#1 Data Science Tutoring
Private Data Science Tutoring, From Zero to Job-Ready
One student, one working data practitioner as your mentor. Learn Python, data analysis, visualization, and machine learning through real projects until you have a portfolio you can be proud of. In person or online, starting from Rp 120.000 per session.
180+
tutors
1,400+
students
4.9
rating
50+
cities
What is Data Science Tutoring?
Data science tutoring is one-on-one private guidance for learning how to turn data into decisions and predictions. The material covers Python for data, data cleaning and analysis, visualization, applied statistics, and machine learning. Learning is project-based with real datasets, guided by working data practitioners, with fees starting from Rp 120.000 per session, online or in person.
- One student, one mentor, with the learning pace following your speed
- From basic Python all the way to complete machine learning
- Learn through real projects with genuine datasets, going beyond theory
- Every module produces an analysis for your portfolio
- Suitable for complete beginners and for those switching careers into data
Data Science Tutoring Service Options
Choose the learning format that fits your schedule and learning style best.
In-Person Data Science Tutoring
The mentor comes to your home for hands-on guidance while you write code and analyze data.
Rp 150.000/session
90-120 minutes
- Mentor comes to your home
- Hands-on guidance while coding and analyzing
- In-person notebook review
- Flexible schedule that suits you
Online Data Science Tutoring
Learn via Zoom with screen sharing and live notebook review, perfect for any city.
Rp 120.000/session
90-120 minutes
- No transport costs, more affordable
- Two-way screen sharing and pair coding
- Session recordings you can replay
- Available for students living abroad
Small-Group Data Science Tutoring
Learn together with 2-3 friends on collaborative analysis projects at a lighter cost.
Rp 100.000/session
120 minutes
- 2-3 students per group
- Team projects with a shared dataset
- Lower cost per student
- Learn Git and notebook collaboration
Data Science Learning Stages
Four progressive stages, from your first line of Python to a running machine learning model.
Python & Data Foundations (Complete Beginner)
Never written code beforeGet to know Python and how to think with data. You learn basic syntax and start working with tabular data using pandas before moving on to analysis and models.
Subjects:
Python basicsNumPy and pandasReading data (CSV, Excel)Jupyter Notebook
Focus Areas:
- Comfortable writing Python code
- Working with tabular data
- Understanding data types and structures
- Working neatly in a notebook
Data Analysis & Visualization
Reading the story behind the dataClean up messy data and uncover patterns through exploratory data analysis. You learn to present findings with clear charts and basic statistics.
Subjects:
Data cleaningExploratory data analysisVisualization (Matplotlib, Seaborn)Descriptive statistics
Focus Areas:
- Cleaning real-world data
- Finding patterns and anomalies
- Creating charts that tell a story
- Drawing conclusions from data
Machine Learning
Making data predictStep up to modeling: building prediction and classification models with scikit-learn, then evaluating their performance. At this stage you understand the complete machine learning workflow.
Subjects:
Supervised learningRegression and classificationscikit-learnModel evaluation
Focus Areas:
- Training your first prediction model
- Choosing the right algorithm
- Measuring accuracy correctly
- Avoiding overfitting
Projects & Job Readiness
Work you can show offWork on a complete final data project from raw dataset to insights and model, then build your portfolio plus preparation for entering the workforce or a data internship.
Subjects:
End-to-end projectPortfolio and GitHubCommunicating resultsData interview preparation
Focus Areas:
- A complete data project in your portfolio
- A tidy GitHub for recruiters
- Able to explain results to non-technical people
- Interview and internship preparation
Data Science Tutoring Curriculum
Four core modules arranged step by step, all project-based with real datasets.
The mentor tailors the modules to your goals. Those who want to focus on analysis will go deeper into EDA and visualization, while those aiming for machine learning will add more modeling.
Python for Data
The foundations of Python and manipulating tabular data with pandas, every data scientist's main tool.
- Python syntax and data structures
- NumPy for numerical computing
- pandas: DataFrames and data operations
- Reading from various data sources
Analysis & Visualization
Cleaning, exploring, and visualizing data so it tells a clear story.
- Data cleaning and handling missing values
- Exploratory data analysis
- Matplotlib and Seaborn
- Descriptive statistics and correlation
Machine Learning
Building prediction and classification models and evaluating them correctly.
- Supervised learning: regression and classification
- scikit-learn workflow
- Train-test split and cross-validation
- Evaluation metrics and basic tuning
Projects & Portfolio
Weaving all the skills together into a complete, job-ready data project.
- End-to-end project with a real dataset
- Git and GitHub for data
- Storytelling and presenting results
- Portfolio and interview preparation
Who Is Data Science Tutoring For?
Career Switcher
Professionals from other fields who want to move into the data industry. The private format lets you learn between work commitments with clear targets and a real portfolio.
Recommended:
A roadmap from Python to machine learningA mentor who understands the transition into a data career
University Students & Fresh Graduates
Students from various majors who want practical data skills, as well as fresh graduates preparing a portfolio to apply as a data analyst or data scientist.
Recommended:
Focus on projects and a GitHub portfolioGuidance on data analysis for your thesis
Professionals Looking to Level Up
Employees who want to make data-driven decisions, automate analysis, or add analytical skills to their current job.
Recommended:
Focus on practical analysis and visualizationCase studies tailored to your line of work
High School & Vocational Students
Students interested in data and technology early on, who want to get to know Python, or are preparing to enter data- and computer-related majors.
Recommended:
Start with Python and data logicFun projects matched to the student's interests
Data Science Pipeline
The Data Science Workflow You Will Master
Every real data project moves through six stages. You practice all of them until you can run a data project from start to finish on your own.
01
Understanding the Problem
Turning business questions into data questions
Tools Used
Requirements discussionMetric definitionInitial hypothesis
Skills Developed
- Translating a problem into analysis goals
- Defining success metrics
- Recognizing the data needed
Stage Outcome: A clear, measurable data question before touching any code.
Collecting & Cleaning Data
02
Collecting & Cleaning Data
Preparing raw data so it is ready to analyze
Tools Used
pandasSQLAPI & CSV/Excel files
Skills Developed
- Pulling data from various sources
- Handling missing values and duplicates
- Merging and tidying up data formats
Stage Outcome: A clean, tidy dataset ready to explore.
Data Exploration (EDA)
03
Data Exploration (EDA)
Discovering patterns, trends, and anomalies
Tools Used
MatplotlibSeabornDescriptive statistics
Skills Developed
- Reading distributions and correlations
- Creating visualizations that tell a story
- Spotting outliers and hidden patterns
Stage Outcome: A deep understanding of what the data contains and where the analysis is heading.
Machine Learning Modeling
04
Machine Learning Modeling
Making data predict and group
Tools Used
scikit-learnFeature engineeringTrain-test split
Skills Developed
- Choosing a suitable algorithm
- Designing relevant features
- Training prediction and classification models
Stage Outcome: Your first machine learning model running on real data.
Evaluation & Interpretation
05
Evaluation & Interpretation
Making sure the model is correct and trustworthy
Tools Used
Evaluation metricsCross-validationConfusion matrix
Skills Developed
- Measuring performance with the right metrics
- Detecting overfitting
- Explaining why the model makes its predictions
Stage Outcome: A tested model and conclusions you can stand behind.
Communication & Deployment
06
Communication & Deployment
Sharing results so they have an impact
Tools Used
DashboardStorytellingGitHub
Skills Developed
- Crafting a data narrative for non-technical audiences
- Building a dashboard or report
- Documenting the project in your portfolio
Stage Outcome: Insights that decision makers understand and a project ready to show off.
Why Learn Through the Pipeline?
Many people learn data science in scattered pieces: Python in one place, statistics in another, machine learning from random videos. The result is confusion when facing real data. By practicing one complete workflow from problem to result, you get used to working on projects like a real data scientist. This is what recruiters look for and what makes your portfolio stand out.
Why Choose EduPoint Data Science Tutoring
Our private format is designed to produce real skills, going beyond a mere certificate.
One Student, One Mentor
You set the pace. Ask as much as you like without embarrassment, and move quickly without waiting for a class.
Working Data Practitioners
Learn from people who genuinely work on data projects, complete with real workplace practices.
Project-Based Learning
Every module uses a real dataset and produces a genuine analysis. You learn by doing, going beyond just watching.
Mastering the Complete Workflow
From understanding the problem, to processing data, to communicating results. No more disconnected pieces of material.
Job-Ready Portfolio
Graduate with a tidy data project on GitHub, ready to show to recruiters.
Transparent Per-Session Pricing
Clear costs from the start, beginning per session with no obligation to commit to a long package upfront.
Selected Data Science Mentors
Our mentors are working data practitioners as well as top students and alumni in statistics, computer science, and mathematics from leading Indonesian universities, experienced in guiding learners from scratch.
Tutor Qualifications
- Working data practitioners and alumni of Statistics, Computer Science, and Mathematics from leading universities
- Experienced in real data and machine learning projects
- Passed technical skills screening and a teaching test
- Skilled in the modern stack: Python, pandas, scikit-learn, plus SQL and visualization
Kak Mahfuzha I.
Statistics, IPB
Machine Learning & Python4.96 years experience
“Data science is not about memorizing formulas, it is about getting used to questioning the data and finding its answers.”
Kak Verry W.
Computer Science, UI
Data Analysis & Visualization4.95 years experience
“When a student first makes a chart that answers their own question, that is when data starts to feel alive.”
Kak Allisya I.
Mathematics, ITB
Statistics & Modeling4.95 years experience
“Statistics is the foundation. I help students understand the logic, going beyond just running a function.”
Kak Muhammad A.
Computer Science, UGM
Machine Learning & MLOps4.84 years experience
“A good model is not enough. I teach how to make it run and become useful to other people.”
Kak Nadia F.
Statistics, UNPAD
Data Analysis & SQL4.94 years experience
“Career switchers often hesitate to start. Yet with structured practice, they catch up quickly.”
Kak Alifia S.
Information Systems, ITS
Python & Data Engineering4.95 years experience
“I teach more than code; I teach how to think in a structured way when facing messy data.”
What Our Students Say
Real stories from those who learned data science with EduPoint.
I come from an economics background with zero coding experience. In 7 months of private tutoring, I now have 3 analysis projects in my portfolio and got accepted for an internship as a data analyst. My mentor was patient explaining Python from the basics.
Hansen H.
Career switcher from economics • Jakarta
Self-teaching from online courses left me confused about the order. With a mentor, the roadmap was clear from pandas to machine learning. Each session had a target and a small project I worked on right away.
Dewi M.
Statistics student • Bandung
My thesis needed data analysis and a prediction model, but I was stuck. The EduPoint mentor guided me from data cleaning to interpreting the results. My defense went smoothly because I truly understood the analysis.
Nadya O.
Final-year student • Surabaya
I am a manager who wanted to read data myself without always asking my team. The private tutoring focused on analysis and visualization suited to my work needs. Now I can build my own dashboards for reports.
Arif M.
Professional, looking to level up • Tangerang
My work schedule is packed, so evening online tutoring helped a lot. The sessions are recorded so I can replay them. Slow but consistent, and now I am comfortable with pandas and scikit-learn.
Cantika N.
Employee, learning while working • Depok
I enjoy the statistics side and my mentor steered me straight into modeling. Now I understand when to use regression or classification and how to evaluate them. It feels like opening up a new way of thinking.
Budi N.
Mathematics student • Semarang
My child is in university and wants to enter the data field. The mentor guided them from Python to real projects, and gave feedback diligently. They have become more confident applying for data internships.
Gina N.
Parent of a university student • Jogja
What I love is that the learning is genuinely project-based with real datasets. Each finished module produces a real analysis that goes into my portfolio. So learning feels productive, with no theory left hanging.
Vina S.
Career switcher from marketing • Medan
Data Science Tutoring Fees
Transparent pricing with no hidden costs. Choose the package that fits your goals.
Free mentor replacement guarantee if it is not a good fit within the first 2 sessions.Trial Package
Great for getting to know a mentor and finding the right learning focus.
Rp 500.000
Rp 125.000/session • 4x pertemuan
Valid 1 month
- 4 tutoring sessions
- Online or small group
- Defining your focus and learning roadmap
- Mentor replacement available
Popular
Regular Package
The most popular choice for consistent weekly learning.
Rp 960.000
Rp 120.000/session • 8x pertemuan
Valid 2 months
- 8 tutoring sessions
- In person or online
- A real analysis project for your portfolio
- Notebook review for each project
- Mentor replacement available
Career Package
A complete roadmap from Python to a job-ready data portfolio.
Rp 1.840.000
Rp 115.000/session • 16x pertemuan
Valid 4 months
- 16 tutoring sessions
- In person or online
- Complete from Python to machine learning
- An end-to-end data project for your portfolio
- Portfolio and data interview preparation
Prices may adjust based on your learning focus, location, and tutoring format. Contact us for an exact quote.
Data Science Tutoring Availability
Online Tutoring
Online WorldwideOnline data science tutoring is available for students across Indonesia and abroad.
- Two-way screen sharing and pair coding
- Sessions are recorded and can be replayed
- No transport costs
In-Person Tutoring
All CitiesIn-person data science tutoring, with the mentor coming to your home in the following cities.
Jakarta45+ tutorsBandung24+ tutorsSurabaya20+ tutorsDepok16+ tutorsTangerang15+ tutorsBekasi14+ tutorsBogor10+ tutorsSemarang10+ tutors
+4 more citiesStudent Achievements
Our Students' Career Journeys
Real stories from those who changed their career direction through data science.
Success
From Admin to Data Analyst
Anisa R.
Success
A Student with a Machine Learning Portfolio
Ismail H.
Success
A Professional Who Moved into a Data Role
Nisa N.
Questions About Data Science Tutoring
Answers to the questions prospective students ask most often.
Complete Guide
Complete Guide to Data Science Tutoring
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Start Data Science Tutoring Now
Free consultation with our team to find the learning focus and mentor that suit you best.
Free consultation with no commitmentWorking data practitionersGood-fit guarantee within the first 2 sessions