Roles this leads to

Typical India salary

Data Analyst₹4–9 LPA
Business Analyst₹5–12 LPA
MIS / Reporting Analyst₹3.5–7 LPA
BI Developer (Power BI)₹6–12 LPA

Outcomes

Skills you'll gain

SQL querying (SELECT, filtering, joins)
Window functions & CTEs
Database objects: tables, views, procedures
Power Query (connecting & shaping data)
Data modelling & star schemas
DAX calculated columns & measures
Interactive dashboards & reports
Data storytelling

Objectives

What you'll be able to do

The course objectives, as the vendor's own blueprint states them.

Write SQL that reads: SELECT, filtering, grouping and sorting against a live database.
Combine tables correctly with inner, outer, cross and self joins, and with set operators.
Create and change tables and data safely with DDL, DML and transactions.
Use string, date, NULL and conditional functions to clean data in the query itself.
Answer ranking, running-total and period-on-period questions with window functions.
Structure complex logic with subqueries, common table expressions and views.
Write stored procedures, and read a query plan well enough to make a slow query fast.
Build a layered data warehouse and run an exploratory analysis against it.
Connect Power BI to databases, files, folders and the web, and shape the data in Power Query.
Design a star schema, set cardinality and filter direction, and handle many-to-many relationships.
Write DAX measures, including CALCULATE, filter context and time intelligence over a proper date table.
Build and publish Power BI reports with slicers, drill-through, bookmarks and row-level security.
Use AI visuals, Performance Analyzer and the optimization tools to keep a report fast.
Work fluently in Excel with Tables, structured references, validation and Power Query.
Build PivotTables, dashboards and Power Pivot models, and know when to relate rather than flatten.
Choose correctly between VLOOKUP, INDEX + MATCH and XLOOKUP, and handle every Excel error type.
Use dynamic arrays, formula auditing, protection, Goal Seek and Solver.
Automate repeated Excel work with the macro recorder and VBA, with proper error handling.
Model data in Tableau with joins, unions, relationships and blends, and know which to use when.
Write Tableau row-level, aggregate, level of detail and table calculations.
Pick the right chart for the question and remove everything that is not data.
Build an interactive Tableau dashboard with actions, parameters and device layouts, and publish it.

Included

What you get

Curriculum built from the current vendor syllabi for SQL, Power BI, Power Query, Excel and Tableau.
This instructor-led classroom course is designed with an aim to build theoretical knowledge supplemented by ample hands-on lab exercises.
Every topic is practiced on real data sets, not slides.
Facility of Lab on cloud available.
Courseware includes reference material to maximize learning.
Assignments and tests to ensure concept absorption.
Repeating of lectures allowed (On approval basis)
Candidates can attend lectures online.

Credential

Certification details

You earn an RST Forum certificate of completion - and a portfolio of SQL analyses and interactive dashboards built on real datasets, which is what analyst interviews actually weigh.

The Power BI half of the course covers the core of what Microsoft's PL-300 (Power BI Data Analyst) certification tests - Power Query, data modelling, DAX and report design - so you finish well placed to prepare for that exam if you want the credential.

After the course

Placements & outcomes

Placement assistance is included: CV preparation, interview drills with real scenario questions, and referrals through our alumni network of 1 million+ engineers trained since 1997.

Learning path

Where this course takes you

This course is one stage of the Data Analysis path - 6–9 months end to end, beginner → professional. Here is what comes before it, and what comes after.

Data Analysis with SQL, Power BI, Advanced Excel and TableauYou are hereData Analyst · 2–3 months
Python ProgrammingAnalytics Engineer · 2–3 months
Data EngineeringData Engineer · 2–3 months

Next up: Python Programming - The analyst who automates their reports stops doing them by hand - and starts doing analysis.

See the full Data Analysis path

29 modules

Full syllabus

Every module below is taught live and practiced hands-on - nothing here is a slide-only topic.

SQL - Databases, Tables and the SELECT Statement

Relational databases, tables, rows, columns and keys.
Where SQL sits in a data stack, and which dialects differ.
Installing and connecting to a database server.
The anatomy of a SELECT statement and the order the engine runs it in.
Choosing columns, aliasing, DISTINCT and TOP / LIMIT.
Sorting results with ORDER BY on one and many columns.
Reading an execution message and finding your own syntax errors.

SQL - Filtering, Sorting and Aggregation

Comparison, logical and range operators: AND, OR, NOT, BETWEEN, IN.
Pattern matching with LIKE and wildcards.
Aggregate functions: COUNT, SUM, AVG, MIN, MAX.
Grouping with GROUP BY and filtering groups with HAVING.
WHERE versus HAVING, and why the difference matters.
Counting rows versus counting values, and where NULL changes the answer.

SQL - Defining and Changing Data (DDL and DML)

CREATE, ALTER and DROP on tables.
Data types, constraints, primary keys and foreign keys.
INSERT, UPDATE and DELETE.
TRUNCATE versus DELETE.
Transactions, COMMIT and ROLLBACK, and why an UPDATE without a WHERE is a story everyone tells once.

SQL - Combining Tables

INNER JOIN, LEFT, RIGHT and FULL OUTER JOIN.
CROSS JOIN and self joins.
Anti joins: finding the rows that are not there.
Joining three or more tables, and controlling the order.
UNION and UNION ALL, INTERSECT and EXCEPT.
Choosing between a join and a set operator.

SQL - Built-in Functions

String functions: CONCAT, UPPER, LOWER, TRIM, LEN, LEFT, RIGHT, SUBSTRING, REPLACE.
Date and time functions: current date, extracting parts, date arithmetic, formatting and casting.
Handling NULL with ISNULL, COALESCE and NULLIF.
Conditional logic with CASE WHEN, including inside aggregates.
Casting and converting between types.

SQL - Window Functions

The OVER clause, PARTITION BY and ORDER BY.
Window functions versus GROUP BY aggregation.
Window aggregates: running totals, moving averages and part-to-whole.
Ranking: ROW_NUMBER, RANK, DENSE_RANK and NTILE.
Value functions: LAG, LEAD, FIRST_VALUE and LAST_VALUE.
Frame clauses: ROWS and RANGE.
De-duplicating rows and period-on-period comparisons.

SQL - Subqueries, CTEs and Views

Scalar, multi-row and correlated subqueries.
Subqueries in SELECT, FROM, WHERE and HAVING.
EXISTS and NOT EXISTS.
Common table expressions, and chaining several together.
Recursive CTEs.
Creating, altering and dropping views, and what a view does and does not store.

SQL - Stored Procedures, Indexes and Performance

CTAS and temporary tables.
Writing stored procedures with parameters and control flow.
Clustered and non-clustered indexes, and how an index is used.
Covering indexes, index fragmentation and maintenance.
Table partitioning.
Reading a query plan, and the habits that keep queries fast.

SQL - Analytics Projects

Building a layered data warehouse: bronze, silver and gold.
Exploratory data analysis against a live schema.
Advanced analytics: trends, cumulative measures, segmentation and part-to-whole.
Preparing for a SQL technical interview.

Power BI - Power BI Desktop and the BI Workflow

The Power BI product family: Desktop, Service, Mobile and Report Server.
Front end versus back end, and the four-step workflow every report follows.
Touring the Power BI Desktop interface.
Report, Data and Model views.

Power BI - Connecting and Shaping Data with Power Query

Connector types, and when each one is appropriate.
The Power Query Editor and Applied Steps.
Import, DirectQuery and Live Connection storage modes.
Connecting to databases, files, folders and the web.
Basic table transformations: promote headers, remove columns, filter rows, split and merge.
Data quality and profiling tools.
Text, numerical and date tools.
Rolling calendar tables and change type with locale.
Index and conditional columns.
Grouping, aggregating, pivoting and unpivoting.
Appending and merging queries, and merge join types.
Query dependencies, references and duplicates.
Refresh, parameters and the M language.

Power BI - Building the Data Model

Why a data model beats one flat table.
Star schema: facts and dimensions.
Relationships, cardinality and cross-filter direction.
Primary and foreign keys in a Power BI model.
Active versus inactive relationships.
Snowflake schemas and bridge tables.
Managing many-to-many relationships.
Hierarchies, sort-by-column and data categories.
Formatting, display folders and hiding fields from report view.

Power BI - Calculated Fields with DAX

Calculated columns versus measures, and when each is evaluated.
Row context and filter context.
DAX syntax, operators and comments.
Aggregation functions and iterators: SUM versus SUMX.
CALCULATE, and modifying filter context.
FILTER, ALL, ALLSELECTED and REMOVEFILTERS.
Logical and conditional functions: IF, SWITCH, AND, OR, DIVIDE.
Time intelligence: year-to-date, prior period, running totals and a proper date table.
RELATED and RELATEDTABLE across the model.
Variables with VAR, and writing DAX that can be read later.

Power BI - Reports, Visuals and Dashboards

Choosing a visual from the question being asked.
Bar, column, line, area and combination charts.
Tables, matrices and conditional formatting.
Cards, KPIs, gauges and multi-row cards.
Maps and geospatial visuals.
Scatter, treemap, funnel and waterfall charts.
Slicers, filters, and the filter pane hierarchy.
Drill down, drill through, tooltips and bookmarks.
Buttons, selections and report navigation.
Themes, layout, alignment and mobile layout.
Publishing to the Power BI Service, workspaces and sharing.
Row-level security.

Power BI - AI Visuals, Optimization and What's New

Q&A, key influencers, decomposition tree and smart narrative.
Anomaly detection and forecasting.
Performance Analyzer and the optimization ribbon.
Model size, data reduction and refresh performance.
Recent additions to Power BI Desktop.

Advanced Excel - Foundations

What Excel really is, and the four levels of problem it solves.
The eight regions of the window, and the two that do the work.
Formula, value and display: the three things in every cell.
Calculation mode, and the errors it causes.
The shortcuts that are worth more than the ribbon.

Advanced Excel - Core Formulas and Formatting

The four data types, and how alignment reveals them.
Every date is a number, and why imported dates arrive as text.
Number formats change the display, never the value.
Operator precedence, and debugging with F9 and Evaluate Formula.
Relative, absolute and mixed references.
Function anatomy, nesting and the Function Arguments dialog.
The core five functions and the counting trap.
Fill, series and custom lists.
Conditional formatting, including formula rules and rule order.
Charts that answer the question, and print setup that does not waste paper.

Advanced Excel - Data, Tables and Power Query

The one data shape every Excel feature requires.
Flat data versus a report, and seven rules for a usable list.
Excel Tables, structured references and calculated columns.
Sort, filter, custom lists and Subtotal.
Data validation, error alerts and dependent dropdowns.
Cleaning imported data: Text to Columns, Remove Duplicates, Paste Special, Go To Special.
Why two identical-looking values compare unequal.
The Power Query Editor, Applied Steps and the M language.
Append and merge, refresh, and moving the source folder.

Advanced Excel - PivotTables, Dashboards and the Data Model

PivotTables with no formulas: the four areas.
Value Field Settings, and "Count" where you expected "Sum".
The pivot cache, drill-down and building on a Table.
Grouping, slicers, timelines and PivotCharts.
Turning a PivotTable into a dashboard that stays usable.
The star schema and what the Data Model actually is.
Power Pivot and DAX measures in Excel.
Working across many sheets, Data Consolidate, and why one sheet per month is a design problem.
Large workbooks: what makes them slow, and what does not.

Advanced Excel - Logic, Lookups and Dynamic Arrays

Defined names, and formulas that read like sentences.
IF, AND, OR, NOT, IFS and SWITCH, and when a lookup table beats all of them.
Conditional aggregation: SUMIFS, COUNTIFS, AVERAGEIFS and wildcards.
VLOOKUP anatomy and its four failure modes.
INDEX + MATCH, and XLOOKUP's six arguments.
Exact versus approximate match.
The seven error values, IFERROR versus IFNA.
Text functions: FIND, SEARCH, SUBSTITUTE, REPLACE, TEXT and VALUE.
Date maths, DATEDIF, working days and holidays.
Dynamic arrays, spilling, the # operator and LET.
Formula auditing, worksheet protection, Goal Seek and Solver.

Advanced Excel - Macros and VBA

When a procedure beats a formula, and when you do not need VBA at all.
The macro recorder, absolute versus relative recording, and running a macro.
The Excel object model, and references that do not break.
Finding the last row and reading cells properly.
Option Explicit, data types, Dim and Set, and scope.
Decisions and loops, and the two bugs every beginner writes.
MsgBox, InputBox and validating what users type.
Error handling with On Error GoTo, and the statement that hides every bug.
Stepping through code, breakpoints and the Immediate window.

Tableau - Why Visual Analytics

From raw data to insight, and what each buzzword actually refers to.
Volume, velocity and variety.
Why the brain reads a picture faster than a table.
The four things a spreadsheet cannot do.
Where Tableau came from and where it sits in the market.

Tableau - The Product Suite and Architecture

Desktop, Public, Prep, Server, Online and Reader.
The four steps every Tableau project follows.
Where a workbook lives, and the three hosting and licensing models.
Workbook and data source file types, and what the x in twbx and tdsx means.
Live connections versus extracts: freshness or speed.
The anatomy of a data source.
What runs on Tableau Server, and how a workbook reaches a browser.
The Tableau Desktop workspace: shelves, cards, marks and the three drop targets.

Tableau - Data Modeling

The logical and physical layers.
Joins: inner, left, right and full outer.
Unions, and when to stack rather than join.
Relationships, and why they are the default.
Data blending, the only cross-source method.
Choosing between join, union, relationship and blend.

Tableau - Metadata, Fields and Organization

Data types, and dimension versus measure.
Discrete versus continuous: the blue pill and the green pill.
Roles, geographic roles and advanced types.
Renaming, aliases, default properties and comments.
Groups, sets, hierarchies, bins and combined fields.
Folders, and where each object actually lives.

Tableau - Filters and Parameters

The six filter types and the fixed order they execute in.
Extract, data source, context, dimension, measure and table calculation filters.
Filter scope, showing values, and practical filter tips.
Parameters as user-controlled variables.
The four things parameters are really used for: swapping measures, top N, what-if and thresholds.

Tableau - Calculations

The four calculation types, and where each one runs.
Row-level functions: string, number, date, logical and type conversion.
IF, ELSEIF, ELSE and the NULL a bare IF leaves behind.
CASE for one field with many values.
AND, OR and NOT, row by row.
Aggregate calculations and the viz level of detail.
Level of detail expressions: FIXED, INCLUDE and EXCLUDE.
Nested aggregation, and aggregating an aggregation.
Table calculations: running total, difference, percent difference, rank and window functions.
Addressing and partitioning in a table calculation.

Tableau - Charts and Visual Design

Starting from the question, not the chart type.
Showing two or more measures: dual axis, combined axis, shared axis and small multiples.
Magnitude and change over time.
Bullet, Pareto, box plot and waterfall charts.
Proportion, spread and distribution.
Maps, symbol maps and filled maps.
Removing everything that is not data.
Color, labels, tooltips, annotations and formatting.

Tableau - Dashboards and the Final Project

Dashboard objects, tiled and floating layout, and containers first.
Device layouts and sizing.
Dashboard actions: filter, highlight, URL, parameter and set actions.
Legends, filters and showing or hiding a container.
Stories and story points.
Publishing to Tableau Public and Tableau Server.
The final project: building a complete dashboard sheet by sheet from a brief.

206 labs

Hands-on labs

Structured labs on real equipment, with 24/7 lab-on-cloud access through Skillopia - our own lab platform.

SQL

Environment Setup and Database Configuration

Retrieving Data with SELECT and FROM

Filtering Data with WHERE Clause

Sorting Results with ORDER BY

Aggregating Data with GROUP BY

Filtering Aggregated Data with HAVING

Removing Duplicates with DISTINCT

Limiting Results with TOP

Creating Tables with CREATE TABLE

Modifying Tables with ALTER TABLE

Removing Tables with DROP TABLE

Inserting Data with INSERT INTO

Updating Data with UPDATE

Deleting Data with DELETE and TRUNCATE

Logical Operators (AND, OR, NOT)

Range & Membership Operators (BETWEEN, IN/NOT IN)

Pattern Matching with LIKE

Introduction to Multiple Tables - Querying Without Joins

INNER JOIN - Matching Data Between Tables

LEFT JOIN - All Rows from Left Table

RIGHT JOIN & FULL JOIN - Other Outer Joins

Anti Joins (LEFT ANTI, RIGHT ANTI, FULL ANTI)

CROSS JOIN - Cartesian Products

JOIN Decision Tree & Choosing the Right Join

Multiple Table Joins - Real-World Analytical Approach

SQL Set Operators - Syntax, Rules, and Fundamentals

SQL Set Operators - UNION and UNION ALL

SQL Set Operators - INTERSECT and EXCEPT

SQL Set Operators - Real-World Use Cases and Best Practices

SQL Set Operators - Advanced Use Cases for Data Engineering

Introduction to SQL Functions

SQL String Functions - Part 1 (CONCAT, UPPER, LOWER, TRIM)

SQL String Functions - Part 2 (REPLACE, LEN, LEFT, RIGHT)

SQL String Functions - Part 3 (SUBSTRING, CHARINDEX)

SQL String Functions - Part 4 (Advanced Combinations)

SQL Numeric Functions - ROUND() and ABS()

SQL Numeric Functions - Advanced (FLOOR, CEILING, POWER, SQRT)

SQL Date & Time Data Types - Fundamentals

Date Part Extraction I - YEAR(), MONTH(), DAY(), DATEPART()

Date Part Extraction II - DATENAME(), DATETRUNC(), EOMONTH()

Date Arithmetic - DATEADD() and DATEDIFF()

Date Validation - ISDATE(), TRY_CAST(), TRY_CONVERT()

Understanding NULL Values in SQL

ISNULL() Function - Replace NULL with Static Values

COALESCE() Function - Return First Non-NULL Value

NULLIF() Function - Convert Specific Values TO NULL

IS NULL / IS NOT NULL - Check for NULLs and Find Unmatched Records

Anti-Joins with NULL - Finding Unmatched Records

CASE Statement Fundamentals - Basic Syntax and Conditions

CASE Essentials - Multiple Conditions and ELSE Clause

CASE for Data Categorization - Creating Business Categories

CASE with Custom Sorting - ORDER BY with Conditional Logic

CASE with GROUP BY - Segmentation and Aggregation

CASE for NULL Handling - Replacement and Management

CASE for Conditional Aggregation - Advanced Techniques

CASE Statement Mastery - Complex Real-World Scenarios

Window Functions Fundamentals - Introduction and OVER()

PARTITION BY - Defining Windows

ORDER BY and Running Totals

Ranking Functions - ROW_NUMBER, RANK, DENSE_RANK

Window Frames - ROWS BETWEEN and Frame Specifications

Window Functions Integration and Mastery

Window Aggregate Functions - COUNT()

Window Aggregate Functions - SUM()

Window Aggregate Functions - AVG()

Window Aggregate Functions - MIN() and MAX()

Window Ranking Functions Introduction - ROW_NUMBER()

RANK() and DENSE_RANK() - Tie Handling

ROW_NUMBER() Real-World Use Cases

NTILE() - Bucket/Group Segmentation

CUME_DIST() and PERCENT_RANK() - Percentage-Based Ranking

Window Ranking Functions Integration

Introduction to Common Table Expressions (CTEs)

Standalone CTEs - Single and Multiple Independent CTEs

Nested CTEs - Chaining CTEs Together

Recursive CTEs - Self-Referencing Queries

Introduction to Views

Creating, Querying, and Managing Views

Views vs CTEs vs Tables

Views for Simplification and Abstraction

Views for Security

Advanced View Patterns

Introduction to SQL Data Warehouse Project

Designing Data Warehouse Architecture

Project Initialization - Database and Schemas

Building the Bronze Layer

Building the Silver Layer - Data Cleansing

Building the Silver Layer - Advanced Transformations

Building the Gold Layer - Star Schema

Microsoft Power BI

Power BI Environment Setup and Interface Overview

Connecting to Data Sources - CSV Files

Basic Table Transformations

Data Profiling and Quality Assurance

Text Transformations

Number and Statistics Transformations

Connecting to Folder Data Sources

Connecting to SQL Server Databases

Connecting to Web Data Sources

Data Source Settings and Parameters

100

Date Tools and Creating Calendar Tables

101

Handling Date Locale Errors

102

Creating Rolling Calendars with M Code

103

Index Columns and Conditional Columns

104

Grouping and Aggregating Data

105

Pivoting and Unpivoting Data

106

Merging Queries

107

Appending Queries

108

Introduction to DAX and the Data Model

109

Calculated Columns in DAX

110

DAX Measures - Implicit vs Explicit

111

Quick Measures and Best Practices

112

Creating a Dedicated Measure Table

113

Understanding Filter Context

114

DAX Fundamentals - Context, Syntax & Operators

115

Math, Stats & Counting Functions

116

Logical Functions & SWITCH

117

Relationships and Cardinality in the Data Model

118

Star Schema Design - Facts and Dimensions

119

Cross-Filter Direction and Bi-Directional Filtering

120

Active and Inactive Relationships with USERELATIONSHIP

121

Resolving Many-to-Many with a Bridge Table

122

Hierarchies, Sort-by-Column and Data Categories

123

Iterator Functions - SUMX, AVERAGEX and RANKX

124

CALCULATE - Modifying Filter Context

125

Filter Functions - FILTER, ALL, ALLSELECTED and REMOVEFILTERS

126

Relationship Functions - RELATED and RELATEDTABLE

127

DAX Variables with VAR and RETURN

128

Building a Date Table for Time Intelligence

129

Time Intelligence - Year-to-Date and Prior Period

130

Time Intelligence - Running Totals and Moving Averages

131

Report Canvas Basics - Adding and Formatting Visuals

132

Bar, Column and Line Charts

133

Combination Charts and Dual Axes

134

Tables and Matrices with Conditional Formatting

135

Cards, KPIs, Gauges and Multi-Row Cards

136

Map Visuals and Geographic Data

137

Scatter and Bubble Charts with a Play Axis

138

Treemap, Funnel and Waterfall Charts

139

Slicers and Slicer Synchronization

140

The Filter Pane - Visual, Page and Report Filters

141

Drill Down, Drill Through and Report Page Tooltips

142

Bookmarks, Selections, Buttons and Report Navigation

143

Themes, Page Layout and the Mobile Layout

144

Q&A and Smart Narrative Visuals

145

Key Influencers and the Decomposition Tree

146

Anomaly Detection and Forecasting

147

Performance Analyzer and Report Optimization

148

Publishing to the Power BI Service

149

Workspaces, Apps and Sharing

150

Scheduled Refresh and Data Gateways

151

Row-Level Security - Roles and Rules

152

Final Project: an End-to-End Power BI Report

Advanced Excel

153

The Grid, the Formula Bar and the Eight Regions

154

Data Types, Dates and Number Formats

155

Operators, Precedence and References

156

Functions: the Core Five and the Fill Tools

157

Conditional Formatting, Charts and Printing

158

Flat Data and Excel Tables

159

Sort, Filter and Subtotal

160

Data Validation

161

Cleaning Imported Data

162

Power Query: Append and Merge

163

PivotTables and Dashboards

164

The Data Model and Big Workbooks

165

Names and Logic

166

Conditional Aggregation

167

Lookups and Errors

168

Text and Date Functions

169

Dynamic Arrays, Auditing, Protection and What-If

170

The Macro Recorder and the Object Model

171

VBA Variables, Decisions and Loops

172

VBA: Interaction, Errors and Debugging

173

Final Project: a Refreshable Monthly Sales Report

Tableau

174

Installing Tableau Desktop and Touring the Workspace

175

Connecting to Files, Databases and Saved Data Sources

176

Creating and Refreshing an Extract

177

Choosing Between a Live Connection and an Extract

178

Building a First View with Rows, Columns and Marks

179

Joining Tables in the Physical Layer

180

Unioning Tables to Add Rows

181

Building a Relationship in the Logical Layer

182

Blending Two Data Sources

183

Choosing Between Join, Union, Relationship and Blend

184

Setting Data Types, Roles and Geographic Roles

185

Working with Discrete and Continuous Fields

186

Renaming Fields and Setting Default Properties

187

Creating Groups, Sets, Hierarchies and Bins

188

Applying the Six Filter Types in the Correct Order

189

Building Context Filters and Top N Filters

190

Creating Parameters for Measure Swapping and What-If

191

Writing Row-Level Calculations

192

Writing Logical Calculations with IF and CASE

193

Writing Aggregate Calculations

194

Writing FIXED, INCLUDE and EXCLUDE Level of Detail Expressions

195

Writing Table Calculations: Running Total and Difference

196

Controlling Addressing and Partitioning in a Table Calculation

197

Building Dual-Axis and Combined-Axis Charts

198

Building Bullet, Pareto, Box Plot and Waterfall Charts

199

Building Symbol Maps and Filled Maps

200

Formatting a View: Color, Labels, Tooltips and Annotations

201

Laying Out a Dashboard with Containers

202

Wiring Filter, Highlight and Parameter Actions

203

Building Device Layouts for Desktop, Tablet and Phone

204

Building a Story with Story Points

205

Publishing to Tableau Public

206

Final Project: a Complete Sales Dashboard from a Brief

Answers

Frequently asked questions

Do I need to know coding to join the Data Analysis course?: No. The course starts from zero - your first SELECT statement is written in week one, and Power BI is taught click by click. Basic computer literacy and comfort with numbers are enough.
What is the Data Analysis course fee at RST Forum?: The course fee is ₹30,000. There are no hidden charges - lab access and course material are included.
How long is the course?: 100 hours of live instructor-led training (lectures + hands-on lab practice), across weekday-evening or weekend batches.
Is the training online or classroom?: Both. Classroom batches run at our Mumbai campus; the same batch streams live for online students across India. Both get identical lab access.
How deep does the SQL half go?: Well past the basics: SELECT, filtering and joins first, then aggregate and window functions (ROW_NUMBER, RANK, LAG, LEAD), subqueries, CTEs including recursive CTEs, views, temp tables, stored procedures and triggers - with assessment tests along the way. You leave able to do a full analysis in SQL alone.
What does the Power BI half cover?: The complete Power BI Desktop workflow: connecting and shaping data with Power Query, building a proper star-schema data model, writing DAX (calculated columns, measures, CALCULATE, time intelligence), and designing interactive reports with slicers, drill-through, bookmarks and AI visuals.
Does this course prepare me for the PL-300 (Microsoft Power BI Data Analyst) exam?: The Power BI half covers the core of what PL-300 tests - Power Query, data modelling, DAX and report design. If you want the Microsoft credential, you finish this course well placed to prepare for the exam.
Do I get a certificate?: Yes - an RST Forum certificate of completion, plus the portfolio of SQL analyses and dashboards you build during the course, which is what analyst interviews actually weigh.
How is this different from a data science course?: Data analysis answers business questions with existing data - SQL, dashboards, storytelling. Data science builds predictive models on top. This course makes you a job-ready analyst first; if you later want the modelling side, our Artificial Intelligence (ML, DL, RAG, Agentic) Engineering course continues the arc.
Do you provide placement assistance?: Yes. CV preparation, mock interviews with real scenario questions, and referrals through our alumni network of 1 million+ engineers trained since 1997.
What comes after this course?: Most students add Python next - it is the bridge from analysis into automation and modelling - and then continue into our AI Engineering track. Some go deeper on the BI side and take Microsoft's PL-300 certification exam.

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