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Data Science

Sains Data

This study program combines mathematics, statistics, programming, and computing to turn data into analysis and accountable decisions.

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Level S1 · 4 years
Market demand High, and continuing to rise
Mid salary Rp 8–15 million /mo
01

Big Picture

DATA

Understand entire data lifecycle: formulate problem, collect and clean data, analyze patterns, build models, then communicate results for real decisions.

Core courses

  • ›Statistics and Probability
  • ›Python Programming
  • ›Databases
  • ›Data Mining
  • ›Machine Learning
  • ›Big Data
  • ›Artificial Intelligence
WHO THIS FITS

Good fit if you like math, logic, programming, and finding patterns in data. You also need to be comfortable explaining analysis results to non-technical people.

Education levels

how does it differ from other levels?
D4
Applied Data Science is an applied bachelor's program usually stronger in practice, projects, and job readiness; an S1 gives more room for theoretical foundations, analysis, and a research track.
S2
Master's deepens theory, methods, and data science research after an S1 or D4. Duration usually shorter than S1, with output being specialist expertise or a thesis, not a bachelor's foundation.

Often confused with…

what's the difference?
vsStatistika
Statistics emphasizes theory, inference methods, and statistical modeling; Data Science typically combines these with programming, data engineering, and computing systems.
vsInformatika
Informatics covers computing and software development broadly; Data Science focuses more on data processing, analysis, and modeling.
vsSistem Informasi
Information Systems focuses on business needs, organizational processes, and system implementation; Data Science focuses on data-driven insights and predictions.
vsMatematika
Mathematics goes deeper into mathematical structures and reasoning; Data Science applies mathematics and statistics to data using computation.

Myth vs Fact

MYTH Data Science only teaches you how to use analytics applications.
FACT You still learn mathematics, statistics, programming, databases, and model evaluation.
MYTH Graduates are guaranteed to become Data Scientists immediately.
FACT Graduates can also start in roles like Data Analyst, Business Intelligence Analyst, or junior Data Engineer.
MYTH No communication skills needed.
FACT Model results need to be explained with context, limitations, and decision impact.
MYTH All data can be used directly for models.
FACT Data often needs to be cleaned, validated, protected, and checked for bias.
02

Toolkit & Prep

GUIDE

Personal hardware & software you need to prepare before classes start — requirements vary by program. Expensive, heavy-duty equipment is usually provided by the campus.

MINIMUM LAPTOP SPEC

Laptop with minimum 4-core modern CPU, 16 GB RAM, 512 GB SSD, and stable internet connection. Dedicated GPU not required for early stages; use cloud when experimenting with large models.

ESTIMATED STARTUP BUDGET
Rp 8–15 million

A one-time purchase that lasts through your studies.

Laptop with 16 GB RAM and 512 GB SSD
Choose upgradable RAM if possible.
Required
Python and JupyterLab
Required
Git and GitHub
Required
Local SQL database
Required
Cloud notebook service
Useful when local compute is limited.
Optional
Dedicated GPU
Not needed for all coursework.
Optional
03

Facility Checklist

GUIDE

Questions specific to Data Science to bring to an open house or campus visit. Important facilities vary by program — these matter most.

Note: Myjor doesn't maintain a facilities database per campus. This is a tool for you to assess it yourself.

Is a computing laboratory with sufficient equipment available for entire-class lab sessions?
Do students get access to servers, cloud, or computing environments for large-scale data?
Is legal, up-to-date data analysis and machine learning software available?
Does the campus provide real datasets or access to data sources for projects?
How is technical support during programming, database, and machine learning lab sessions?
Can research facilities be used by students for their final projects?

Tip: ask for a tour of the relevant labs/studios, not just the main building. Ask about access hours and whether undergrads can use the equipment.

04

Academic Quality

GUIDE

Three objective ways to assess a program's academic quality — including how to check them yourself.

A

Program accreditation status

Check the program's own accreditation rating (not just the campus's) on official sites. International accreditation is a plus for certain fields.

Unggul / A — top tier Baik Sekali / B Baik / C

Check at: BAN-PT (banpt.or.id) or the relevant LAM, & PDDIKTI (pddikti.kemdikbud.go.id).

B

Student–faculty ratio & how to calculate it

ratio = active students ÷ full-time faculty
Example: 600 students ÷ 25 faculty = 1 : 24
✓Ideal: ≈1:20 for STEM/science fields, ≈1:30 for social sciences/humanities (per Ministry of Education guidance).
!The smaller the second number, the more attention each student gets. Above 1:40 is worth flagging.
!Also check faculty qualifications (Master's/PhD) and whether there are industry practitioner lecturers.
C

Faculty research — relevant & current?

Look up faculty profiles and publications (Google Scholar / SINTA). For Data Science, look for those researching currently relevant topics:

Predictive modeling and machine learningData mining for business or public dataTime-series data analysisNatural language processingArtificial intelligence and model evaluationBig data and distributed processing systemsData visualization and insight communicationEthics, privacy, and algorithmic bias

E.g., is there faculty researching AI, or cancer-related topics? Active research topics signal the program keeps up with the field.

ASK AT THE OPEN HOUSE

"What's this program's student–faculty ratio? What research topics are faculty currently working on? What percentage of graduates find field-relevant jobs within 6 months?"

05

Partnerships

GUIDE

Campus partnerships heavily shape internship, research, and career paths. Which partners matter varies by program — here's what's relevant for Data Science.

Bootcamps & certifications
Ask about access to or collaboration with AWS Academy, Cisco NetAcad, Microsoft, Tableau, Dicoding, or other data certification providers.
Industry internships
Ask about real internship pathways at startups, technology companies, banks, consulting firms, or corporations, and the number of students placed each year.
Joint industry research
Ask about joint research projects, datasets, grants, or papers with companies and institutions that use data.
International communities & academies
Ask about support for data communities, analytics competitions, exchange programs, or open-source projects.
List of active MoUs
Ask for a list of active MoUs and evidence of ongoing activities; don't judge solely by the logos on a brochure.

Ask for a list of official partners with active MoUs — not just logos in a brochure. Ask how many students are actually placed each year.

06

Career & Salary

DATA
Entry
0–2 years
Rp 4–8 million
per month
Mid
3–6 years
Rp 8–15 million
per month
Senior
7+ years
Rp 15–30 million
per month
Career pathsSalary range /mo
Data Analyst Rp 4–12 million
Business Intelligence Analyst Rp 5–15 million
Data Scientist Rp 8–25 million
Machine Learning Engineer Rp 8–25 million
Data Engineer Rp 7–25 million
Product Analyst Rp 5–18 million
Risk Analyst Rp 5–18 million

Sources: Myjor 2024 industry salary survey (n=4,200), BPS, and job portals. Figures are medians, influenced by city, company, and experience.

Sources & References

The career & salary data above traces back to its sources. Click to expand/collapse the source list.

06 · Career & Salary (5 sources)
https://id.indeed.com/career/data-scientist/salaries 2026-07-27 · job-portal salary aggregate
https://id.indeed.com/career/data-analyst/salaries 2026-07-27 · job-portal salary aggregate
https://glints.com/id/lowongan/gaji-data-scientist/ 2026-07-27 · job-portal salary article