Data Analytics · Excel for Analysts
Why Every Data Analyst Should Learn Excel in 2026
Quick summary — why data analysts should learn Excel
Every data analyst should learn Excel in 2026 because it remains a common place where business data is shared, checked and discussed. Modern Excel lets you clean data repeatably with Power Query, answer questions quickly with XLOOKUP, FILTER and UNIQUE, summarise with PivotTables and build simple dashboards. It also complements SQL, Python and Power BI rather than competing with them.
In this guide you will learn:
- Cleaning data — text, dates and duplicates with formulas and Power Query.
- Dynamic arrays — XLOOKUP, FILTER, UNIQUE, SORT and LET in practice.
- PivotTables and dashboards — summaries managers actually read.
- Templates — cleaning checklist, formula sheet and dashboard plan.
- Interview questions — Excel tasks asked in analyst interviews.
SECTION 01Cleaning messy data in Excel
Most analysis time goes on preparation, not on clever charts. Exports from CRMs, billing systems, survey tools and shared sheets arrive with extra spaces, inconsistent capitalisation, dates stored as text, merged cells and duplicate rows. If you summarise that data without cleaning it, "Delhi", "delhi " and "DELHI" become three different cities and your totals are quietly wrong.
Excel gives you two ways to clean. Formulas such as TRIM, PROPER, TEXTBEFORE and DATEVALUE are quick for one-off fixes and easy to audit cell by cell. Power Query records each cleaning step so that next week's export can be cleaned with a single Refresh. A good analyst habit is to never edit the raw data directly: keep the original export untouched on its own sheet and do all the cleaning in a separate, traceable layer.
| Problem in the export | Quick formula fix | Power Query step | Why it matters |
|---|---|---|---|
| Extra or double spaces | =TRIM(A2) | Transform > Format > Trim | Stops false duplicates in groups |
| Inconsistent case | =PROPER(A2) or =UPPER(A2) | Format > Capitalize Each Word | Groups match correctly |
| Full name in one column | =TEXTBEFORE(A2," ") | Split Column > By Delimiter | Lets you sort and join on parts |
| Dates stored as text | =DATEVALUE(A2) then format | Change Type > Date (Using Locale) | Enables month grouping and timelines |
Raw data in Sheet 'Raw', columns: A Name, B City, C Phone, D OrderDate
Clean city (spaces + case):
=PROPER(TRIM(Raw!B2))
Remove non-breaking spaces copied from web pages:
=TRIM(SUBSTITUTE(Raw!B2, CHAR(160), " "))
First and last name from "Firstname Lastname" (double space):
First: =TEXTBEFORE(TRIM(Raw!A2), " ")
Last : =TEXTAFTER(TRIM(Raw!A2), " ", -1)
Keep only the last 10 digits of a phone number:
=RIGHT(SUBSTITUTE(SUBSTITUTE(Raw!C2," ",""),"-",""),10)
Text date "15/03/2026" to a real date (dd/mm/yyyy):
=DATE(RIGHT(Raw!D2,4), MID(Raw!D2,4,2), LEFT(Raw!D2,2))
Flag duplicates by order ID (column A of table Orders):
=IF(COUNTIF(Orders[OrderID], [@OrderID])>1, "Duplicate", "")
Count blank cells in a required column:
=COUNTBLANK(Orders[Amount])
Count text values hiding in a numeric column:
=SUMPRODUCT(--ISTEXT(Orders[Amount]))
Unique list of cities after cleaning (spills down):
=SORT(UNIQUE(Orders[City]))
Reconcile: row count before vs after cleaning
=ROWS(Raw!A2:A5001) & " raw vs " & ROWS(Orders[OrderID]) & " clean"
SECTION 02Dynamic array formulas every analyst should know
Microsoft 365 and Excel 2021 introduced dynamic arrays: one formula can return many results that spill into neighbouring cells. This changes how analysts work. Instead of copying a formula down thousands of rows or building helper columns, you write a single FILTER that returns every matching row, or a UNIQUE that lists each customer once, and the result updates when the data changes.
XLOOKUP replaces most uses of VLOOKUP and INDEX/MATCH. It looks left or right, returns a friendly message when nothing is found and defaults to an exact match. LET lets you name parts of a long formula so it is easier to read and review. If your organisation uses an older Excel version, these functions will not be available, so check which version a team uses — but most interview tests now assume Microsoft 365.
| Function | Analyst question it answers | Example | Replaces |
|---|---|---|---|
| XLOOKUP | Which region is this customer in? | =XLOOKUP(B2, Cust[ID], Cust[Region], "Not found") | VLOOKUP, INDEX/MATCH |
| FILTER | Show all orders above a value in one city | =FILTER(Orders, (Orders[City]="Noida")*(Orders[Amount]>5000)) | Manual filtering and copying |
| UNIQUE | How many distinct customers bought? | =COUNTA(UNIQUE(Orders[CustomerID])) | Remove Duplicates on a copy |
| SORTBY | Top products by revenue | =TAKE(SORTBY(Prod[Name], Prod[Revenue], -1), 5) | Sorting the source data |
Bring region from the Customers table into Orders:
=XLOOKUP([@CustomerID], Customers[CustomerID], Customers[Region], "Missing")
All orders for the city typed in cell H1, newest first:
=SORT(FILTER(Orders, Orders[City]=H1, "No orders"), 4, -1)
(4 = position of the OrderDate column in the table)
Orders between two dates in H2 and H3:
=FILTER(Orders, (Orders[OrderDate]>=H2)*(Orders[OrderDate]<=H3))
Either of two categories (OR uses +):
=FILTER(Orders, (Orders[Category]="Laptops")+(Orders[Category]="Tablets"))
Reference a spilled range elsewhere with #:
=COUNTA(K2#)
One-formula summary table: city, orders, revenue
=LET(
cities, SORT(UNIQUE(Orders[City])),
orders, COUNTIFS(Orders[City], cities),
revenue, SUMIFS(Orders[Amount], Orders[City], cities),
HSTACK(cities, orders, revenue)
)
Add a header row on top:
=VSTACK({"City","Orders","Revenue"},
LET(c, SORT(UNIQUE(Orders[City])),
HSTACK(c, COUNTIFS(Orders[City], c),
SUMIFS(Orders[Amount], Orders[City], c))))
(HSTACK, VSTACK and TAKE need Microsoft 365.)
SECTION 03PivotTables, Power Query and dashboards
A PivotTable is still the fastest way to answer "how much, by what, over when". Drag Region into Rows, Month into Columns and Revenue into Values, and you have a cross-tab in seconds. Group dates by month or quarter, show values as a percentage of the column total, and add slicers so managers can filter by product or channel themselves.
Power Query ties it together. Connect to the raw file, apply your cleaning steps, merge in a lookup table, and load the result to the Data Model. Next month you replace the file and click Refresh All: the clean table, the pivots and the charts update together. A one-page dashboard then combines a few KPI cells, two or three pivot charts and shared slicers. Keep it simple — one clear message per chart is better than a crowded screen.
| Dashboard element | Built with | Good practice | Common mistake |
|---|---|---|---|
| KPI cards | Formula cells linked to the clean table | 3 to 5 numbers with comparison to last period | Ten numbers with no context |
| Trend chart | PivotChart, line, dates grouped by month | Start the axis sensibly, label clearly | 3D effects and gradients |
| Category comparison | PivotChart, sorted bar | Sort largest to smallest | Pie chart with many slices |
| Filters | Slicers and a timeline connected to all pivots | Use Report Connections to link them | Separate filters on every chart |
let
Source = Csv.Document(File.Contents("C:\Reports\sales_export.csv"),
[Delimiter=",", Encoding=65001]),
Headers = Table.PromoteHeaders(Source, [PromoteAllScalars=true]),
Trimmed = Table.TransformColumns(Headers,
{{"City", each Text.Proper(Text.Trim(_)), type text}}),
Typed = Table.TransformColumnTypes(Trimmed,
{{"OrderDate", type date}, {"Amount", type number}}, "en-IN"),
NoBlanks = Table.SelectRows(Typed, each [OrderID] <> null and [OrderID] <> ""),
Deduped = Table.Distinct(NoBlanks, {"OrderID"}),
AddMonth = Table.AddColumn(Deduped, "Month",
each Date.StartOfMonth([OrderDate]), type date)
in
AddMonth
PIVOT FOR MONTHLY REGION REVIEW
Rows : Region, then City
Columns : Month (grouped by Months and Years)
Values : Sum of Amount -> Number format, no decimals
Count of OrderID -> rename "Orders"
Sum of Amount again -> Show Values As
% of Column Total
Filters : Category
Slicers : Channel, Product Line
Timeline: OrderDate
Then: PivotTable Analyze > Insert Slicer
Slicer > Report Connections > tick every pivot
Data > Refresh All after replacing the source file
SECTION 04Templates: cleaning checklist, formula sheet, dashboard plan
Keep these next to you while working on any new dataset or report request.
DATA CLEANING CHECKLIST (before any analysis)
[ ] Raw export saved untouched on its own sheet or file
[ ] Row count of raw data noted: ______
[ ] Headers in one row, no merged cells
[ ] Text trimmed, case made consistent
[ ] Dates are real dates (right-aligned, can group)
[ ] Numbers are numbers (no text, no currency symbols)
[ ] Duplicates checked on the key column
[ ] Blanks in required columns counted and explained
[ ] Lookup gaps (#N/A / "Missing") investigated
[ ] Clean data formatted as a Table and named
[ ] Final totals reconciled with the source system
EXCEL CHEAT SHEET FOR ANALYSTS
Clean : TRIM PROPER UPPER SUBSTITUTE CLEAN
TEXTBEFORE TEXTAFTER TEXTSPLIT VALUE
Lookup : XLOOKUP(key, lookup_col, return_col, "Missing")
Filter : FILTER(table, condition, "None")
AND = (cond1)*(cond2) OR = (cond1)+(cond2)
Distinct: UNIQUE(range) COUNTA(UNIQUE(range))
Sort : SORT(range, col, -1) SORTBY(range, by, -1)
Top N : TAKE(SORTBY(names, values, -1), N)
Summary : SUMIFS COUNTIFS AVERAGEIFS MAXIFS
Readable: LET(name, value, ..., result)
Errors : IFERROR(formula, "") ISNUMBER ISTEXT
Keys : Ctrl+T table Ctrl+Shift+L filter Ctrl+Alt+F5 refresh all
ONE-PAGE DASHBOARD PLAN
Audience : ____________ (who reads it, how often)
Main question : ____________________________________
KPI row (max 5): Revenue | Orders | Avg order value |
New customers | Return rate
each vs previous period
Chart 1 : Monthly revenue trend (line)
Chart 2 : Revenue by region (sorted bar)
Chart 3 : Top 10 products (table or bar)
Slicers : Channel, Category + date timeline
Data source : Power Query -> Orders table
Refresh : Replace file, Data > Refresh All
Check : KPI totals match source system
SECTION 05Tips for analysts building Excel skills
Excel rewards good habits more than memorised shortcuts. These make the biggest difference:
- Separate raw, clean and report — three layers make your work traceable and easy to refresh.
- Use Tables everywhere — structured references keep formulas readable and ranges automatic.
- Prefer Power Query for repeat jobs — if you will do it again next month, record it as query steps.
- Reconcile your totals — always check that your final numbers match the source system.
- Learn where Excel stops — very large data or shared live reporting usually belongs in SQL or Power BI.
SECTION 06Interview Q&A — Excel for data analyst roles
Q1Why use XLOOKUP instead of VLOOKUP?
XLOOKUP can return values from columns to the left or right, defaults to exact match, has a built-in if-not-found argument and does not break when columns are inserted. VLOOKUP needs a column index number and only looks to the right.
Q2How would you remove duplicates without losing the original data?
I keep the raw data untouched and either use UNIQUE on the key column to create a de-duplicated list, flag duplicates with COUNTIF in a helper column, or use Power Query's Remove Duplicates step so the cleaning is repeatable and traceable.
Q3What is Power Query and when do you use it?
Power Query is Excel's built-in tool for importing and transforming data through recorded steps. I use it whenever a report repeats, the source needs cleaning, or several files and tables must be combined, because a single refresh reapplies every step.
Q4How do you check that a PivotTable is correct?
I compare its grand total with a SUM or COUNT on the source table, confirm the source range includes all rows, check for blank or text values in number columns, and refresh after any data change.
Q5What does a #SPILL! error mean?
A dynamic array formula wants to return several values, but the cells it needs to spill into are not empty or it is inside a Table. Clearing the blocking cells or moving the formula outside the Table fixes it.
SECTION 07Test yourself — Excel for data analysts quiz
Five questions. No sign-up.
0 / 5Pick an answer to see why it is right or wrong.
SECTION 08Frequently asked questions
Is Excel still required for data analyst jobs in 2026?
Yes, in many roles. Analyst job descriptions commonly list Excel alongside SQL and a BI tool, because business teams still share and review data in spreadsheets. Expect practical Excel tasks in interviews, especially for entry-level positions.
Which Excel skills do data analysts need most?
Data cleaning with text and date functions, XLOOKUP, SUMIFS and COUNTIFS, dynamic arrays such as FILTER and UNIQUE, PivotTables with slicers, Power Query for repeatable preparation, and clear charts for a simple dashboard. Keyboard shortcuts help with speed in timed tests.
Should a data analyst learn Excel or Power BI first?
Excel first for most learners. It teaches tables, formulas and pivots, which carry over directly into Power BI. Power Query is shared by both tools, so learning it in Excel makes the move to Power BI much smoother.
Can Excel handle large datasets for analysis?
A worksheet holds just over one million rows. Power Query and the Data Model can work with larger volumes, but performance depends on your machine. For very large or frequently queried data, SQL databases or Power BI are better choices.
What Excel test is given in data analyst interviews?
Often a short practical task: clean a messy sheet, look up values from another table, build a PivotTable summary, answer a few business questions and sometimes create a chart. Speed matters, but correct, well-checked answers matter more.
SECTION 09Related reads
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