Data preparation
From a raw export to a table you can trust: tidy shape, missing values, duplicates and formats.
What this course covers
A shared foundation for data analysts, data scientists and data engineers. Data from seller dashboards, forms and apps is rarely ready to count: orders recorded twice, blank cells, dates in three formats and rupiah amounts stored as text. This course teaches the tidy table shape, how to handle missing values and duplicates without inventing data, and how to check your work against row counts and the source figures. It then puts that into practice in Google Sheets and Excel, in Python with pandas, and on to joining tables and quality checks that run by themselves.
New to AI? Start with AI & data fundamentals.
Part of the career paths:Data analystData engineerData scientist
What you will be able to do
- Spot duplicates, missing values and inconsistent formats in raw data
- Reshape data into tidy form: one row per event, one column per variable
- Clean data in Google Sheets, Excel and pandas with steps you can repeat
- Check prepared data against row counts and the source figures
Units and bites
- Unit 15 bitesAbout 15 min
Why data needs preparing
Raw data, the tidy table shape, missing values and duplicates, inconsistent formats, and how to check your work.
- 3 min
A first look at raw data
An order export is rarely ready to analyse. Spot the problems before you calculate anything.
- 3 min
One row per event
The tidy table shape: every row one event, every column one thing.
- 3 min
Missing values and duplicates
A blank cell is not zero, and rows that look alike are not always duplicates.
- 3 min
Inconsistent formats
Dates, rupiah amounts and city names written in different ways, and how to make them consistent.
- 3 min
Checking your work
Record the row count before and after every step, and make sure every change can be explained.
- Unit 24 bitesAbout 12 min
Spreadsheets for analysts
Cleaning, looking up and summarising data in Google Sheets or Excel without miscounting.
- 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
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 45 bitesAbout 15 min
Joining and reshaping data
Combining tables without losing or duplicating rows, and reshaping a table to fit the question.
- 3 min
Choosing a join key
Which column really identifies a row, and why a customer name is not a key.
- 3 min
Checking join match rates
What share of rows found a match, and what it means when many did not.
- 3 min
Stacking monthly exports
Combining January-to-December exports whose columns are not always the same.
- 3 min
Wide to long, and back
Melt and pivot: the long shape for analysis, the wide shape for reading.
- 3 min
Building a clean orders table
From three raw exports to one orders table the whole team can use.
- Unit 53 bitesAbout 9 min
Data quality checks you can automate
Writing down what you expect of the data as rules, then running them every time the data refreshes.
- 3 min
Writing down data expectations
From "the data looks odd" to rules you can test: unique, not blank, within range.
- 3 min
Checks for nulls and uniqueness
Two checks that catch most problems, in a spreadsheet, SQL and pandas.
- 3 min
Range and freshness checks
Negative totals, dates in the future, and data that stopped updating yesterday.
Questions about this course
Learning
How long does Data preparation take?
About 66 minutes in all: 5 units made of 22 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 preparation 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.
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- AI & data fundamentalsHow AI works, how to instruct it, and how to read numbers honestly.
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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.
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8 min readBe first on the app
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