Statistics
The statistics every data role uses: summarising, reading distributions and measuring uncertainty.
What this course covers
The shared foundation for data analysts, data scientists and data engineers. It starts with types of data, the mean and the median, spread and standard deviation, then histograms, the normal distribution and correlation, and on to samples, standard errors and confidence intervals. Every idea comes with a chart you can move yourself, and examples from online shops, ride-hailing and apps in Indonesia. No formula goes unexplained.
New to AI? Start with AI & data fundamentals.
Part of the career paths:Data analystData engineerData scientist
What you will be able to do
- Summarise data with the right measure of centre and spread
- Read histograms, box plots and the normal distribution
- Tell a signal from sampling noise
- Explain a confidence interval in plain words
Units and bites
- Unit 15 bitesAbout 15 min
Describing data
Centre and spread: how to condense many numbers without hiding anything.
- 3 min
Why statistics
Numbers always bounce around. Statistics helps tell a signal from noise.
- 3 min
Types of data
Categories or numbers? The type of data decides which summary makes sense.
- 3 min
Mean, median and mode
Three ways to say "the typical value", and when each one misleads.
- 3 min
Spread and standard deviation
Two datasets can share an average and tell very different stories.
- 3 min
Percentiles and box plots
Five numbers that summarise a column, and the picture that makes them comparable.
- Unit 25 bitesAbout 15 min
Distributions
The shape of data: histograms, skew, the normal curve, z-scores and correlation.
- 3 min
Reading a histogram
A histogram groups numbers into ranges and counts each one — so the shape of the data shows.
- 3 min
Shapes of distributions
Symmetric, right-skewed or two-peaked — and what that means for the mean and the median.
- 3 min
The normal distribution
The bell curve, the 68–95–99.7 rule, and when your data is not normal at all.
- 3 min
z-scores
How many standard deviations a value is from the mean — a way to compare numbers on different scales.
- 3 min
Scatter plots and correlation
Two variables in one picture, and one number — r — that sums up how tightly they move together.
- Unit 36 bitesAbout 18 min
Samples & uncertainty
From "who did we measure?" to confidence intervals: the honest way to say how sure we are.
- 3 min
Population and sample
We almost never measure everyone. What matters is: who did we actually measure?
- 3 min
Sampling noise
Two samples from the same population give different numbers. Small samples differ far more.
- 3 min
The central limit theorem
The raw data can be skewed. The averages of many samples still form a bell.
- 3 min
Standard error
How far does a sample mean usually miss? One short formula answers that.
- 3 min
Confidence intervals
Not one number but an honest range: the estimate ± how far it could miss.
- 3 min
Comparing two groups
Version B converts at 6%, version A at 5%. Is B really better? Look at the interval of the difference.
- Unit 45 bitesAbout 15 min
Hypothesis testing
Is this difference real or chance? How to answer that honestly.
- 3 min
The null and the alternative
Start from "no effect" and see whether the data argues against it.
- 3 min
p-values, plainly
What a p-value says — and three things people think it says.
- 3 min
Significant vs important
A tiny effect can be significant and a big one not. Size is what decides.
- 3 min
Type I and type II errors
False alarms and missed signals, and what each one costs.
- 3 min
Too many comparisons
Test 20 things and one will be "significant" by chance.
- Unit 56 bitesAbout 18 min
Relationships & regression
Measuring how two variables move together without assuming cause.
- 3 min
Correlation is not causation
Two numbers rising together need not cause each other.
- 3 min
Confounders
The third factor that moves both.
- 3 min
Simpson's paradox
When the total and the groups tell opposite stories.
- 3 min
Simple linear regression
The best line through the points, and what its slope means.
- 3 min
Residuals
What the line leaves out — and what that tells you.
- 3 min
Regression to the mean
Why a record month is usually followed by an ordinary one.
Questions about this course
Learning
How long does Statistics take?
About 81 minutes in all: 5 units made of 27 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; Statistics 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.
- Data visualisationCharts that answer a question honestly, from picking the type to dashboards and stories.
- SQL for analysisAsking the database directly: filtering, summarising, joining tables and window functions.
- 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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