Data Analyst
From a business question to an answer people can use: spreadsheets, SQL, charts and dashboards.
What this track covers
The career track for aspiring data analysts. It starts with how analysts think — sharpening the question, defining the metric, checking numbers before sharing them — then spreadsheets, SQL basics, honest charts, dashboards and storytelling, and just enough statistics to make decisions. The examples come from working life in Indonesia: online shops, marketplaces, Ramadan and 11.11. Best taken after AI & Data Fundamentals.
New to AI? Every track builds on AI & Data Fundamentals.
What you will be able to do
- Turn a vague request into a sharp analysis question
- Summarise data with spreadsheets and SQL without miscounting
- Build honest charts and dashboards that people read
- Use basic statistics to support a decision
Units and bites
- Unit 14 bitesAbout 12 min
How analysts think
Question first, data second: the habits that make an analyst trusted.
- 3 min
What a data analyst actually does
Not making as many charts as possible, but helping people make decisions.
- 3 min
Start with the question
Requests arrive vague. Three questions back turn them into something you can answer.
- 3 min
Define your numbers
The same data can give three "active customer" numbers. Agree the definition before you count.
- 3 min
Check before you share
Five small checks that stop a wrong number reaching the board.
- Unit 25 bitesAbout 15 min
Spreadsheets for analysts
Cleaning, looking up and summarising data in Google Sheets or Excel without miscounting.
- 3 min
Tidy data
One row per event, one column per thing: the table shape that is easy to analyse.
- 3 min
Cleaning text and dates
TRIM, PROPER, and a 03/11 date that could mean 3 November or 11 March.
- 3 min
SUMIFS and COUNTIFS
Summing and counting with conditions — the formulas analysts use most.
- 3 min
Lookups without tears
Combining two tables with XLOOKUP, and why #N/A is often a hidden space.
- 3 min
Pivot tables
Summarising tens of thousands of rows by city and month in a minute.
- Unit 35 bitesAbout 15 min
SQL basics
Asking the database directly: selecting, filtering, grouping and joining tables.
- 3 min
SELECT and WHERE
Taking the columns you need and filtering to the rows that matter.
- 3 min
GROUP BY and aggregates
SUM, COUNT and AVG per city or per month, and how WHERE differs from HAVING.
- 3 min
Joining tables
INNER and LEFT JOIN, and the join that quietly doubles your rows.
- 3 min
Dates and time zones
Cutting data by month correctly when the database stores time in UTC.
- 3 min
NULLs and edge cases
Why COUNT(*) and COUNT(column) differ, and how to handle missing values safely.
- Unit 45 bitesAbout 15 min
Charts that tell the truth
Choosing the right chart and avoiding misleading ones, including the accidental kind.
- 3 min
Choosing a chart type
Lines for time, bars for comparison, and when a table beats both.
- 3 min
Axes and scales
Axes that do not start at zero and scales that make small changes look dramatic.
- 3 min
Totals vs rates
The biggest city nearly always wins on totals. Compare per store, per user or per head.
- 3 min
Seasonality in charts
Ramadan, Lebaran, 11.11: comparing with a like-for-like period, not the month before.
- 3 min
Titles that state the point
Chart titles that state the finding, not just the name of the metric.
- Unit 55 bitesAbout 15 min
Dashboards & storytelling
Dashboards people actually read, and presenting findings so they get acted on.
- 3 min
A dashboard for a decision
Start from who reads it and what they decide every week.
- 3 min
KPI tiles and context
A number without a comparison means nothing: target, last period and last year.
- 3 min
Automating the refresh
From a manual export every Monday to data that refreshes itself every morning.
- 3 min
The one-page story
Finding, evidence and recommendation, in the order a manager reads them.
- 3 min
Presenting bad news
How to present numbers nobody wanted without losing trust.
- Unit 65 bitesAbout 15 min
Stats for decisions
Just enough statistics not to be fooled: medians, samples, A/B tests and differences that matter.
- 3 min
Mean, median and outliers
One corporate order can drag the mean. The median tells you about the typical customer.
- 3 min
Spread and ranges
Two shops with the same average can be very different. Percentiles and ranges show it.
- 3 min
Samples and noise
Why 6 orders vs 9 orders does not prove anything yet.
- 3 min
Reading an A/B test
Conversion, percentage-point gaps, and the temptation to stop a test too early.
- 3 min
Correlation and confounders
When two numbers rise together because a third thing drives both.
Questions about this track
Learning
How long does Data Analyst take?
About 87 minutes in all: 6 units made of 29 bites of about three minutes each, and every bite ends with a quick check. You can do one bite a day or several in a row.
Do I need to know how to code?
No. Every Datamew track is written for working professionals without a technical background. Where there is arithmetic, it is the kind you can do on a phone or in a spreadsheet.
Where should I start?
Everyone begins with AI & Data Fundamentals, which covers how AI works, how to write a good prompt and how to read data honestly. Then take Data Analyst for your role.
When can I start learning?
Datamew is coming to iOS and Android soon. There is no release date or price announced yet: join the waitlist and we will tell you when the app is out.
Is it available in Bahasa Indonesia?
Yes. Datamew is written in English and Bahasa Indonesia.
More tracks
- AI & Data FundamentalsHow AI works, how to instruct it, and how to read numbers honestly.
- Data ScientistPredictions, experiments and models the business uses: Python, statistics and machine learning.
- Data EngineerBuilding pipelines that deliver data on time and correct: SQL, Python, orchestration and warehouses.
Read while you wait
Guides from the Datamew team, free on the web today.
How to Become a Data Analyst in Indonesia: Skills, Roadmap and Salary (2026)
To become a data analyst, learn spreadsheets, SQL, one BI tool such as Looker Studio or Power BI, basic statistics, and how to explain a finding in plain words. Most people can learn the basics in about six months part-time, then need two or three portfolio projects to get interviews. In Indonesia the average data analyst salary on Indeed is about Rp6.3 million a month.
8 min readHow to Become a Data Scientist in Indonesia: Skills, Roadmap and Salary (2026)
A data scientist uses Python, statistics and machine learning to predict what will happen and to test what works. Most people get there through data analysis first: learn SQL and statistics, then Python, experiments and machine learning, which takes roughly a year part-time. In Indonesia, data scientist salaries on Indeed average about Rp14.5 million a month.
8 min readBe first on the app
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