Undergraduate Programme
Data
Science
Curriculum Handbook
2026 Revision · End-to-End Pipeline Track
8 Semesters |
145 Credits |
53 Courses |
6 Phases |
8 Semesters |
145 Credits |
53 Courses |
6 Phases |
A short introduction to this curriculum
Data science is often taught as three separate subjects that never quite meet: statistics in one room, software engineering in another, business reporting in a third. Graduates then discover that industry does not divide the work that way. This handbook treats the pipeline as a single loop, data engineering, analysis, modelling, deployment, and decision, and builds every semester as one more turn around it.
There is exactly one. Semester 5 opens a single concentration, Applied Data Science, that runs from business intelligence through deep learning to MLOps. Splitting the field into several narrower tracks was considered and rejected: because real projects integrate data engineering, business intelligence, machine learning, and deployment within one working cycle, fragmenting the concentration risks producing graduates whose competence is partial in every direction and complete in none.
The arc before that concentration is cumulative. Semester 1 grounds data literacy alongside discrete mathematics and calculus. Semester 2 builds the engineering machinery: objects, databases, data structures, and statistical inference. Semesters 3 and 4 broaden into systems, optimisation, text, warehouses, and distributed processing. Semesters 6 and 7 turn outward entirely under Kampus Berdampak, where approved placement hours convert into course credit. Semester 8 closes the arc with a defended capstone.
How to read this book. Each semester page lists every course by code, name, specialised tools, and credit weight, grouped by the block or category it belongs to. The progression ladder gives the full six-phase shape at a glance before the semester-by-semester detail begins.
Data Science · Curriculum Handbook 202602What this curriculum is built around
Data Science is not a list of courses in the usual order. It is a sequence chosen on purpose: mathematics beside engineering, breadth before depth, one integrated concentration, and a full field placement before the final project. Three principles run through every phase.
Discrete mathematics, calculus, statistical foundations, stochastic modelling, and optimisation are spread across Semesters 1 to 3 rather than compressed into a single early term. Each arrives near the course that needs it, so the derivation and the application are learned together.
Semester 5 commits to Applied Data Science as a single depth spanning reporting, prediction, and deployment. Narrower parallel tracks would let students graduate strong in one stage of the pipeline and unfamiliar with the rest.
Semesters 6 and 7 turn outward. Time in an approved industry placement is not extra to the semester, it is the semester, converted directly into SKS through the concentration courses carried out there.
Data Science · Curriculum Handbook 202603The problem this programme is built to solve
Able to carry a question the whole way: source and clean the data, model it honestly, deploy the result so it keeps running, and explain what it means to the people who have to act on it. Not a modeller who cannot ship, and not a reporter who cannot model.
Most analytical work dies in one of two places, a model that never leaves the notebook, or a dashboard nobody trusts. Both failures come from the same gap: the pipeline is owned in pieces, and no one is accountable for the distance between the data and the decision.
Data Science · Curriculum Handbook 202604Structure in numbers
8 Semesters |
145 Total Credits |
53 Courses |
6 Curriculum Phases |
The eight semesters are organised around six phases rather than a flat sequence: Foundation, Core Systems, Enrichment across two semesters, a single Concentration, Kampus Berdampak across two semesters, and the Capstone. The order is deliberate: mathematical and engineering foundations are laid together, breadth is built before depth, depth narrows to one integrated concentration, and that concentration is then tested in the field through a structured placement whose hours convert directly into semester SKS.
Semesters 1 to 4 are further divided into two blocks each, A and B, so that a heavy term is delivered as two shorter, more focused halves rather than eight parallel courses running the whole way through.
Each phase is defined by one verb. The full ladder is on the next page.
Data Science · Curriculum Handbook 202605From first notebook to a defended system
| S1 | GroundData literacy, discrete mathematics, and calculus arrive together with the first notebook, so the maths is never a separate subject from the work. |
| S2 | BuildObject-oriented code, databases, data structures, and statistical inference: the machinery that turns raw data into something a model can consume. |
| S3 | DeepenApplications on one side, stochastic modelling and optimisation on the other, the mathematics that later machine learning depends on. |
| S4 | ExtendText, warehouses, streaming, and mobile. Analytics stops being one dataset on one laptop and becomes something that runs at scale. |
| S5 | SpecialiseApplied Data Science: business intelligence through deep learning to deployment, one concentration covering the full cycle. |
| S6 | ImmerseKampus Berdampak begins: concentration work carried out inside an approved industry placement, credited directly into the semester's SKS. |
| S7 | ImmersePlacement continues, a second full semester of supervised field experience converted into course credit. |
| S8 | MasterCapstone: a complete, defended project drawing on the whole pipeline, often carried out while a placement continues in parallel. |
Data Science · Curriculum Handbook 202606Courses and credits, semester by semester
| Semester | Focus | Courses | SKS | Note |
|---|---|---|---|---|
| Semester 1 | Foundation | 8 | 19 | Mathematics from week one |
| Semester 2 | Core Systems | 7 | 18 | Engineering the pipeline |
| Semester 3 | Enrichment I | 8 | 20 | Systems and applied mathematics |
| Semester 4 | Enrichment II | 8 | 21 | Heaviest by design |
| Semester 5 | Concentration | 7 | 19 | Single integrated concentration |
| Semester 6 | Kampus Berdampak I | 6 | 20 | Field placement plus concentration work |
| Semester 7 | Kampus Berdampak II | 6 | 18 | Placement continues |
| Semester 8 | Ending. Capstone | 3 | 10 | Capstone plus research |
| Total | 53 | 145 | One credit below the 146 minimum |
Reading the notes. Semester 4 is the heaviest classroom term by design, immediately before the concentration narrows the field. Semesters 6 and 7 look conventional on paper but are delivered in the field: their four-credit concentration courses are carried out inside an approved industry placement under Kampus Berdampak. Semester 8 pairs the capstone with the tail end of that placement where a student chooses to continue it.
The programme currently totals 145 SKS against a 146 SKS minimum. The shortfall is a single credit and is resolved by raising one existing course by 1 SKS rather than adding a course; the recommended candidates are Professional Ethics or Data Privacy and Security, both of which carry 2 SKS against five stated learning outcomes.
Data Science · Curriculum Handbook 202607What opens in Semester 5
Applied Data Science is a single concentration, not a choice between several. Every student takes all of it. The three strands below describe the parts of the pipeline it covers, not alternatives to pick from.
ADecision Strand BUSINESS INTELLIGENCE AND REPORTING Turning modelled data into dashboards, reports, and decisions stakeholders can act on. Role focus BI Analyst / Developer
| BModel Strand PREDICTION, DEEP LEARNING AND LANGUAGE Building predictive and neural models, including transformer-based language systems. Role focus Data Scientist / ML Engineer
| CDelivery Strand MLOPS AND MODEL DEPLOYMENT Getting models out of the notebook: packaging, serving, monitoring, and versioning them. Role focus MLOps Engineer
|
Professional Ethics runs alongside the strands as the supporting course, covering fairness, transparency, accountability, and the societal consequences of a deployed model, applied to the same projects the concentration is building.
Because real projects integrate data engineering, business intelligence, machine learning, and deployment within one working cycle, separating them into parallel concentrations would produce graduates with fragmented competence. Holding the concentration together is what lets a student own a problem from raw data through to the decision it supports.
Data Science · Curriculum Handbook 202608| Kode | Mata Kuliah | SKS |
|---|---|---|
| Block IA | ||
| SDA2141 | Foundations of Data Science | 3 |
| WUC1113 | Design Thinking | 2 |
| CIK1107 | Discrete Mathematics | 3 |
| SDA2154 | Data Visualization | 3 |
| Block IB | ||
| SDA2145 | Algorithms | 3 |
| SDA2146 | Mathematical and Statistical Foundations | 2 |
| SDA2148 | Differential Calculus | 2 |
| WUC1118 | Career Design and Professional Development 1 | 1 |
Data Science · Curriculum Handbook 202609| Kode | Mata Kuliah | SKS |
|---|---|---|
| Block II A | ||
| CIK2108 | Object-Oriented Programming | 3 |
| CIK4105 | Database Systems | 3 |
| CIK2109 | Data Structures | 3 |
| Block II B | ||
| SDA2138 | Data Wrangling | 2 |
| CIK2110 | Web Client Development | 3 |
| SDA2136 | Statistical Thinking | 3 |
| WUC1120 | Career Design and Professional Development 2 | 1 |
Data Science · Curriculum Handbook 202610| Kode | Mata Kuliah | SKS |
|---|---|---|
| Block III A | ||
| CIK3101 | Web Application Development | 3 |
| CIK4106 | Advanced Database Systems | 3 |
| SDA2151 | Stochastic Modeling | 2 |
| CIK3102 | Numerical Methods | 2 |
| Block III B | ||
| SDA2153 | Advanced Computational Mathematics | 2 |
| CIK1109 | Human-Computer Interaction | 3 |
| SDA2155 | Optimization Methods | 3 |
| SDA2147 | Networks | 2 |
Block A asks how to compute; Block B asks why it works. Numerical Methods and Advanced Computational Mathematics are paired deliberately, the second supplies the mathematical foundation that later machine learning courses assume.
Data Science · Curriculum Handbook 202611| Kode | Mata Kuliah | SKS |
|---|---|---|
| Block IV A | ||
| PSD1209 | Text Mining | 3 |
| CIK4101 | Data Warehousing and Mining | 3 |
| WUC1115 | Technical / Professional Writing | 2 |
| CIK4103 | Mobile Computing | 3 |
| Block IV B | ||
| SDA2158 | Big Data Analytics | 2 |
| SDA2159 | Advanced Methods for Data Analytics | 3 |
| SDA2160 | Data Privacy and Security | 2 |
| SDA2137 | Communication Protocols | 3 |
Data Science · Curriculum Handbook 202612| Kode | Mata Kuliah | SKS |
|---|---|---|
| Konsentrasi | ||
| SDA2161 | Business Intelligence Systems | 3 |
| SDA2162 | Predictive Analytics | 3 |
| SDA2110 | Deep Learning | 3 |
| SDA2165 | Natural Language Processing | 3 |
| SDA2164 | MLOps and Model Deployment | 3 |
| SDA2163 | Enterprise Data Integration | 2 |
| Pendukung | ||
| WUC1122 | Professional Ethics | 2 |
Every course this term feeds the same cycle: integrate, model, deploy, report. The concentration is not a menu of electives, it is one route walked end to end.
Data Science · Curriculum Handbook 202613| Kode | Mata Kuliah | SKS |
|---|---|---|
| Konsentrasi | ||
| SDA2156 | Data Science Project Management | 4 |
| SDA2166* | Advanced Data Science Applications | 4 |
| SDA2167* | Data Solutions Architecture | 4 |
| Pendukung | ||
| SDA2168* | Business Communication and Data Presentation | 4 |
| Umum | ||
| WUC1111 | Indonesian Way of Life / Pancasila | 2 |
| WUC1101 | Religions of the World | 2 |
Kampus Berdampak. Semester 6 opens the placement track: the four-credit concentration courses are carried out on real organisational problems, and approved placement hours convert directly into SKS.
Data Science · Curriculum Handbook 202614| Kode | Mata Kuliah | SKS |
|---|---|---|
| Konsentrasi | ||
| SDA2169* | Data Product Engineering | 4 |
| Pendukung | ||
| SDA2170* | Data Strategy and Innovation | 4 |
| SDA2171* | Digital Transformation | 2 |
| SDA2172* | Data-Driven Decision Making | 4 |
| Umum | ||
| WUC2107 | Applied Indonesian Language | 2 |
| WUC2109 | Civic Education | 2 |
Credit conversion continues through Semester 7, so the two semesters function as one continuous placement rather than two separate terms.
Data Science · Curriculum Handbook 202615| Kode | Mata Kuliah | SKS |
|---|---|---|
| Capstone | ||
| WUC1114 | Capstone Project | 6 |
| Pendukung | ||
| CIK1103 | Research Methodology | 2 |
| Umum | ||
| WUC1108 | English | 2 |
Students may continue their Kampus Berdampak placement through Semester 8 while completing and defending the capstone, so placement and closing project run side by side rather than one blocking the other.
Data Science · Curriculum Handbook 202616Data Science moves from first notebook to defended capstone in eight semesters: mathematics and engineering laid together, breadth across systems and analytics, one integrated concentration covering the full pipeline, two semesters inside industry, and a capstone that has to work in front of an audience.