COSC2670 Practical Data Science with Python
The Complete Study & Assessment Guide · S2 2026COSC2670 Overview
- RMIT University School of Computing Technologies
- Semester 2, 2026
- a postgraduate course
- 12 credit points
- a practical data-science course using Python
COSC2670 Practical Data Science with Python develops data acquisition, curation, summarisation, visualisation, modelling, classification, clustering, recommender systems and reproducible project practice. It is taught within RMIT University School of Computing Technologies. It is a postgraduate course. It carries 12 credit points.
- Four individual tasks The Handbook publishes a 10/25/35/30 split across quiz, written work and two prototyping tasks.
- No published exam row The accessible 2026 Handbook uses continuous assessment; offering-specific conditions remain controlled by Part B or Canvas.
- Validation before score Keep training, model selection and final evaluation separate so performance is not measured on data used to tune the model.
What you get in the full COSC2670 Study & Assessment Guide
- 7 chapters, 20 pages — every assessed topic, week by week
- 10 original practice questions, each with a model answer
- 7 worked examples, solved the way COSC2670 marks
- 16 computed figures, redrawn from the course’s own numbers
- Before-you-submit checklist for the Design Development and Prototyping 1 (35%) — what markers reward, what costs marks
Free on this page: assessment table, chapter map, one worked example, key terms, FAQ. The rest is in the full study & assessment guide.
How COSC2670 is assessed
| Component | Weight | Format |
|---|---|---|
| Quiz or Test | 10% | Individual; detailed timing and conditions must be confirmed in the live course guide |
| Written Work | 25% | Individual data-science written work |
| Design Development and Prototyping 1 | 35% | Individual project work aligned to the published learning outcomes |
| Design Development and Prototyping 2 | 30% | Individual project work; use the live brief for conditions |
The accessible 2026 Handbook publishes four individual tasks worth 10%, 25%, 35% and 30%. It publishes no final-exam row. Course Guide Part B is unavailable, so detailed task and hurdle conditions must be confirmed live.
What other students are asking about COSC2670
- Where can I find lecture notes or study guides for COSC2670 at RMIT?
- The COSC2670 study guide in AskSia Library covers Practical Data Science with Python in 7 chapters, with the assessment table, a chapter-by-chapter topic map, key terms and a worked example, free to read on this page.
- Is there a final exam in COSC2670?
- No final exam is listed in the offering this AskSia Library guide is built from; COSC2670 is assessed by Quiz or Test 10%, Written Work 25%, Design Development and Prototyping 1 35%, Design Development and Prototyping 2 30%.
- What does COSC2670 cover on data acquisition curation and integration?
- In AskSia Library's COSC2670 guide, the Data Acquisition Curation and Integration chapter explains data provenance · data curation · schema integration · combine messy sources while preserving provenance, types, missingness and semantic meaning.
- What does COSC2670 cover on data summarisation and visual evidence?
- In AskSia Library's COSC2670 guide, the Data Summarisation and Visual Evidence chapter explains distribution · aggregation · visual encoding · select summaries and charts that expose distribution, group structure and anomalies without hiding the observation level.
- What does COSC2670 cover on model design training and validation?
- In AskSia Library's COSC2670 guide, the Model Design Training and Validation chapter explains feature · training set · validation strategy · choose features and a train-validation-test structure matched to dependence, clustering and the real prediction setting.
Current COSC2670 dates
| Date | Item | Control |
|---|---|---|
| 24-30 August 2026 | Canvas Assignment 1 due week | The syllabus does not safely map this label to a Handbook assessment number. |
| 28 September-4 October 2026 | Canvas Assignment 2 due week | Assignment 3 is released in the same week. |
| 19-25 October 2026 | Canvas Assignment 3 due week | Confirm the exact deadline and task mapping in Canvas. |
Dates are as published in the current course materials. Confirm exact deadlines and submission settings in the live LMS.
What COSC2670 covers
The sequence opens at Data Science Lifecycle and Reproducible Python, develops its central analytical shift in Model Design Training and Validation, and closes with Experimental Method and Data Science Reporting.
Data Science Lifecycle and Reproducible Python
data-science lifecycle · notebook · reproducibility · build a reproducible Python workflow from declared inputsDefine data-science lifecycle
The course material gives this chapter a concrete anchor: The opening material positions practical data science as a lifecycle supported by executable Python notebooks.
- data-science lifecycle
- notebook
- reproducibility
- Applying data-science lifecycle
- Limits of notebook and reproducibility
Data Acquisition Curation and Integration
data provenance · data curation · schema integration · combine messy sources while preserving provenance, types, missingness and semantic meaningDefine data provenance
The course material gives this chapter a concrete anchor: The curation material treats cleaning and integration as evidence-sensitive design decisions rather than clerical preparation.
- data provenance
- data curation
- schema integration
- Applying data provenance
- Limits of data curation and schema integration
Data Summarisation and Visual Evidence
distribution · aggregation · visual encoding · select summaries and charts that expose distribution, group structure and anomalies without hiding the observation levelDefine distribution
The course material gives this chapter a concrete anchor: The Week 3 material links statistical summaries with chart selection and interpretation.
- distribution
- aggregation
- visual encoding
- Applying distribution
- Limits of aggregation and visual encoding
Model Design Training and Validation
feature · training set · validation strategy · choose features and a train-validation-test structure matched to dependence, clustering and the real prediction settingDefine feature
The course material gives this chapter a concrete anchor: The modelling lecture makes validation and the danger of naive random or k-fold splitting central to model design.
- feature
- training set
- validation strategy
- Applying feature
- Limits of training set and validation strategy
Classification Neighbours and Decision Trees
classification · nearest-neighbour rule · decision tree · compare a distance-based classifier with a tree while controlling feature scale, depth and validationDefine classification
The course material gives this chapter a concrete anchor: The classification block develops k-nearest neighbours, distance choices, confusion matrices, decision trees, Gini splitting and cross-validation.
- classification
- nearest-neighbour rule
- decision tree
- Applying classification
- Limits of nearest-neighbour rule and decision tree
Clustering and Recommender Systems
clustering · similarity · contextual recommendation · select a representation and evaluation for clusters or recommendations without treating an algorithmic grouping as a natural categoryDefine clustering
The course material gives this chapter a concrete anchor: The current syllabus assigns unsupervised grouping and recommendation methods, and the landed reading shows context, graph construction and cross-validated ranking metrics.
- clustering
- similarity
- contextual recommendation
- Applying clustering
- Limits of similarity and contextual recommendation
Experimental Method and Data Science Reporting
experimental methodology · metric · technical report · turn model output into a report whose claim can be traced to data, method, metric and validation designDefine experimental methodology
The course material gives this chapter a concrete anchor: The project examples connect classification questions, cross-validation, accuracy and report structure.
- experimental methodology
- metric
- technical report
- Applying experimental methodology
- Limits of metric and technical report
It is positioned as a practical data-science course using Python.
The course follows the data-analysis lifecycle through hands-on Python work and evaluates it through four individual tasks rather than a final examination.
Assessment in COSC2670 is distributed as follows: 10% individual quiz or test, 25% individual written work, 35% individual design development and prototyping, and 30% individual design development and prototyping
The operational assessment conditions matter here.
No final-exam row appears in the accessible 2026 Handbook assessment table.
The current Canvas syllabus schedules three assignments across the semester, but it does not safely map every Canvas label to the four Handbook assessment numbers.
What makes COSC2670 demanding is concrete: Designing a reproducible analysis rather than a sequence of library calls: data provenance, curation, feature choices, validation split, metric and interpretation must remain connected to the original problem.
Treat the component-level hurdle status as unconfirmed: the accessible Handbook contains no hurdle statement, while the offering-specific Course Guide Part B is currently unavailable.
For enrolment planning, The 2026 RMIT Handbook publishes no assumed knowledge; students should still confirm program and enrolment rules.
The sequence opens at Data Science Lifecycle and Reproducible Python, develops its central analytical shift in Model Design Training and Validation, and closes with Experimental Method and Data Science Reporting.
Coverage note: RMIT Course Guide Part B is currently unavailable.
Hurdle and detailed task conditions are therefore unconfirmed beyond the accessible 2026 Handbook and current Canvas schedule.
Worked example · free
Audit a classification result before accepting its accuracy
- 1Count correct predictions: 72 plus 2 equals 74.
- 1Compute accuracy as 74 divided by 100, giving 74%.
- 1Compute minority recall as 2 divided by 20, giving 10%.
- 1Explain that the aggregate score is dominated by the majority class.
- 1Select a class-sensitive metric and validation split matched to the real decision cost.
Key terms
- Data wrangling
- The process of transforming raw, inconsistent or fragmented data into a documented form suitable for analysis.
- Classification
- Supervised learning that assigns observations to predefined outcome categories from labelled examples.
- Recommender system
- A model that ranks or selects items for a user or context using observed interactions, content or related signals.
- Exploratory data analysis
- Iterative use of summaries and visualisations to understand distributions, relationships, anomalies and data-quality limits.
- Feature
- A measured or constructed input variable used by a statistical or machine-learning model.
- Training set
- The portion of data used to estimate model parameters or learn decision structure.
- Validation set
- Data withheld from fitting and used to compare choices or tune a model before final evaluation.
- Confusion matrix
- A table comparing predicted and actual classes so different kinds of classification error remain visible.
- Clustering
- Unsupervised grouping of observations according to a defined representation and similarity or distance rule.
COSC2670 FAQ
Which current COSC2670 dates are captured?
Canvas Assignment 1 due week: 24-30 August 2026; Canvas Assignment 2 due week: 28 September-4 October 2026; Canvas Assignment 3 due week: 19-25 October 2026. Confirm any change and the exact submission setting in the live LMS.
How is COSC2670 assessed?
10% individual quiz or test, 25% individual written work, 35% individual design development and prototyping, and 30% individual design development and prototyping
Where do students usually lose marks in COSC2670?
Designing a reproducible analysis rather than a sequence of library calls: data provenance, curation, feature choices, validation split, metric and interpretation must remain connected to the original problem.
What is the COSC2670 final assessed-task format?
No final-exam row appears in the accessible 2026 Handbook assessment table. The current Canvas syllabus schedules three assignments across the semester, but it does not safely map every Canvas label to the four Handbook assessment numbers.
What prerequisites or restrictions apply to COSC2670?
The 2026 RMIT Handbook publishes no assumed knowledge; students should still confirm program and enrolment rules.
Is this COSC2670 resource an official university guide?
No. It is an independent COSC2670 study resource; current institutional instructions remain authoritative for assessment operation.
Questions students ask in each COSC2670 chapter
- What is the main task in Data Science Lifecycle and Reproducible Python?
- Build a reproducible python workflow from declared inputs. Chapter 01 · Data Science Lifecycle and Reproducible Python
- What is the main task in Data Acquisition Curation and Integration?
- Combine messy sources while preserving provenance, types, missingness and semantic meaning. Chapter 02 · Data Acquisition Curation and Integration
- What is the main task in Data Summarisation and Visual Evidence?
- Select summaries and charts that expose distribution, group structure and anomalies without hiding the observation level. Chapter 03 · Data Summarisation and Visual Evidence
- What is the main task in Model Design Training and Validation?
- Choose features and a train-validation-test structure matched to dependence, clustering and the real prediction setting. Chapter 04 · Model Design Training and Validation
- What is the main task in Classification Neighbours and Decision Trees?
- Compare a distance-based classifier with a tree while controlling feature scale, depth and validation. Chapter 05 · Classification Neighbours and Decision Trees
- What is the main task in Clustering and Recommender Systems?
- Select a representation and evaluation for clusters or recommendations without treating an algorithmic grouping as a natural category. Chapter 06 · Clustering and Recommender Systems
- What is the main task in Experimental Method and Data Science Reporting?
- Turn model output into a report whose claim can be traced to data, method, metric and validation design. Chapter 07 · Experimental Method and Data Science Reporting
How to prepare for the assessments
Retrieve the course map, practise the recurring method—define the data-science question, audit and transform the data, choose features and a model, separate training from validation, evaluate with decision-relevant metrics and document a reproducible interpretation—on changed scenarios, and verify every operational assessment detail in the live institutional system.
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