#1 Data Analytics Course
Data Analytics Lessons, from Spreadsheet to Dashboard
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.
70+
tutors
550+
students
4.9
rating
25+
cities
What is Data Analytics Course?
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.
- One student, one mentor, with material matched to your goal
- Learn with real-world datasets that go beyond theoretical examples
- Master the analyst stack: spreadsheet, SQL, BI, and Python
- Build an analysis portfolio you can show to recruiters
- Suitable for beginners with no technical background
Services
Learn Data Analytics Online, Private, or with Colleagues
Whatever the format, the same curriculum carries you from raw data to a dashboard that tells a story.
Online Data Analytics Lessons
Learn via Zoom with screen sharing, practicing directly in the same tool as your mentor.
Rp 100.000/session
60-90 minutes
- Hands-on practice while guided by a mentor
- Sessions recorded for replay
- Digital datasets and exercises
- Available for students across Indonesia
In-Person Data Analytics Lessons
A mentor comes to your home for direct guidance in select cities.
Rp 130.000/session
60-90 minutes
- Mentor visits your home
- Direct guidance in front of the screen
- Great for those who need distraction-free focus
- Flexible schedule that suits you
Small-Group Data Analytics Lessons
Learn with 2-3 colleagues or friends, sharing case studies and discussion.
Rp 85.000/session
90 minutes
- 2-3 students per group
- Great for a team learning together
- Lower cost per student
- Case studies worked on together
Stages of Learning Data Analytics
Four progressive stages, from spreadsheets to analysis with Python.
Beginner (Spreadsheet & Data Basics)
Never seriously worked with dataMaster cleaning, filtering, and summarizing data in Excel or Google Sheets. The foundation that makes every later stage feel logical.
Subjects:
Cleaning and tidying dataKey formulas and functionsPivot tablesBasic charts
Focus Areas:
- Thinking structurally about data
- Building quick reports
- Highlighting key findings
- The habit of checking data
SQL & Database
Comfortable with spreadsheetsLearn to pull data straight from a company database with SQL, where real data is stored in large volumes.
Subjects:
SELECT, WHERE, and filteringJOIN across tablesGROUP BY and aggregationBasic subqueries
Focus Areas:
- Pulling exactly the data you need
- Combining multiple tables
- Answering business questions with queries
- Working with large data
Visualization & BI
Ready to build dashboardsTurn tables of numbers into interactive dashboards with Power BI, Tableau, or Looker Studio that decision-makers can read easily.
Subjects:
Choosing the right chart typeBuilding dashboardsFilters and interactivityData storytelling
Focus Areas:
- Dashboards that are easy to read
- Visuals that convey the message
- Presenting findings to the team
- Automated, tidy reports
Python for Analytics
Want to level upUse Python and pandas to clean large-scale data, combine multiple sources, and automate repetitive work.
Subjects:
Python basics for datapandas for data manipulationAutomated data cleaningVisualization with Python
Focus Areas:
- Processing complex data
- Automating repetitive tasks
- Reproducible analysis
- A foundation toward data science
Data Analytics Course Curriculum
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.
Data Foundations & Spreadsheets
How to think about data and master Excel or Google Sheets as your first analysis tool.
- Cleaning and tidying data
- Formulas, functions, and pivots
- Initial data exploration
- Quick reports
SQL & Database Processing
Pulling and processing data directly from a database with the SQL language.
- Basic queries and filters
- JOIN and table relations
- Aggregation and grouping
- Real query case studies
Data Visualization & Dashboards
Building interactive dashboards with Power BI, Tableau, or Looker Studio.
- Choosing the right chart
- Building dashboards
- Interactivity and filters
- Storytelling with data
Python for Data Analysis
Using Python and pandas for large and repetitive data work.
- Python basics for analytics
- pandas for data manipulation
- Automating data cleaning
- Reproducible analysis
Statistics & Data Storytelling
Statistics fundamentals so conclusions are correct, and how to present findings clearly.
- Descriptive statistics
- Correlation and significance
- Building a data narrative
- Presenting insight to decision-makers
Who It Is For
Career Switchers, Students, Business Owners: All Begin with the Same Data
Career Switchers
Employees from non-data fields who want to move into a data analyst role, with a structured learning path and a real portfolio.
Recommended:
SQL, BI, and portfolio trackCase studies like real work
Students & Fresh Graduates
Students and new graduates who want to stand out in the job market with the data skills many companies seek.
Recommended:
Spreadsheet-to-SQL foundationBuilding a portfolio early
Business & SME Owners
Business owners who want to read their own sales and customer data, without always relying on others.
Recommended:
Focus on spreadsheets and dashboardsCase studies from your own business data
Upskilling Professionals
Employees in marketing, finance, or operations who want to make data-driven decisions in their current role.
Recommended:
Emphasis on analysis and visualizationMaterial tailored to your work context
A Data Analyst Toolbox
The Tool Stack You Will Master
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.
Spreadsheet (Excel & Google Sheets)
FoundationEvery 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.
Sample task
Building a sales pivot by region, then highlighting the top three regions.
SQL
CoreThe 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.
Sample task
Pulling a list of customers who have not bought again in the last 90 days.
Visualization & BI (Power BI, Tableau, Looker Studio)
CoreTurning 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.
Sample task
Building a sales dashboard that updates automatically every week.
Python (pandas)
AdvancedFor 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.
Sample task
Merging 12 monthly files into one clean dataset automatically.
Statistics
SupportingThe 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.
Sample task
Testing whether the sales rise after a promo is truly significant.
You Do Not Need to Master Everything at Once
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.
From What Happened to What Should Be Done
The Four Questions Data Analysis Answers
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.
1
Descriptive
What happened?
Summarizes past data into a clear picture: totals, averages, and key trends.
Real example
Sales this month dropped 12 percent compared to last month.
Powered by:
SpreadsheetBI Dashboard
Business value: Understanding the situation
2
Diagnostic
Why did it happen?
Digs deeper to find the cause behind the numbers that appear.
Real example
The drop happened because one region ran out of stock for two weeks.
Powered by:
SQLPivot & filter
Business value: Finding the cause
3
Predictive
What will happen?
Uses past patterns to estimate what is likely to happen next.
Real example
If the trend holds, that region's stock will run out again by month-end.
Powered by:
PythonStatistics
Business value: Estimating the future
4
Prescriptive
What should be done?
Translates findings into action recommendations you can run immediately.
Real example
Add 20 percent stock to that region and ship earlier before month-end.
Powered by:
Insight + business context
Business value: Recommending action
The further right, the higher the value and complexity
Most of the Work Lives in the First Two Levels
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.
From Working with Real Data
Four Traps That Often Catch Beginner Analysts
The mistakes we see most often, and how mentors train students to avoid them.
Treating correlation as causation
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.
Analyzing data that is still dirty
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.
Chasing numbers that look good but mean little
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.
Choosing the wrong chart type
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.
Why Us
Learning with Real Data That Goes Beyond Theoretical Examples: That Is What Sets This Data Analytics Course Apart
Mentors used to working with data guide you one-on-one, from tidying a spreadsheet to building a dashboard and a portfolio.
One Student, One Mentor
You set the pace. Ask and practice as much as you like, with every error discussed right away, no waiting for a class.
Learn with Real Datasets
Practice with data that resembles the working world, so skills are immediately relevant when applying or working.
Master the Whole Analyst Stack
From spreadsheet, SQL, visualization, to Python, complete in one coherent learning path.
Ends in a Portfolio
Your learning is woven into an analysis portfolio you can show to recruiters, going well beyond a certificate.
Transparent Per-Session Pricing
Costs are clear from the start, per session, with no long package required upfront.
Flexible and Online
Learn online from anywhere on a schedule that fits your work or study routine.
Selected Data Analytics Mentors
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.
Tutor Qualifications
- Graduates of Statistics, Information Systems, and Computer Science from leading campuses
- Used to working with real data, from spreadsheets to SQL and Python
- Teach analysis through applied case studies that go beyond theory alone
- Guide from zero to having a portfolio ready to show
Ella P.
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.”
Atma R.
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.”
Anggita A.
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.”
Vina V.
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.”
Nabila N.
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.”
Kintan K.
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.”
Testimonials
From Lost at a Spreadsheet to Confidently Reading Data
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
Data Analytics Course Pricing
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.Trial Package
Great to get to know your mentor and start from data foundations.
Rp 420.000
Rp 105.000/session • 4x pertemuan
Valid 1 month
- 4 lesson sessions
- Online or small group
- Setting your goal and learning path
- Mentor change available
Popular
Regular Package
The most popular choice for consistent weekly learning.
Rp 800.000
Rp 100.000/session • 8x pertemuan
Valid 2 months
- 8 lesson sessions
- Online or in-person
- Spreadsheet, SQL, to basic BI
- Practice with real datasets
- Mentor change available
Career & Portfolio Package
Comprehensive preparation to switch careers into a data analyst role.
Rp 1.520.000
Rp 95.000/session • 16x pertemuan
Valid 4 months
- 16 lesson sessions
- Online or in-person
- SQL, BI, to Python for analytics
- Portfolio-building guidance
- Support until ready to apply
Prices may adjust to your learning goal, location, and lesson format. Contact us for a firm quote.
Data Analytics Course Availability
Online Tutoring
Online WorldwideOnline data analytics lessons are available for students across Indonesia and abroad.
- Learn directly on screen with screen sharing
- Sessions recorded for replay
- Digital datasets and exercises
In-Person Tutoring
All CitiesIn-person data analytics lessons, with a mentor visiting your home in these cities.
Jakarta16+ tutorsBandung10+ tutorsSurabaya9+ tutorsDepok7+ tutorsTangerang7+ tutorsBekasi6+ tutorsJogja8+ tutorsSemarang6+ tutors
+4 more citiesStudent Achievements
Careers Built Through Data
Real journeys of EduPoint students from learning to building a career with data.
Success
From a Non-IT Graduate to Junior Data Analyst
Aquina W.
Success
From Admin Staff to Internal Data Analyst
Septian Z.
Success
An SME Owner Who Finally Reads Her Own Data
Veronica D.
FAQ
Confused About Tools, Coding, or Data Analytics Course Cost? Here Are the Answers
The questions that come up most before starting, answered honestly.
Complete Guide
Complete Guide to Learning Data Analytics
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Start Turning Data into Decisions Today
A free consultation to set your goal, match a mentor, and plan your path from spreadsheet to dashboard and portfolio.
Free consultation, no commitmentLearn with real datasetsMatch guaranteed in the first 2 sessions