Tentative Winter 2026–2027 Course Schedule
This tentative progression is planned around four 50-minute meetings per week: two lecture sessions and two hands-on/lab sessions. Proposed labs below are weekly activity outlines; detailed materials, readings, homework, and assignment deadlines will be revised separately. Exam weeks and the shortened MLK week will use a flexible meeting balance. Teaching weeks pause during holiday vacation.
Dates follow the 2026–27 Academic Year Calendar. Classes begin Monday, November 30, 2026, at 8:00 a.m. Holiday vacation begins after the last class on Friday, December 18; classes resume Monday, January 4, 2027, at 8:00 a.m. No classes on Martin Luther King, Jr. Day (Monday, January 18). Regular instruction ends Friday, February 19, before finals.
| Week | Dates (2026–2027) | Topics | Proposed hands-on/lab sessions |
|---|---|---|---|
| Week 1 | Nov 30–Dec 4, 2026 | Introduction to Data Science + Data Acquisition + Data Cleaning | Lab 1: Data Acquisition Lab 2: Guided Data Cleaning |
| Week 2 | Dec 7–11, 2026 | Data Quality + Exploratory Data Analysis | Lab 1: Data Quality — missing/inconsistent data and quality issues Lab 2: Exploratory Data Analysis — distributions, summary statistics, and relationships |
| Week 3 | Dec 14–18, 2026 | Data Visualization + Review + Exam 1 | Lab 1: Data Visualization and critique of visualizations Lab 2: Integrated review — acquisition → cleaning → data quality → EDA → visualization |
| Holiday Vacation: after the last class on December 18, 2026, through January 3, 2027. Classes resume January 4. | |||
| Week 4 | Jan 4–8, 2027 | Distance, Similarity & Clustering Distance metrics; distance matrices; k-means clustering; centroids |
Lab 1: Distance metric and distance matrix calculations Lab 2: k-means clustering, centroid updates, and Python verification |
| Week 5 | Jan 11–15, 2027 | Hierarchical Clustering + Decision Trees for Classification and Regression Entropy and information gain |
Lab 1: Hierarchical clustering and dendrogram interpretation Lab 2: Decision trees, entropy, and information gain |
| Week 6 | Jan 19–22, 2027 Jan 18: Martin Luther King, Jr. Day — no classes |
Metrics, Baselines + Project Team Formation / Sprint 0 Confusion matrix; accuracy, precision, recall, and F1 |
Lab 1: Confusion matrix and classification metrics Lab 2: Baseline/model comparison activity or project team formation/Sprint 0 |
| Week 7 | Jan 25–29, 2027 | Linear and Logistic Regression: prep, 1HE, matrix form Linear regression; matrix representation; MSE and R²; logistic regression; sigmoid/probabilities; one-hot encoding (1HE) |
Lab 1: Linear regression calculations, MSE/R², and matrix representation Lab 2: Logistic regression, sigmoid, and classification probabilities |
| Week 8 | Feb 1–5, 2027 | Softmax, CCE + Predictive Models Softmax; one-hot encoding as needed; categorical cross-entropy (CCE); model comparison |
Lab 1: Softmax and cross-entropy calculations Lab 2: Predictive modeling / model comparison |
| Week 9 | Feb 8–12, 2027 | Review + Exam 2 + Project Sprint | Lab 1: Integrated predictive-modeling review Lab 2: Project Sprint |
| Week 10 | Feb 15–19, 2027 | Project Presentations | Project work and presentations during lab/meeting time as appropriate |
| Finals | Feb 22–25, 2027 | Final examination period: Monday, February 22, 8:00 a.m., through Thursday, February 25, 5:00 p.m. | |
| Term ends | Mar 1, 2027 | Final grades due at 9:00 a.m.; Winter Term ends at 5:00 p.m. | |
Tentative meeting adjustments: In Weeks 3 and 9, the integrated review may share lecture or lab time to make room for the exam. Week 6 has three meetings; baseline comparison and Sprint 0 may be combined in one hands-on session.
Weekly Meetings and Reading — Tentative
Readings: Skiena, The Data Science Design Manual, before each meeting. M/T: lectures and readings; Th/F: labs. All meetings are 50 minutes.
Week 1 · Nov 30–Dec 4, 2026
| Day / Date | Topic | Slides / Materials | Reading | Labs / Activities |
|---|---|---|---|---|
| Mon, Nov 30 | Introduction to Data Science | Introduction worksheet Slides: TBD |
§§1.1–1.3: Data science, questions, and data types. | Discuss questions a dataset could answer. |
| Tue, Dec 1 | Data Acquisition + Data Cleaning | Slides: TBD | §3.2: Collecting data. §§3.3.1–3.3.3: Errors, compatibility, and missing values. §4.3: Z-scores and normalization. |
Trace source → raw data → cleaning decisions. |
| Thu, Dec 3 | Data Acquisition | Python practice Customer-churn CSV Lab handout: TBD |
§§3.1.1–3.1.2: Notebooks and data formats. | Lab: Use a notebook to load a CSV in pandas; inspect its schema and document its source. |
| Fri, Dec 4 | Data Cleaning | Customer-churn CSV Lab handout: TBD |
Guided lab: Identify missing/inconsistent values, justify fixes, and practice normalization in pandas. |
Previous schedule and resources (Winter 2024–2025)
The dated schedule below is preserved for reference. Its topic order, exams, deadlines, and project milestones have not yet been aligned with the tentative sequence above. These historical dates are not the current course schedule.
CSSE 386 Winter 2024-2025 Schedule
Worksheets (scans) are due the same day by 11:59pm, Labs and HWK are due on Mondays
| Session | Topics | Materials | What's Due | Extra | |
|---|---|---|---|---|---|
| Week 1 | Mon, Dec 2 | Introduction to Data Science | 1.1, 1.2, 1.4 | Worksheet 01 |
Slides |
| Tue, Dec 3 | Python for Data Science: Jupyter Notebooks & Data Frames | Code Practice | Worksheet 02 |
Slides |
|
| Thu, Dec 5 | Colab Demo | Colab Practice | Quiz 01 |
||
| Fri, Dec 6 | Individual Coding Lab | Complete Lab01 Pandas |
Lab 01 |
Due Monday 12/09 |
|
| Week 2 | Mon, Dec 9 | Exploratory Data Analysis | 6.1–6.3 Select 2 graphsMatplotlib |
Worksheet 03 |
Slides Color Blindness Simulator Color Brewer Simulator |
| Tue, Dec 10 | Missing Data Analysis | Missing Data Practice Dataset |
Worksheet 04 |
Slides | |
| Thu, Dec 12 (no class meeting) | Matplotlib Demo |
Matplotlib Practice Dataset Link |
Quiz 02 |
MatplotLib Cheatsheet |
|
| Fri, Dec 13 | Individual Coding Lab | Complete Lab02 Dataset: penguins |
Lab 02 |
Due on Monday |
|
| Week 3 | Mon, Dec 16 | Pivot Tables, Feature Scaling | Min-Max Technique Scaling Technique Datasets Hub |
Worksheet 05 |
Slides |
| Tue, Dec 17 | Data Mining Techniques | Techniques Summary | Worksheet 06 |
Slides |
|
| Thu, Dec 19 | In-Class Demo |
Dataset Coding Practice |
Quiz 03 |
||
| Fri, Dec 20 | Individual Coding Lab | Lab 03 | Due on Monday Jan 6 | ||
| Holiday Break | |||||
| Week 4 | Mon, Jan 6 | Simple Linear Regression | Self-paced (no attendance required). Submit Worksheet 07 | Worksheet 07 |
Slides |
| Tue, Jan 7 | Logistic Regression and ROC | Self-paced (no attendance required) | Worksheet 08 |
Slides |
|
| Thu, Jan 9 | Scikit-learn Models | Regression Coding Practice |
Self-paced (no attendance required/no worksheet). This practice will prepare you for the Lab04 and HWK01 | ||
| Fri, Jan 10 | Individual Coding Lab | Complete Lab 04 | Lab 04 | Due on Monday Jan 13 | |
| Week 5 | Mon, Jan 13 | Classification models | Meeting Virtually (Link will be sent) | Worksheet 09 |
Slides (Overfitting) Slides (Classification) |
| Tue, Jan 14 | Naive Bayes | Review in Class | Worksheet 10 |
Slides | |
| Thu, Jan 16 | SVM, Cross-validation, Confusion matrix | Exam Review | Worksheet 11 |
Slides(Confusion Matrix) Slides(SVM) Slides(KNN) | |
| Fri, Jan 17 | Individual Coding Lab | Complete Lab 05 | Lab 05 | Due on Monday Jan 20 | |
| Week 6 | Mon, Jan 20 | MLK Day | |||
| Tue, Jan 21 | Exam 1 (Gradescope) No In-Class Meeting Timed Exam (50min) | Homework 1 | HWK1 Due on Monday 01/27 | ||
| Thu, Jan 23 | NCAA Competition, Group Assigning | ||||
| Fri, Jan 24 | Kaggle Introduction | Milestone 1 |
Due on Monday 01/27 |
||
| Week 7 | Mon, Jan 27 | Dimensionality Reduction: PCA | PCA Practice | Worksheet 12 |
Slides |
| Tue, Jan 28 | Dimensionality Reduction: LDA | Worksheet 13 |
Practice 1 PCA Iris Data Practice 2: Comparison PCA and LDA |
Slides | |
| Thu, Jan 30 | Preps for Kaggle competition Each group must complete Kaggle training course |
(Individual or team work) | Link to Kaggle Training | No meeting in Class Self-paced (no attendance required) | |
| Fri, Jan 31 | Kick-Off Competition | In-person or virtual (required per team) 9am-2pm |
Submit Kaggle Training by Mon Feb 3 | ||
| Week 8 | Mon, Feb 3 | Recommendation Systems | Worksheet 14 |
Slides |
|
| Tue, Feb 4 | Recommendation Systems | KNN recommendation Various Rec System practice |
Worksheet 15 |
Slides | |
| Thu, Feb 6 | In-Class Quiz |
Preps for Milestone 2 |
Quiz 04 |
||
| Fri, Feb 7 | Individual Coding Lab | Complete Lab 06 | Lab 06 | Due on Monday Feb 10 | |
| Week 9 | Mon, Feb 10 | Using DL Models | Sample Prediction using Churn Data Dataset Churn data Notebook Practice: See notebooks |
Worksheet 16 |
Slides |
| Tue, Feb 11 | Working on Milestone 2 | Milestone 2 CheckList |
Survey |
||
| Thu, Feb 13 | (Meeting online) | Exam Review |
Attend virtually Sports Analytics Sports Analytics Townhall | ||
| Fri, Feb 14 | Team work | Milestone 2 |
Homework 2 |
HMW2 due 02/23 | |
| Week 10 | Mon, Feb 17 | Exam 2 (Gradescope) No In-Class Meeting Timed Exam (50min) |
Exam starts at 10am and 11am for each section |
Milestone 2 Due on Monday 02/17 | |
| Tue, Feb 18 | Work on Kaggle Competition | Meeting in Class | |||
| Thu, Feb 20 | Work on Kaggle Competition | No Meeting in Class | |||
| Fri, Feb 21 | Work on Kaggle Competition | No Meeting in Class | |||
| Finals Week | Mon, Feb 24 | Submit Kaggle Competition and Video | Milestone 3 Video (gradescope) Code (Kaggle) |
HMW2 due 02/23 Milestone 3 Due on Monday, 02/24 | |