Data Scientist
Predictions, experiments and models the business uses: Python, statistics and machine learning.
What this track covers
The career track for aspiring data scientists. Framing a prediction problem and beating the baseline, Python and pandas, statistics and A/B tests, machine learning basics, honest model evaluation, and using LLMs in data science work. The focus is on decisions rather than algorithms: a good model is one that changes what the business does. Best taken after AI & Data Fundamentals.
New to AI? Every track builds on AI & Data Fundamentals.
What you will be able to do
- Frame a prediction problem without data leakage
- Clean and summarise data with pandas
- Design and read an A/B test you can trust
- Evaluate a model and explain it to stakeholders
Units and bites
- Unit 14 bitesAbout 12 min
What data scientists do
Predictions, experiments and decisions: framing the problem before touching a model.
- 3 min
What the job really is
Analysts ask what happened. Data scientists ask what will happen, and what happens if we change something.
- 3 min
The data science workflow
From framing the problem to monitoring the model, and why most of the time goes on data.
- 3 min
Framing a prediction problem
Unit, target, prediction time and decision — and the data leakage that makes a model look great, then fail.
- 3 min
Beat the baseline first
The simplest guess can look very accurate. Your model has to beat it.
- Unit 25 bitesAbout 15 min
Python & pandas
Loading, cleaning and summarising data with pandas in a notebook.
- 3 min
Notebooks and Python
Jupyter or Colab, cells, and why the order you run them in matters.
- 3 min
DataFrames
A table inside Python: rows, columns, dtypes and how to look inside.
- 3 min
Filtering and selecting
Choosing columns, filtering rows by condition, and how loc differs from iloc.
- 3 min
groupby and merge
Summarising by group and joining two tables without doubling rows.
- 3 min
Missing and messy values
NaN, "Rp125.000" stored as text, and a "Success" status written five ways.
- Unit 35 bitesAbout 15 min
Statistics & experiments
Distributions, hypothesis tests and A/B tests whose results you can trust.
- 3 min
Distributions and sampling
Why a sample average wobbles, and by how much.
- 3 min
Hypothesis tests and p-values
What a p-value really means, and what it does not.
- 3 min
Designing an A/B test
Randomisation unit, primary metric and guardrails before the test starts.
- 3 min
Sample size and power
How long a test has to run to detect a 0.5 percentage-point lift.
- 3 min
Peeking and stopping early
Stopping the first time p dips below 0.05 multiplies false alarms.
- Unit 45 bitesAbout 15 min
Machine learning basics
Regression, classification, decision trees and the features that make a model useful.
- 3 min
Supervised vs unsupervised
Learning from labelled answers, or finding groups on its own.
- 3 min
Regression and classification
Predicting a number or predicting a label, and how to choose.
- 3 min
Decision trees and ensembles
One tree is easy to read; hundreds of trees are more accurate.
- 3 min
Features that matter
Recency, frequency, monetary, and calendar features such as Ramadan and payday.
- 3 min
Overfitting
The model that memorises its training data and trips on new data.
- Unit 55 bitesAbout 15 min
Model evaluation
Honest data splits, the confusion matrix, precision and recall, and explaining them to the business.
- 3 min
Train–test and time splits
When a random split is fine, and when data must be cut by time.
- 3 min
The confusion matrix
Four boxes that explain every kind of right and wrong.
- 3 min
Precision and recall
How often a flag is right, and how many cases get caught.
- 3 min
Cross-validation
Scoring a model several times so one lucky split does not fool you.
- 3 min
Explaining a model to the business
From metric numbers to decision language: how many people, how many rupiah, how much risk.
- Unit 65 bitesAbout 15 min
Using LLMs in data science work
LLMs as a coding partner, a text processor, and a tool that still has to be evaluated.
- 3 min
The LLM as a coding partner
Asking for pandas code you can check, and reading it before you run it.
- 3 min
LLMs for text data
Tagging thousands of reviews with a fixed list of topics.
- 3 min
Evaluating LLM output
Human-labelled samples, agreement rates, and when an LLM is good enough.
- 3 min
Retrieval and context
Giving the LLM the relevant documents so its answers rest on your data.
- 3 min
Privacy and LLMs
Masking personal data before it is sent, in line with data-protection law and company policy.
Questions about this track
Learning
How long does Data Scientist 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 Scientist 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 AnalystFrom a business question to an answer people can use: spreadsheets, SQL, charts and dashboards.
- 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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