One student, one mentor, learning to turn raw data into decisions: cleaning data in Excel, pulling it with SQL, building dashboards in Power BI or Tableau, and processing it with Python. Learn through real datasets, online from anywhere.





A data analytics course is private one-on-one guidance to learn how to analyze data, from cleaning and tidying data, pulling it from a database with SQL, building visual dashboards, to processing it with Python. The goal is one thing: turning raw numbers into insight that supports decisions. Material is tailored to your goal, whether switching careers into a data analyst role, adding a skill in your current job, or reading your own business data. Mentors are analysts and graduates of Statistics and Information Systems, starting from Rp 100,000 per session.
Whatever the format, the same curriculum carries you from raw data to a dashboard that tells a story.
Learn via Zoom with screen sharing, practicing directly in the same tool as your mentor.
A mentor comes to your home for direct guidance in select cities.
Learn with 2-3 colleagues or friends, sharing case studies and discussion.
Four progressive stages, from spreadsheets to analysis with Python.
Master cleaning, filtering, and summarizing data in Excel or Google Sheets. The foundation that makes every later stage feel logical.
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Learn to pull data straight from a company database with SQL, where real data is stored in large volumes.
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Turn tables of numbers into interactive dashboards with Power BI, Tableau, or Looker Studio that decision-makers can read easily.
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Use Python and pandas to clean large-scale data, combine multiple sources, and automate repetitive work.
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Five pillars built up gradually, from foundation to advanced analysis.
Mentors adjust the emphasis to your goal. Job seekers focus on SQL, BI, and portfolio, business owners focus on spreadsheets and dashboards, aspiring data scientists add Python and statistics.
How to think about data and master Excel or Google Sheets as your first analysis tool.
Pulling and processing data directly from a database with the SQL language.
Building interactive dashboards with Power BI, Tableau, or Looker Studio.
Using Python and pandas for large and repetitive data work.
Statistics fundamentals so conclusions are correct, and how to present findings clearly.
Employees from non-data fields who want to move into a data analyst role, with a structured learning path and a real portfolio.
Recommended:
Students and new graduates who want to stand out in the job market with the data skills many companies seek.
Recommended:
Business owners who want to read their own sales and customer data, without always relying on others.
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Employees in marketing, finance, or operations who want to make data-driven decisions in their current role.
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A data analyst does not rely on a single tool. They pick the right tool for each task. These are the five core tools you will master gradually, along with the role of each.
Every analyst's starting point. For tidying, filtering, and computing moderate amounts of data quickly.
When you use it: Initial exploration, pivot tables, and quick reports you can share right away.
Building a sales pivot by region, then highlighting the top three regions.
The language for pulling data straight from a company database, where real data is stored.
When you use it: When data is too large for a spreadsheet or spread across many tables.
Pulling a list of customers who have not bought again in the last 90 days.
Turning tables of numbers into interactive dashboards that decision-makers can read easily.
When you use it: When results need regular monitoring or presenting to the team and managers.
Building a sales dashboard that updates automatically every week.
For cleaning large-scale data, combining many sources, and automating repetitive work.
When you use it: When a task is too complex or repetitive to do manually in a spreadsheet.
Merging 12 monthly files into one clean dataset automatically.
The way of thinking that keeps conclusions correct and free of coincidence or misreading.
When you use it: When comparing groups, testing a hunch, or reading a trend.
Testing whether the sales rise after a promo is truly significant.
Most analysts start with spreadsheets, then add SQL and one visualization tool, and only touch Python when it is truly needed. In the consultation session, the mentor helps choose the learning order to fit your target role and the time you have.
Data analysis is not a single thing. It is layered, from simply understanding the past to recommending action. Understanding these four levels helps you know where you stand and where you are headed.
What happened?
Summarizes past data into a clear picture: totals, averages, and key trends.
Sales this month dropped 12 percent compared to last month.
Why did it happen?
Digs deeper to find the cause behind the numbers that appear.
The drop happened because one region ran out of stock for two weeks.
What will happen?
Uses past patterns to estimate what is likely to happen next.
If the trend holds, that region's stock will run out again by month-end.
What should be done?
Translates findings into action recommendations you can run immediately.
Add 20 percent stock to that region and ship earlier before month-end.
The further right, the higher the value and complexity
Most data analyst work sits at the descriptive and diagnostic levels, and both are already highly valuable to companies. The predictive and prescriptive levels become added value as experience grows. We teach all four gradually, starting from the most commonly used.
The mistakes we see most often, and how mentors train students to avoid them.
Why it happens
Two numbers rising together look like they influence each other, when it may be coincidence or both driven by a third factor.
How we fix it
Mentors train students to read context and test the relationship before concluding, the habit that separates a mature analyst from a beginner.
Why it happens
Beginners are tempted to make charts first, when data often has duplicates, messy date formats, and empty cells.
How we fix it
We instill the habit of cleaning and checking data as the first step, before a single chart is made.
Why it happens
Metrics like view counts feel impressive, but may not connect to the actual business goal.
How we fix it
Mentors guide students to pick metrics that truly answer the business question, moving past ones that merely look nice.
Why it happens
The wrong chart, such as a pie chart for too many categories, confuses readers or leads to misreading.
How we fix it
We teach pairing the data type with the right chart, so the message lands in a single glance.
Mentors used to working with data guide you one-on-one, from tidying a spreadsheet to building a dashboard and a portfolio.
You set the pace. Ask and practice as much as you like, with every error discussed right away, no waiting for a class.
Practice with data that resembles the working world, so skills are immediately relevant when applying or working.
From spreadsheet, SQL, visualization, to Python, complete in one coherent learning path.
Your learning is woven into an analysis portfolio you can show to recruiters, going well beyond a certificate.
Costs are clear from the start, per session, with no long package required upfront.
Learn online from anywhere on a schedule that fits your work or study routine.
Our mentors are graduates of Statistics, Information Systems, and Computer Science from leading campuses, used to working with real data and teaching it in down-to-earth language.

Statistics, Universitas Negeri Yogyakarta
From raw data to insight“A Statistics graduate from Universitas Negeri Yogyakarta who gets students into the habit of cleaning and tidying data before drawing conclusions, a foundation beginners often skip.”

Information Systems, Universitas Negeri Semarang
SQL and databases“With a background in Information Systems at Universitas Negeri Semarang, Atma teaches how to pull data straight from a database with SQL, a core skill of an analyst.”

Business Statistics, Institut Teknologi Sepuluh Nopember
Analytics for business decisions“Anggita, a Business Statistics graduate from Institut Teknologi Sepuluh Nopember, is used to connecting numbers to real business questions so analysis ends in a decision.”

Statistics, Politeknik Statistika STIS
Down-to-earth statistics“From Politeknik Statistika STIS, Vina explains statistical concepts in everyday language, so students grasp the meaning behind averages, correlation, and significance.”

Statistics, Universitas Islam Indonesia
Visuals that tell a story“Nabila, a Statistics graduate from Universitas Islam Indonesia, trains students to pick the right chart so the data speaks clearly while moving past charts that merely look busy.”

Statistics, Universitas Terbuka
Starting from the spreadsheet“Drawing on her Statistics studies at Universitas Terbuka, Kintan starts from the spreadsheet most people already know, then steps up to pivots, formulas, and simple dashboards.”
Honest experiences from EduPoint students, from career switchers to business owners who finally understand their data.
I came from marketing and wanted to move into data analyst work. My mentor laid out a path from Excel, SQL, to Power BI with real case studies. Now I dare to apply for analyst roles with my own portfolio.
Prasetyo L.
Career switcher from marketing • Jakarta
As a fresh graduate, I needed a skill that set me apart from other applicants. Learning SQL and visualization made my CV more noticed. My mentor patiently explained concepts that once felt complex.
Vallerie T.
Fresh graduate • Surabaya
I run an online store with lots of data, but it was just a pile of numbers. After learning pivots and dashboards, I can see which products sell and when it gets busy. Stock decisions are now far more accurate.
Pak Zaenal A.
Online store owner • Bandung
In a finance job, I often have to process large data. Learning Power BI turned monthly reports that used to take days into something automatic. My manager was amazed by the dashboard.
Debby A.
Finance staff • Tangerang
I am a statistics student and wanted something more practical. The SQL and Python for analytics material complemented campus theory. Now I know how to apply it to real data, well beyond exam questions.
Dinda R.
Statistics student • Jogja
I learned by myself from videos but kept getting stuck and confused about the order. With a private mentor, my path became clear and every error was discussed right away. My progress was much faster.
Caesar R.
Upskilling employee • Bekasi
As a manager, I do not need to be an analyst, but I want to understand my team's reports. The mentor tailored the material to my work context. Now data-driven meetings make far more sense.
Ibu Desy P.
Team manager • Semarang
I am in my final year preparing to enter the data world. What I love is that the mentor has me practice with real datasets, far from made-up examples. Learning feels relevant.
Naufal R.
Final-year student • Malang
Transparent pricing with no hidden fees. Pick the package that fits your goal.
Free mentor replacement if not a match within the first 2 sessions.Great to get to know your mentor and start from data foundations.
Rp 105.000/session • 4x pertemuan
Valid 1 month
The most popular choice for consistent weekly learning.
Rp 100.000/session • 8x pertemuan
Valid 2 months
Comprehensive preparation to switch careers into a data analyst role.
Rp 95.000/session • 16x pertemuan
Valid 4 months
Prices may adjust to your learning goal, location, and lesson format. Contact us for a firm quote.
Online data analytics lessons are available for students across Indonesia and abroad.
In-person data analytics lessons, with a mentor visiting your home in these cities.
Real journeys of EduPoint students from learning to building a career with data.
The questions that come up most before starting, answered honestly.
Other programs that might be suitable for you
A free consultation to set your goal, match a mentor, and plan your path from spreadsheet to dashboard and portfolio.