Social media analytics
Read Instagram, TikTok and YouTube numbers honestly, and use AI without being fooled.
What this course covers
For anyone who runs or reports on social media accounts. You learn what reach, impressions and engagement actually count, how to compute engagement rate honestly, how to compare posts fairly, and how to read a weekly trend without being misled by one viral week. You also use AI to summarise hundreds of comments, then check its output against a sample. Later units cover content experiments, social listening, attribution to sales and reporting to stakeholders.
New to AI? Start with AI & data fundamentals.
Part of the career paths:Data analyst
Units and bites
- Unit 15 bitesAbout 15 min
Honest social media numbers
Reach, engagement rate, fair comparisons, weekly trends and AI comment summaries you have checked.
- 3 min
The numbers that matter
Reach, impressions and engagement: what each one counts, and why every platform counts them a little differently.
- 3 min
Engagement rate, computed honestly
One name, several formulas. Choose the denominator, decide which interactions count, and write both down.
- 3 min
Comparing posts fairly
Counts or rates, followers at the time, and why posts with a small reach are easy to misread.
- 3 min
Reading a weekly trend without fooling yourself
One viral week is not a baseline. Compare with the usual range, not with the peak.
- 3 min
Summarising comments with AI, then checking
AI reads hundreds of comments quickly, but can misread sarcasm and slang. Check it with a sample.
- Unit 24 bitesAbout 12 min
Content experiments
Testing formats, hooks and posting times in a way whose results you can trust.
- 3 min
One change at a time
If the format, caption and posting time all change at once, you cannot tell which one mattered.
- 3 min
Testing posting times
How to compare posting times without being fooled by different days and topics.
- 3 min
How many posts are enough
One post is not evidence. How many you need before you dare draw a conclusion.
- 3 min
An experiment log
One simple spreadsheet to record the hypothesis, change and result of every experiment.
- Unit 34 bitesAbout 12 min
Social listening and sentiment
Hearing what people say about your brand beyond your own account.
- 3 min
What social listening is
The difference between monitoring your own account and listening to conversations across platforms.
- 3 min
Building a keyword list
Brand names, typos, abbreviations and product names: the keywords that decide what you catch.
- 3 min
Sentiment labels you can trust
Testing the accuracy of sentiment labels from a tool or AI against a human-labelled sample.
- 3 min
Spotting an issue early
When a rise in complaints is a signal, and when it is just a few loud comments.
- Unit 44 bitesAbout 12 min
From social to sales
Connecting content to clicks and sales, and admitting what cannot be measured.
- 3 min
UTM links
Tagging links so visits from each post show up in your site analytics.
- 3 min
Promo codes and landing pages
Other ways to track sales from creators and campaigns, and their weaknesses.
- 3 min
The limits of last-click attribution
Why the content that introduced people to your brand often gets no credit.
- 3 min
Holdout tests
Comparing regions or groups that did not see a campaign to measure its real effect.
- Unit 54 bitesAbout 12 min
Reporting to stakeholders
Social media reports that get read, understood and used to make decisions.
- 3 min
A one-page monthly report
A few numbers, one chart and three sentences: what happened, why, and what next.
- 3 min
Metrics the team can act on
Telling numbers that look nice from numbers that change decisions.
- 3 min
Explaining a drop
How to report a falling number honestly, without panic and without hiding it.
- 3 min
Reports drafted by AI
Using AI to draft a report's narrative, then matching every number against the data.
Questions about this course
Learning
How long does Social media analytics take?
About 63 minutes in all: 5 units made of 21 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; Social media analytics 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.
- 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.
- 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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