Learn data analysis in Excel—by doing it.

Identify the ribbon, formula bar, Name Box, worksheets, rows, columns, cells and ranges.

Open a blank workbook. Rename three sheets Raw_Data, Analysis and Dashboard. Enter your name in B2 and use the Name Box to jump to B2.

Use Ctrl+Arrow, Ctrl+Home, Ctrl+End, Page Up/Down, sheet tabs and the Name Box.

Create a 20-row list, then reach its four edges using only keyboard shortcuts. Freeze the top row from View > Freeze Panes.

Distinguish values, text, dates and formulas; edit with F2; undo, redo and clear contents.

Enter five products with dates, quantities and prices. Correct two deliberate mistakes using F2 and Undo/Redo.

Apply number, currency, percentage and date formats without changing stored values.

Format Unit_Price as currency, Discount as percentage and Date as a readable date. Widen columns with AutoFit.

Use Shift, Ctrl, fill handle, copy, paste and Paste Special.

Create dates 1–10 September with a fill series. Copy prices and use Paste Special > Values into a second area.

Understand why =B2C2 changes when copied down.

Lock a tax or exchange-rate cell with $ signs and recognise A$1 versus $A1.

Put tax rate 8% in H2. Calculate tax with =D2*$H$2 and total with =D2+E2.

Use clear sheet names, consistent headers, sensible colours and separate raw data from outputs.

Move raw entries to Raw_Data, calculations to Analysis and a one-number summary to Dashboard.

Use file versions, descriptive names, workbook properties and simple sheet protection appropriately.

Save a new version as Excel101_v02.xlsx. Protect formula cells while leaving input cells editable.

Rebuild a small sales sheet without following a demonstration.

From a blank workbook, enter 15 sales rows, format them, calculate Revenue and Tax, freeze headers and save correctly.

Apply the rule: one row per observation, one column per variable, one header row.

Repair a badly designed mini-sheet containing merged headers, blank rows and totals inside the data.

Use Ctrl+T, table names, filters, Total Row and automatic expansion.

Convert the practice sales range to a table and rename it Sales. Add one record beneath it.

Read and write formulas such as =[@Units][@Unit_Price].

Perform single and multi-level sorts without separating rows.

Sort by Region A–Z, then Net_Sales largest to smallest. Restore original order using Order_ID.

Use text, number, date and colour filters; clear filters explicitly.

Show only South-region orders above MVR 1,000. Record the visible order count, then clear filters.

Use Go To Special, Conditional Formatting and Remove Duplicates carefully.

Highlight duplicate Order_ID values and blank Region cells. Copy the data before removing any duplicates.

Use TRIM, CLEAN, UPPER, LOWER, PROPER and Find/Replace.

Clean inconsistent salesperson names and invisible spaces in a helper column using =PROPER(TRIM(CLEAN(cell))).

Use Text to Columns, Flash Fill, TEXTJOIN and concatenation.

Split Full_Name into first and last name. Build a label combining Order_ID, Region and Product.

Create dropdowns and numeric/date rules with helpful messages.

Add a Region dropdown and restrict Customer_Rating to whole numbers 1–5. Test invalid input.

Clean an intentionally messy dataset and document every decision.

Fix headers, types, blanks, spaces, duplicate IDs and inconsistent categories. Never silently invent missing values.

Recognise =, function names, arguments, operators, precedence and nested expressions.

Evaluate =2+3*4 and =(2+3)*4. Explain the difference. Use Insert Function to inspect SUM.

Summarise numeric columns and distinguish total from typical value.

Calculate total Net_Sales, average order value, smallest order and largest order.

Choose COUNT, COUNTA, COUNTBLANK and COUNTIF according to the question.

Count numeric sales, nonblank IDs, blank ratings and South-region orders.

Aggregate one or several criteria safely.

Calculate Net_Sales for North; then North sales for one category using SUMIFS.

Count records matching business conditions.

Count orders above MVR 1,000 and orders that are both South and rated 4 or 5.

Calculate conditional averages while checking sample size.

Find average order value by region and average rating for one product category.

Use TODAY, YEAR, MONTH, DAY, EOMONTH and date arithmetic.

Calculate order age in days, month label and month-end date from each sale date.

Use LEFT, RIGHT, MID, LEN, FIND, SUBSTITUTE, TEXTBEFORE and TEXTAFTER when available.

Extract the numeric part of an Order_ID and split a code formatted Region-Category.

Use ROUND, ROUNDUP, ROUNDDOWN and understand displayed versus stored decimals.

Calculate commission at 3.75%, round payments to two decimals and compare with formatting only.

Build a reusable summary block from a question sheet.

Answer ten questions using SUMIFS, COUNTIFS, AVERAGEIFS, dates and text functions.

Translate a plain-language rule into a logical test and two outcomes.

Classify orders as Target/Below Target using a threshold cell and an absolute reference.

Combine conditions without losing the business meaning.

Flag Priority when Net_Sales > 1500 AND Rating >= 4; flag Review when either value is missing.

Create ordered categories and prevent overlapping thresholds.

Classify sales as High, Medium or Low with IFS or nested IF. Test exact boundary values.

Use IFERROR only after understanding the original error.

Create a division that can produce #DIV/0!, diagnose it, then wrap a meaningful fallback.

Retrieve matching values with exact match and a not-found result.

Create a Products table, then return Category and Unit_Price into Sales with XLOOKUP.

Understand a flexible lookup pattern and why it still matters.

Rebuild one XLOOKUP result using INDEX(return_range,MATCH(value,lookup_range,0)).

Use sorted thresholds for grades, commissions or bands.

Build a rating-band table and classify scores with approximate XLOOKUP or VLOOKUP.

Use FILTER, SORT and UNIQUE and understand spill ranges.

Return a sorted unique product list and a live table of orders above a selected threshold.

Name intermediate calculations inside a formula.

Rewrite a repeated revenue-and-tax formula with LET and meaningful variable names.

Join two tables and create management flags.

From Sales and Products, retrieve category and cost, calculate margin, flag low-margin orders and list them with FILTER.

Choose a measure of centre based on distribution and purpose.

Calculate mean and median Net_Sales. Add one extreme value and observe which measure changes more.

Measure spread and distinguish sample from population functions.

Calculate range, VAR.S and STDEV.S for order values by region.

Locate values within a distribution.

Calculate Q1, median, Q3 and the 90th percentile of Net_Sales.

Apply a transparent rule instead of deleting unusual values automatically.

Compute IQR, lower fence and upper fence; flag values outside the fences.

Create bins, FREQUENCY results and a histogram.

Group order values into sensible bands and chart the distribution.

Avoid averaging averages when groups have different sizes.

Calculate weighted average price using SUMPRODUCT(price,units)/SUM(units).

Measure linear association and avoid claiming causation.

Use CORREL on Discount and Units, then create a scatterplot.

Understand population, sample, selection bias and missingness.

Take every fifth row as a systematic sample and compare its mean with the full data.

Explain why estimates vary and compute a simple confidence interval.

Calculate mean, standard error and a 95% interval using CONFIDENCE.T or a t-based approach.

Write a one-page descriptive analysis without causal language.

Summarise centre, spread, outliers, distribution and one relationship in the practice sales data.

Create a PivotTable from an Excel Table and understand fields.

Insert a PivotTable from Sales. Put Region in Rows and Net_Sales in Values.

Switch Sum, Count, Average and percentage calculations intentionally.

Show order count, average order value and total sales together.

Group dates into months, quarters and years where supported.

Create monthly sales totals and expand/collapse the hierarchy.

Create useful bands while retaining detail.

Group Net_Sales into value ranges and count orders per band.

Use report filters, label filters and value filters.

Show the top five products by Net_Sales within one selected region.

Add visual filters and connect them to the right PivotTables.

Add Region and Category slicers; format them compactly.

Know when a source calculation, calculated field or measure is appropriate.

Compare sum of row-level margin with a ratio calculated from totals. Explain why they may differ.

Use % of total, running total, rank and difference from.

Show each region’s share of sales and monthly running total.

Link a chart to a PivotTable without clutter.

Create a PivotChart of monthly sales and control it with a slicer.

Answer five new questions without writing worksheet formulas.

Build a compact PivotTable report answering who, what, where, when and how much.

Match comparison, trend, distribution, relationship and composition to chart types.

For five questions, select a bar, line, histogram, scatter or carefully justified composition chart.

Sort values, use direct labels and remove nonessential ink.

Chart regional sales from largest to smallest with one highlight colour.

Use continuous time, sensible axis intervals and zero only when context requires it.

Chart monthly sales, annotate the highest and lowest months.

Plot two numeric variables and inspect form, direction and strength.

Plot Discount against Units; add a linear trendline and display R².

Use colour scales, data bars and formula rules to direct attention.

Highlight low margin and high-value orders with two restrained rules.

Define a metric, comparison and target before drawing a card.

Create Total Sales, Average Order Value, Orders and Target Attainment KPIs.

Apply a visual hierarchy: questions, KPIs, trends, breakdowns and filters.

Sketch the dashboard on paper, then create aligned sections in Excel.

Connect slicers and timelines; test all combinations.

Build a dashboard with KPIs, monthly trend, regional bars and two slicers.

Use meaningful titles, sufficient contrast, alt text, focus order and print settings.

Add chart alt text, verify greyscale readability and set one-page landscape print area.

Present a dashboard as an argument, not decoration.

Give a five-minute walkthrough: question, evidence, insight, caveat, action.

Understand connect, transform, combine and load—the ETL workflow.

Open Data > Get Data, import the practice CSV and inspect the Power Query Editor.

Assign text, whole number, decimal, date and percentage types deliberately.

Correct every column type and identify any conversion errors.

Filter early and retain only analysis-relevant fields.

Remove blank rows, exclude test orders and keep required columns.

Trim, clean, split, replace and standardise using repeatable steps.

Standardise Region and Category, split a compound code and round a numeric field.

Create derived fields in Power Query and inspect generated M.

Add Revenue and a Value_Band conditional column.

Summarise rows by dimensions before loading.

Group by Region and Category; calculate total sales, order count and average rating.

Join tables using keys and validate match quality.

Merge Sales with Products by Product_ID using a left outer join. Inspect unmatched rows.

Stack files with identical structures and preserve source context.

Create January and February extracts, append them, and add a Source_Month field.

Separate changing inputs from transformation logic.

Create a folder or path parameter where available, change the source and refresh.

Build a raw-to-clean pipeline and document it.

Import two files, standardise types, append, merge a lookup and load a final table.

Convert vague requests into metric, population, period, comparison and decision.

Rewrite “How are sales?” into five answerable questions.

Define every field, type, unit, allowed values, owner and caveat.

Create a dictionary for the Sales dataset and mark derived fields.

Follow structure → quality → distribution → relationships → segments → time.

Create an EDA checklist and record findings before making recommendations.

Use Goal Seek, Scenario Manager or Data Tables for controlled assumptions.

Use Goal Seek to find units required to meet a revenue target; test three discount scenarios.

Separate trend, seasonality and noise; evaluate rather than trust a forecast.

Create a forecast sheet or FORECAST.ETS where suitable and hold back recent periods for checking.

Use regression for association and prediction while checking assumptions.

Enable Analysis ToolPak, regress Units on Discount and Price, then inspect coefficients and R².

Create useful groups based on behaviour rather than arbitrary labels.

Segment products by sales and margin into four action groups.

Identify whether a minority of items drives most outcomes.

Sort products by sales, calculate cumulative percentage and build a Pareto chart.

Reconcile totals, test formulas, trace precedents and build control checks.

Add row-count, total, duplicate-key and missing-value controls to the workbook.

Use the structure: finding → evidence → meaning → action → caveat.

Convert five chart descriptions into decision-ready insight statements.

Understand why multiple related tables are better than one giant flattened sheet.

Add Sales and Products to Excel’s Data Model and identify the one-to-many relationship.

Distinguish events from descriptive lookup tables and define grain.

Classify Sales as a fact table and Products, Calendar and Regions as dimensions. Write the grain of each.

Build a continuous date dimension for reliable time analysis.

Create or import a Calendar table with Date, Year, Quarter, Month and Month_Number; relate it to Sales.

Navigate Diagram View, Data View, calculation area and relationships.

Enable Power Pivot where supported, inspect the model and hide technical keys from client tools.

Choose row-level stored calculations or context-dependent aggregations.

Create one calculated column and one measure; compare when each is evaluated and stored.

Write explicit measures with SUM, COUNTROWS, DISTINCTCOUNT and DIVIDE.

Create Total Sales, Orders, Customers and Average Order Value measures.

Understand how PivotTable rows, columns, filters and slicers change a measure.

Place Total Sales by Region and Category; apply slicers and explain why the measure changes.

Modify filter context intentionally with CALCULATE.

Create sales for one category and sales excluding one region; compare with the unfiltered total.

Use the Calendar table for year-to-date and prior-period comparisons.

Create Sales YTD, Previous Month Sales and Month-over-Month Change where the data supports it.

Build and document a small star schema.

Load at least three tables, create relationships, define six measures and build one PivotTable.

Reduce volatile formulas, excessive formatting, full-column calculations and duplicated work.

Inspect a slow workbook, identify three performance risks and replace at least two.

Use Trace Precedents, Trace Dependents, Evaluate Formula and Watch Window.

Diagnose three planted errors: wrong range, hard-coded constant and inconsistent copied formula.

Apply named ranges, tables, templates and documented input/output conventions.

Turn one analysis workbook into a reusable template with highlighted inputs and protected calculations.

Use comments, notes, co-authoring and change discipline without creating conflicting truths.

Review a workbook with another person, resolve three comments and record decisions.

Minimise personal data, control access, anonymise outputs and avoid harmful inference.

Audit the practice workbook as if it contained employee data; remove or mask unnecessary identifiers.

Recognise when refresh, Office Scripts, VBA or Power Automate may help—and when not to automate.

Write a step-by-step specification for one repeated task, then automate only a safe portion if available.

Use natural-language analysis as a hypothesis generator, then verify every output.

Ask Analyze Data three questions. Recreate one answer manually and compare filters and aggregation.

Create a refresh checklist, last-refresh stamp, source register and output version.

Prepare a monthly report so another person can refresh it from new files.

Show problem, process, evidence, decision and impact without exposing confidential data.

Create a case-study page using screenshots or anonymised outputs from one course project.

Simulate a real analyst handover under time pressure.

Receive a new CSV, clean it, update the model, refresh the dashboard, check controls and brief the teacher.

Answer a management question end-to-end.

Choose a real question, clean data, analyse it, visualise results and recommend one action.

Select a decision-relevant problem small enough to finish.

Write the stakeholder, decision, five questions, success criteria and exclusions.

Design sheets, table names, keys, calculations and outputs before building.

Draw the workbook architecture from Raw to Clean to Model to Analysis to Dashboard.

Collect permitted data and preserve an untouched source.

Import at least two related tables; record source, date, owner and limitations.

Use Power Query or documented steps, never unexplained manual overwrites.

Profile types, blanks, duplicates, invalid values and keys; create clean outputs.

Create relationships or lookups and define trusted measures.

Build a star-like model where appropriate and document KPI formulas.

Run descriptive analysis, segments, time patterns and exceptions.

Produce an EDA page containing distributions, trends and top/bottom cases.

Select only evidence that answers the project questions.

Create final tables and charts; remove anything interesting but irrelevant.

Create a one-screen interactive decision view.

Add KPI cards, trend, comparison, breakdown and filters; test all states.

Test data, formulas, usability, accessibility and interpretation.

Use the QA checklist; ask your teacher/friend to find three weaknesses; repair them.

Explain the decision, evidence, uncertainty and next action in ten minutes.

Deliver the presentation, answer questions, then write what you would improve.