Sales analysis workbook
Deliverable: A multi-view analysis of retail data with correctly joined sources, a written note on the join grain, and three findings each stated with a caveat.
Course
Tableau
Tableau taught as analysis rather than chart production, with level of detail expressions and data structure given the weight they deserve — because that is where reports go wrong.
- Duration: 3 months
- Classroom · Online live
- Level: beginner to advanced
What you will be able to do
- Connect, join and blend data sources and know when each is appropriate
- Reshape data with pivots and unions before it reaches a visual
- Write calculated fields, table calculations and level of detail expressions
- Explain why a number differs between two visuals of the same data
- Design dashboards with a clear question and a deliberate visual hierarchy
- Use parameters, sets and actions for genuine interactivity
- Publish to Tableau Public or Server with a sensible refresh approach
Who this course is for
- Working professionals producing recurring reports
- Analysts, MIS staff and finance teams
- Commerce and management graduates entering reporting roles
- Comfort with Excel helps; no programming required
Syllabus
6 modules · 3 months
Module 1. Getting data in
- Connecting to files, databases and cloud sources
- Live connections versus extracts, and the trade-off
- Joins, unions and relationships, and choosing correctly
- Data blending and when it is the right tool
- Pivoting and cleaning before visualising
Module 2. Building views
- Dimensions and measures, discrete and continuous
- Marks, shelves and how a view is actually constructed
- Filters, and the order Tableau applies them
- Sorting, grouping, sets and hierarchies
- Choosing a chart that answers the question asked
Module 3. Calculations
- Calculated fields and row-level logic
- Aggregate calculations and the aggregation trap
- Table calculations and how they scope
- Level of detail expressions — FIXED, INCLUDE and EXCLUDE
- Diagnosing why two visuals of the same data disagree
Module 4. Interactivity
- Parameters and parameter actions
- Filter, highlight and set actions
- Dashboard actions and navigation
- Tooltips that add information rather than repeat it
Module 5. Dashboard design
- Starting from the question the reader is asking
- Layout, containers and consistent spacing
- Colour, contrast and accessibility
- Reducing clutter and knowing what to remove
- Designing for the device the report is read on
- Performance — why a workbook is slow and how to fix it
Module 6. Sharing and workflow
- Publishing to Tableau Public and to Server
- Extract refresh schedules
- Permissions and row-level security concepts
- Documenting a workbook for handover
- Version control habits for analysts
Tools and technologies you will use
- Tableau Desktop
- Tableau Public
- Tableau Prep (introduction)
- Excel and CSV sources
- SQL sources
Projects you will build
Level of detail exercise
Deliverable: A workbook answering four questions that cannot be solved without LOD expressions, with a written explanation of why each expression type was chosen.
Interactive dashboard
Deliverable: A single-screen dashboard with parameters and actions, built from a stated reader question, meeting contrast requirements and loading in under five seconds.
Published portfolio piece
Deliverable: A workbook published to Tableau Public with documentation explaining the data source, the assumptions made and the limitations of the analysis.
Where this course can take you
- Data Analyst
- Business Intelligence Analyst
- Tableau Developer
- MIS Analyst
- Reporting Analyst
Duration, modes and fees
- Duration
- 3 months
- Delivery modes
- Classroom · Online live
- Fees
- Share your details for the current fee
- Fees vary by batch and delivery mode. Share your details and an advisor will confirm the current fee.
Placement assistance
Every student gets placement assistance — that is what 100% placement assistance means. It is support for all, not a job for all. We do not promise a specific salary, a specific number of interviews, or placement at any named company, and you should be wary of anyone who does.
What is included
- A place in the monthly placement drive, held every third Saturday
- The readiness programme every second Saturday — mock interviews and preparation
- CV review against the specific roles you are targeting
- Portfolio review, so your project work is presented the way a reviewer will read it
- Access to the vacancy pool employers send directly to the Samyak network
- Guidance on which roles realistically fit your background and which do not
- A place in the next drive, with coaching, if you are not selected in this one
What is not included
- Any guarantee of a job, an interview, or a particular salary
- Placement at a named or partner company
- Applying to jobs on your behalf
- Support before you have completed the course and its project work
- Visa, relocation or overseas placement assistance
Where Tableau users plateau
Almost everyone can build a chart in Tableau within a week. The tool is genuinely good at making that easy.
The plateau comes later, and it always looks the same. Two visuals of the same data show different totals. A percentage does not add to a hundred. A filter removes more than expected. The user has no framework for explaining any of it, so they try things until the number looks right — which is how a wrong number ends up in a board pack.
The explanation is aggregation scope: what Tableau is computing over, and when. Table calculations scope to the view. Level of detail expressions escape it. Filters apply in a defined order that changes the result.
Module three exists for that, and it is the module that decides whether someone becomes an analyst or stays a chart builder.
Data structure before visuals
The second most common problem arrives before Tableau opens.
Joining two tables at different grains duplicates rows, and every measure built on top is quietly inflated. Nothing errors. The dashboard looks fine.
So we teach checking the row count after every join, and reshaping data — pivots, unions, cleaning — before it reaches a view. Analysts who habitually verify grain produce numbers that survive scrutiny; those who do not eventually get caught by someone in a meeting.
Design starts with the question
A dashboard is not a collection of the charts you were able to build. It is an answer to something a specific person needs to know.
We start every dashboard exercise by writing that question down, then judge the result against it. It is a small discipline that removes a surprising amount of clutter, because anything not serving the question becomes obviously removable.
The portfolio advantage
Tableau Public is free and doubles as a portfolio, which matters for a jobseeker in a way Power BI does not quite match.
Your fourth project is published there with documentation — data source, assumptions, limitations. A reviewer can open your work in a browser without installing anything, which removes a real barrier between you and being taken seriously.
One caution: Tableau Public is public. Use sample or genuinely open datasets, never anything belonging to an employer.
Questions
Tableau — frequently asked questions
Should I learn Tableau or Power BI?
Power BI appears in more Indian job postings, largely through Microsoft 365 adoption, so it is the safer default if you have no specific employer in mind. Tableau remains entrenched at particular multinationals, consulting firms and analytics-led product companies. The concepts transfer almost entirely, so the second tool takes a fortnight.
Do I need SQL for a Tableau role?
Most postings expect it, because the data usually lives in a database and shaping it before it reaches Tableau is both faster and how teams work. This course covers connecting to SQL sources; our SQL course covers writing it properly. The combination is what job descriptions actually ask for.
Is Tableau Public enough to practise on?
Yes, and it doubles as your portfolio, which is a genuine advantage over Power BI for a jobseeker. Be aware that anything published to Tableau Public is visible to everyone, so use sample or public datasets rather than anything from an employer.
Why does the course spend a whole module on calculations?
Because level of detail expressions are where Tableau users plateau. Most people can build a chart within a week. Explaining why a total in one visual disagrees with the same total in another requires understanding how Tableau scopes aggregation, and that is exactly what a technical interview probes.
Enquire about Tableau
Three details is all we need. A course advisor will call you back.
Related courses
- Power BI
Power BI taught as a data modelling tool rather than a chart menu — star schemas, relationships and DAX first, because that is what separates a report that scales from one that breaks.
- Data Analytics
A structured path from spreadsheets to SQL, Power BI and Python, built around the four things a data analyst is actually paid to do — pull the data, clean it, analyse it and explain it.
- SQL and Databases
SQL taught against a database big enough that a bad query is noticeably slow, because toy datasets hide performance entirely and performance is half of what the job tests.
Next step
Talk to a course advisor
Tell us what you want to learn and we will help you pick the right course, batch and mode.