Data visualisation
Charts that answer a question honestly, from picking the type to dashboards and stories.
What this course covers
A shared foundation for data analysts, data scientists and data engineers. It starts with charts that tell the truth: picking a chart from the question, axes that do not mislead, totals vs rates, seasons such as Ramadan and 11.11, and titles that state the finding. Then colour and labelling, dashboards people actually read, and telling a story with data. Every bite has real charts and questions that check you understood, with examples from online shops, ride-hailing and marketplaces in Indonesia.
New to AI? Start with AI & data fundamentals.
Part of the career paths:Data analyst
What you will be able to do
- Choose the chart that answers the question
- Spot truncated axes, dual y-axes and totals that should be rates
- Compare seasonal periods such as Ramadan and 11.11 like for like
- Write chart titles that state the finding
Units and bites
- Unit 15 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
The question picks the chart: lines for time, bars for comparison, scatter for relationships, and a table for exact numbers.
- 3 min
Axes and scales
Bars must start at zero, lines may zoom if they say so, and two y-axes almost always mislead.
- 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: compare with a like-for-like period, not the month before. And remember that Ramadan moves every year.
- 3 min
Titles that state the point
Chart titles that state the finding, not just the name of the metric, and claim no more than the chart shows.
- Unit 25 bitesAbout 15 min
A chart for every question
Bars, lines, histograms, scatter plots and parts of a whole: when to use each and how to make it clean.
- 3 min
Comparing categories
Sorted bars, horizontal bars for long labels, and when a dot plot is tidier.
- 3 min
Change over time
Lines, areas, and how many lines one chart can hold before it stops being readable.
- 3 min
Showing a distribution
Histograms and box plots for delivery times, basket values and salaries.
- 3 min
Showing a relationship
Scatter plots, trend lines, and why a correlation on a chart is not yet a cause.
- 3 min
Parts of a whole
Pies, stacked bars and 100% bars: which can be compared and which cannot.
- Unit 34 bitesAbout 12 min
Colour and labelling
Colour that means something, labels on the data itself, and charts everyone can still read.
- 3 min
Colour with a purpose
One colour for ordinary data, one highlight colour for what matters.
- 3 min
Charts that work for colour-blind readers
About 1 in 12 men struggle to tell red from green. How to choose a palette that still reads.
- 3 min
Direct labels, not legends
Writing the series name at the end of the line so the eye does not shuttle back to a legend.
- 3 min
Numbers and units on charts
Rp million or Rp billion, per cent or percentage points, and how many decimals you need.
- Unit 46 bitesAbout 18 min
Dashboards people read
Dashboards that answer a recurring question at a glance, not a pile of every chart that could be made.
- 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
Dashboard layout
Headline numbers top left, detail below, and an order that follows how people read.
- 3 min
Filters and drill-down
Date, city and channel filters that do not leave the reader lost.
- 3 min
Automating the refresh
From a manual export every Monday to data that refreshes itself every morning.
- 3 min
Keeping dashboards trusted
Refresh dates, metric definitions, and what happens when the numbers disagree with another report.
- Unit 55 bitesAbout 15 min
Telling a story with data
From finding to message: annotations, the order of charts, uncertainty, and the summary a manager reads.
- 3 min
From finding to message
One sentence that answers "so what?", before the first chart is made.
- 3 min
Annotating charts
Marking launches, promos and outages on the chart itself so readers do not have to guess.
- 3 min
Putting charts in order
From the big picture to the cause to the recommendation, in three to five charts.
- 3 min
Showing uncertainty
Ranges, intervals and honest wording when the data is not yet certain.
- 3 min
Writing the summary
Three lines at the top of the report: the finding, the evidence and what you recommend.
Questions about this course
Learning
How long does Data visualisation take?
About 75 minutes in all: 5 units made of 25 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?
Not to start. The foundations explain every idea in plain words first; where a course uses SQL or Python, it teaches it from the beginning, with a playground in the lesson.
Where should I start?
If you are new to AI, begin with AI & data fundamentals. If you know the job you want, follow its career path; Data visualisation has a place in it.
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 courses
- AI & data fundamentalsHow AI works, how to instruct it, and how to read numbers honestly.
- Data analystHow analysts think and work: from a business question to an answer people use.
- Data engineerBuilding pipelines that deliver data on time and correct: SQL, Python, orchestration and warehouses.
- Data scientistWhat data scientists do, and how to use LLMs in the work.
- Data preparationFrom a raw export to a table you can trust: tidy shape, missing values, duplicates and formats.
- SQL for analysisAsking the database directly: filtering, summarising, joining tables and window functions.
- StatisticsThe statistics every data role uses: summarising, reading distributions and measuring uncertainty.
- Supervised and unsupervised learningThe two ways machines learn from data: with answers, and without them.
- Product recommendationsHow "you might also like" works, the data behind it, and how to prove it pays.
- A/B testingMeasuring whether a change really worked, with two random groups and an honestly read result.
- ForecastingForecast demand as a range, ready for Ramadan, 11.11 and 12.12.
- Customer segmentationSplitting customers into groups that differ, then treating each group differently.
- Social media analyticsRead Instagram, TikTok and YouTube numbers honestly, and use AI without being fooled.
- AI for finance reconciliationMatching bank statements to invoices: rules first, AI for the rest, people decide.
- AI for SEOHow search works in the age of AI answers, keyword research, Search Console, and writing with AI responsibly.
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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