Online Data Science 1-on-1
Online Data Science Tutoring from Your First Python Command to a Job-Ready Portfolio
One student is accompanied by one data-practitioner mentor in a private video session matched to their goal. The Jupyter or Google Colab screen is shared, then student and mentor write code side by side line by line, from cleaning data to training a model, and every meeting is captured for replay while working on projects. All from home, at an open hourly rate.
3,500+
tutors
70,000+
students
4.9
rating
60+
cities
What is Online Data Science Tutoring?
Online data science tutoring pairs one student with one data-practitioner mentor in a live video session. The mentor we match to the need spreads a structured data science module across the screen, guiding the student to read a dataset, choose an analysis approach, and build a prediction model. Once code writing begins, the Jupyter Notebook or Google Colab screen is shared so every Python command, every pandas function, and every scikit-learn step shows clearly, and the student types it on their own device. Notebooks and session recordings are kept in the app to reopen. The emphasis stays on seeing one data project through in full, from raw data to a finished model, so the ability genuinely settles and stands out in a portfolio.
- Online mentors who are active data practitioners and graduates of Statistics, Computer Science, and Data Science at leading universities
- Pair coding with screen sharing in Jupyter and Colab, every line of Python seen then mirrored
- Structured data science modules opened together, notebooks reopenable in the app
- Every session recorded, the steps of building and evaluating a model can be replayed
- Project progress tracked per module and reported regularly via WhatsApp
Services
Three Ways to Take Online Data Science, One Depth of Practice
Full private, small group, or intensive career portfolio. The flow of processing data and building models is guided with the same depth through pair coding and screen sharing.
Private Online Data Science
One student one mentor over video, notebooks and code written together on screen.
Rp 115.000/session
90-120 minutes
- Full attention, one student one mentor
- Pair coding with screen sharing in Jupyter and Colab
- Recorded sessions to replay
- No transport cost, more affordable
Small-Group Online Data Science
Take on online data science together with 2-3 friends through one shared analysis project, with a lighter cost per student.
Rp 90.000/session
120 minutes
- Maximum 3 students per group
- Team project with a shared dataset
- More affordable cost per student
- Practising Git and sharing notebooks
Online Data Science Portfolio Intensive
Focused on building a complete data project for a career portfolio, fully guided through screen sharing.
Rp 130.000/session
120 minutes
- End-to-end project from raw data to model
- Support in assembling a GitHub repository
- Practice explaining results to an interviewer
- Recording to replay while polishing the project
Online Data Science Learning Stages
Material and teaching pace are set to your starting point and goal, all through guided online sessions led by pair coding and recorded.
Python & Data Foundations for Total Beginners
Never written code beforeA first meeting with Python and the habit of reasoning through data, carried out while sharing a screen. The mentor opens Google Colab alongside the student, guiding early syntax and table-data work with pandas, and the session recording can be revisited at home so no step slips by.
Subjects:
Python BasicsNumPy & pandasReading CSV and Excel DataWorking in Jupyter & Colab
Focus Areas:
- Comfortable writing Python over the screen
- Processing table data with pandas
- Understanding data types and structures
- Keeping notebooks tidy
Data Analysis & Visualisation
Uncovering the story the data holdsTaming messy data then combing through its patterns via exploratory data analysis, all worked out together on screen. The student practises pouring findings into plain charts, and each stage can be played back from the recording as a deadline nears.
Subjects:
Data CleaningExploratory Data AnalysisMatplotlib & SeabornDescriptive Statistics
Focus Areas:
- Cleaning real-world data
- Finding patterns and anomalies
- Building charts that tell a story
- Drawing conclusions from data
Machine Learning
Making data predictStepping up to modelling with scikit-learn, from training prediction models to evaluating their performance. The mentor guides each code cell through screen sharing before the student runs it themselves, and sessions are saved to replay while projects are underway.
Subjects:
Supervised LearningRegression & Classificationscikit-learn WorkflowModel Evaluation
Focus Areas:
- Training a first prediction model
- Choosing the right algorithm
- Measuring accuracy correctly
- Avoiding overfitting
Project & Job-Ready Portfolio
Work you can show offBinding every skill into one full data project, from raw data toward findings and a model, then laid out neatly as a GitHub portfolio. Suited to anyone gearing up to apply for a data job or internship, since each stage is worked while sharing a screen and stored as a recording.
Subjects:
End-to-End ProjectPortfolio & GitHubCommunicating ResultsData Interview Prep
Focus Areas:
- A whole data project sitting in your portfolio
- A tidy GitHub for recruiters
- Explaining results to non-technical people
- Interview and internship preparation
Online Data Science Curriculum
Our online data science material is built step by step and project-based, from Python foundations to building a machine learning model that runs.
The mentor aligns the online module with your goal, from a focus on data analysis to a larger share of machine learning modelling for those aiming at a data scientist role.
Python for Data
Laying the groundwork in Python and the way to move table data with pandas, written turn by turn over the screen in Jupyter and Colab.
- Python Syntax and Data Structures
- NumPy for Numerical Computing
- pandas: DataFrames and Data Operations
- Reading Various Data Sources
Analysis & Visualisation
Tidying, tracing, then picturing data so it can tell a clear story, discussed while sharing the screen.
- Data Cleaning and Missing Values
- Exploratory Data Analysis
- Matplotlib and Seaborn
- Descriptive Statistics and Correlation
Machine Learning
Building prediction and classification models then evaluating them correctly, each code cell guided by the mentor over video.
- Supervised Learning: Regression and Classification
- scikit-learn Workflow
- Train-Test Split and Cross-Validation
- Evaluation Metrics and Basic Tuning
Project & Portfolio Through Screen Sharing
Tying every ability into one complete, ready-to-show data project, from the notebook to a well-arranged GitHub repository.
- A Full Data Project Built on Genuine Data
- Git and GitHub for Data
- Storytelling and Presenting Results
- Portfolio and Interview Preparation
Who It Is For
Four Profiles Learning Data Science Online Here
Workers hoping to cross into the data field, students preparing a portfolio, professionals wanting to make number-based decisions, to school learners curious about data, all can pursue data science from home with a mentor chosen to fit their goal.
Career Switchers into Data
Workers from a different field who want to cross into the data world, aided by online sessions whose hours shift around a job plus recordings to go over material after office hours.
Recommended:
A learning map from Python to machine learningPriority on assembling a portfolio
Students & Fresh Graduates
Students from a range of study programs chasing applied data skills, and graduates assembling a portfolio to pursue a data role, all accompanied over the screen from whichever city they are in.
Recommended:
Real projects for a GitHub repositoryGuidance on final-project data work
Professionals Wanting Data Fluency
Staff who want to rest their decisions on figures, trim the repetitive part of analysis work, or enrich the data ability in their present post, learning limberly from home between work hours.
Recommended:
Weight on applied analysis and chartsCases drawn from one's own line of work
Learners Curious About Data
Senior high and vocational teens intrigued by data technology early on, keen to get acquainted with Python, or building a footing before stepping into computing and data study programs.
Recommended:
Setting off from Python and data reasoningLight projects to match an interest
Featured
Track Your Data Project Progress from Anywhere
Through the EduPoint Learning System, the trail of each online meeting is documented. Co-learning modules are spread out together on screen, notebooks and the modelling flow are captured, then we draw a map of the skills already firm and those still needing practice, and the summary reaches your hands.
Report Features
Co-Learning Modules Opened Together
Mentor and student open the same structured data science module on screen, from pandas practice sheets to model-building guides, and the notebook is saved in the app to reopen.
Skill Map per Module
See the student's mastery in each module, from Python and data analysis to machine learning, through the app.
Recorded Model-Building Steps
Long steps of training and evaluating a model are recorded, so they can be replayed while the student polishes a project or prepares for an interview.
Regular Progress Reports
A summary of online learning progress and portfolio growth is sent regularly via WhatsApp, so the direction of study stays visible even when sessions run from a screen.
Sample Progress Report
Farrel D.
Career switcher • May 2026
68%
Overall Progress
Sessions Completed
12 of 20
Next Milestone
Finish an end-to-end classification project for the portfolio
Jun 1, 2026
Python & pandas
On Track86/90
Table-data work is fluent, starting to write tidy functions
Exploratory Data Analysis
Improving78/85
Visualisation runs well, reading correlation still needs practice
Machine Learning
Needs Attention68/82
Often forgets to split train and test data before training a model
Portfolio Project
Improving72/85
First project runs, GitHub documentation needs tidying
Notes from Thousands of Online Data Science Sessions
The Online Data Science Hurdles We Resolve Most Often
Data science over a screen has its own challenges, from the temptation to copy code without understanding to picking things up in disconnected fragments and never seeing a project through. Here are five hurdles we most often meet in online data science sessions and how we handle them.
Copying code from tutorials without understanding it
Why it happens
The student pastes snippets that run without grasping what each line does.
How we fix it
Through pair coding we pause at each key cell, tracing what is changed in the data and why, before moving on, so the student can rewrite the code themselves without copying.
Picking things up in disconnected fragments and never finishing a project
Why it happens
The student watches Python in one place and machine learning in another until they freeze when facing real data.
How we fix it
We guide one whole flow from raw data to model within a single project, through screen sharing, so the skills fuse and show up in a portfolio.
Treating the online session as passive viewing
Why it happens
The student only watches the mentor type without opening a notebook on their own device.
How we fix it
We ask the student to open Jupyter or Colab themselves and mirror each step in real time, so their coding skill is truly built through their own hands.
Training a model without splitting train and test data
Why it happens
The student rushes to chase high accuracy without realising the model is merely memorising the data.
How we fix it
We build the habit of splitting train and test data and using cross-validation first on screen, so the accuracy figure is honest and the model works on new data.
Afraid to start, feeling one must be great at maths
Why it happens
The student delays learning, assuming data science demands advanced mathematics.
How we fix it
We start from plain logic and applied statistics, reinforcing concepts exactly when a project needs them, so the student moves forward unburdened by formulas not yet required.
Because every session is recorded and its notebook saved, students can replay the model-building steps until they can do them on their own. Wrapping up one small project often teaches lessons that outrun hours of tutorial-watching.
Why Us
What Makes Our Online Data Science Tutoring Feel Different
A data-practitioner mentor who guides directly, pair coding while sharing the screen, and recordings that can be revisited any time, all come together in one monitored online session that feels far more accompanied than watching a video course alone.
Live 1-on-1 with a Real Mentor
A student draws data science straight from one chosen mentor in a private video session, the conversation flowing both ways, with room to ask the moment an error message or line of code turns murky.
Pair Coding Through Screen Sharing
The mentor writes code in Jupyter or Colab on a shared screen, then the student mirrors it on their own device in real time, so data science theory turns into a hands-on skill.
Learning Based on Real Projects
Every module rests on genuine data and ends in analysis or a model worthy of the portfolio, so the student draws on knowledge while doing the work directly on screen.
Notebooks & Recordings You Can Replay
Long steps of training a model or cleaning data are saved as notebooks and recordings, watchable again whenever the student needs them.
No Distance Limit, Savings on Transport
A data practitioner from a big city can still accompany a student living anywhere, and the wallet breathes easier with no fare to and from a tutoring venue.
Toward a Portfolio Ready to Show
Sessions are steered until the student holds a well-arranged data project on GitHub, a piece ready to present when applying for an analyst or scientist role in data.
Our Online Data Science Mentors
Our mentors arrive from among working data professionals along with hand-picked students and graduates in the Statistics, Computer Science, and Data Science streams at leading campuses, close to real data and at ease running a session while sharing a screen.
Tutor Qualifications
- Active data practitioners and graduates of Statistics, Computer Science, and Data Science from leading universities
- Cleared a technical-skill screen, a teaching trial, and a run-through of holding an online class
- Nimble writing code in Python, pandas, and scikit-learn, and adept at guiding it while sharing a screen
- Even-tempered, easy to talk to, and seasoned at accompanying data projects from scratch
Himawan H.
Statistics, Sepuluh Nopember Institute of Technology
Statistical foundations for modelling“Over video, Himawan links ideas of spread and probability to the data columns showing on screen, so a student understands why a method is chosen when they later build the model.”
Karina K.
Statistics, Sepuluh Nopember Institute of Technology
Preparing data before analysis“Karina has students comb through empty values and messy formats first on the shared screen, since it is this tidying-up step that decides the quality of the analysis that follows.”
Reta R.
Statistics, Gadjah Mada University
Turning figures into findings“Reta gets students used to saying aloud what a table really claims, then boiling it into a sentence a lay listener can follow, all examined together on screen until it is clear.”
Charles D.
Statistics, Brawijaya University
Charts that carry a message“Charles guides students in picking the chart shape that fits a data story, built right in each student's own notebook, so a single glance is enough to catch its point.”
Angger A.
Statistics, Diponegoro University
Writing Python code together“Angger leads students to type Python commands one at a time in pair coding, waiting for each line to run on the student's device, until they handle their own data without hesitation.”
Sony E.
Applied Data Science, Electronic Engineering Polytechnic Institute of Surabaya
Building prediction models“Grounded in applied data science, Sony accompanies students in assembling a model in scikit-learn on his shared screen, showing how a prediction is born from the data they just tidied.”
Testimonials
Stories from Those Who Chose Online Data Science
From workers switching careers to students building a first portfolio, here is their experience learning data science over a screen.
The word coding used to sound frightening to someone with an accounting background like me. In the video sessions, the mentor patiently typed alongside me in Colab, so I pressed the keys myself while the mentor guided each move. Three months in, I dared to put my first project in front of a prospective employer.
Reynald T.
Switching career from accounting • Jakarta
Before this I watched random video courses and gave up halfway easily. The online sessions were different; someone waited for me to finish each code cell and corrected me when I slipped. My first simple prediction model finally ran.
Prisca W.
Information Systems undergraduate • Bandung
The data-work chapter of my final project stalled while my supervisor was on another island. Over the shared screen, the mentor accompanied me from choosing an approach to checking the model output, and I replayed the recording the night before my defense. My presentation flowed.
Fikri A.
Final-year student • Makassar
As a sales team lead, I was tired of waiting on reports from others. The afternoon video sessions focused on reading our own sales figures. Now I put together weekly charts without depending on anyone.
Damar S.
Worker wanting data fluency • Tangerang
My work shifts keep moving, so sessions I can reschedule were a real help. Every notebook is saved in the app, and if I forget a step I just play back the recording. Little by little I have grown fluent with pandas.
Okta R.
Shift worker • Depok
There is no data community at all where we live. Thanks to video, I learned from a practitioner in Java without moving cities. From writing code side by side, I completed one whole classification model.
Gilang P.
Fresh graduate • Kupang
My child is in a final semester and aiming for a career in data technology. The mentor guided from the earliest syntax to a genuine project, and every fortnight sent me a note of his progress. He is now more sure about applying for an internship.
Ratih K.
Parent of a university student • Yogyakarta
What keeps me going is that every meeting we handle real data, not made-up examples. Once a module wraps up, there is an analysis result I save straight to GitHub. It feels like learning that leaves a trace.
Selin H.
Switching career from marketing • Medan
Reach & Flexibility of Online Data Science Tutoring
Online Tutoring
Online WorldwideThis service reaches students all across the archipelago, from provincial capitals to areas far from any data community, even families settled overseas for a while. Hand-picked data-practitioner mentors can be present without a map drawing the limit, keeping pace with a family's daily rhythm and a busy work or class schedule.
- Nationwide reach, from big cities to remote areas
- Seven-day schedule within the 06:00-23:00 WIB range
- Just a laptop with a stable connection; Google Colab ready when more computing is needed
- Co-learning modules, pair coding in Jupyter and Colab, and notebooks and recordings saved in the app
Student Achievements
Success Stories of Online Data Science Students
Real, measurable change from students who learned data science online consistently over the screen.
Success
From Admin to Data Analyst Through Online Tutoring
Anisa R.
D3 graduate, formerly an administrative staffer
Before
40
After
88
+48 data-readiness points
8 months
Accepted at
First job in data
“I started from total zero over online sessions. Accompanied in writing Python, data work, and SQL together on screen, my portfolio slowly took shape until I was finally hired as a data analyst.”
Tutors:
Angger A.
Success
A Machine Learning Portfolio Before Graduation
Ismail H.
Final-year non-informatics student
Before
45
After
90
+45 points
10 months
Accepted at
Data internship before graduation
“Learning while working on projects through screen sharing had me finish four full data pieces, one of them a prediction model. My GitHub repository stood out and I landed a data science internship straight away.”
Tutors:
Sony E.
Success
From Confused About the Order to a First Model That Runs
Raka P.
Career switcher, outside Java
Before
50
After
92
+42 points
6 months
Accepted at
Confident applying for a data role
“I used to learn in scattered pieces from random videos and never finish. In the online sessions, the mentor guided one whole flow until my model ran, and it all finally connected.”
Tutors:
Reta R.
FAQ
Unsure About Online Data Science Tutoring? Here Are the Answers
Whether learning online truly bites, the app used, how pair coding works on screen, the maths requirement, how loose the schedule is, the ways to pay, right through to changing mentors, every answer is laid out briefly below.
Guides & Tips
Supporting articles to help you decide and maximize learning outcomes.
Data Science Learning Roadmap From Scratch for Beginners Until Your First Project
Seven sequential stages that carry a complete beginner from statistics and Python foundations to training your own machine learning models and weaving them into one end-to-end project.
How to Build a Data Science Portfolio for Beginners
Recruiters judge skill from work they can see. A strong portfolio holds projects that answer real questions, are neatly documented, and reveal how you think.
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Help Your Student Conquer Data Science from Home, Through Online Tutoring
Share the starting point and the goal, and our team will track down an online data science mentor who can guide coding through screen sharing and accompany the project until the portfolio is ready to show. Free consultation.
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