INFO 2951: Introduction to Data Science with R
This page contains an outline of the topics, content, and assignments for the semester. Note that this schedule will be updated as the semester progresses and the timeline of topics and assignments might be updated throughout the semester.
| WEEK | DATE | TOPIC | PREPARE | MATERIALS | DUE |
|---|---|---|---|---|---|
| 1 | Tue, Jan 20 | Welcome to INFO 2951 | π©βπ» Login to Cornellβs GitHub server |
π½οΈ slides 01 β¨οΈ ae 00 - UN votes |
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| Thu, Jan 22 | Grammar of graphics | π r4ds - intro π r4ds - ch 1.1-.3, 1.7 π ims - ch 1 |
π½οΈ slides 02 β¨οΈ ae 00 - WDI β ae 00 - WDI |
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| Fri, Jan 23 | Hello data science! | β¨οΈ hw 00 | |||
| 2 | Tue, Jan 27 | Visualizing various types of data | π ims - ch 4 π ims - ch 5 |
π½οΈ slides 03 β¨οΈ ae 01 β ae 01 |
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| Wed, Jan 28 | β hw 00 | HW 00 at 11:59pm | |||
| Thu, Jan 29 | Grammar of data wrangling | π r4ds - ch 3 | π½οΈ slides 04 β¨οΈ ae 02 β ae 02 |
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| Fri, Jan 30 | Data visualization | β¨οΈ hw 01 | |||
| 3 | Tue, Feb 3 | Working with relational data | π r4ds - ch 19 | π½οΈ slides 05 β¨οΈ ae 03 β ae 03 |
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| Wed, Feb 4 | β hw 01 | HW 01 at 11:59pm | |||
| Thu, Feb 5 | Tidying data | π r4ds - ch 5 | π½οΈ slides 06 β¨οΈ ae 04 β ae 04 |
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| Fri, Feb 6 | Data wrangling | π©βπ» Complete team preference survey for team project | β¨οΈ hw 02 | ||
| 4 | Tue, Feb 10 | Data types and classes | π r4ds - ch 4 π r4ds - ch 12 (read 12.1-12.2, skim the rest) π r4ds - ch 14.1-.3 π r4ds - ch 16 |
π½οΈ slides 07 β¨οΈ ae 05 β ae 05 |
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| Wed, Feb 11 | β hw 02 | HW 02 at 11:59pm | |||
| Thu, Feb 12 | Importing and recoding data | π r4ds - ch 7 π r4ds - ch 17.1 - 17.3 |
π½οΈ slides 08 β¨οΈ ae 06 β ae 06 |
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| Fri, Feb 13 | Git workflows (basics + merge conflicts) | π Happy Git with R - ch 1 | β¨οΈ Collaborating with Git | ||
| 5 | Tue, Feb 17 | No class (February Break) | |||
| Thu, Feb 19 | Databases + SQL | π r4ds - ch 21 | π½οΈ slides 09 β¨οΈ ae 07 β ae 07 |
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| Fri, Feb 20 | What makes for a good data science project? | Read these articles to discuss in class π Are Pop Lyrics Getting More Repetitive? π The Hidden Cost of Digital Consumption |
β¨οΈ hw 03 | ||
| 6 | Tue, Feb 24 | Getting data from the web: Scraping | π r4ds - ch 24 π©βπ» Install SelectorGadget |
π½οΈ slides 10 β¨οΈ ae 08 β ae 08 |
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| Wed, Feb 25 | β hw 03 | HW 03 at 11:59pm | |||
| Thu, Feb 26 | Functions | π r4ds - ch 25.2-.3 | π½οΈ slides 11 β¨οΈ ae 09 β ae 09 |
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| Fri, Feb 27 | Quiz 01 | π quiz 01 β¨οΈ hw 04 |
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| 7 | Tue, Mar 3 | Iteration | π r4ds - ch 26 π r4ds - ch 27 (skim for familiarity with base R syntax) |
π½οΈ slides 12 β¨οΈ ae 10 β ae 10 |
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| Wed, Mar 4 | β hw 04 | HW 04 at 11:59pm | |||
| Thu, Mar 5 | Getting data from the web: APIs | π Application Programming Interface π Obtaining World Bank indicators π Securely storing API keys |
π½οΈ slides 13 β¨οΈ ae 11 β ae 11 |
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| Fri, Mar 6 | Refine project proposals | β¨οΈ hw 05 | |||
| 8 | Tue, Mar 10 | Rectangling data | π r4ds - ch 23 | π½οΈ slides 14 β¨οΈ ae 12 β ae 12 |
Project proposal at 11:59pm |
| Wed, Mar 11 | β hw 05 | HW 05 at 11:59pm | |||
| Thu, Mar 12 | Reproducible reporting with Quarto | π r4ds - ch 28 | π½οΈ slides 15 β¨οΈ ae 13 β ae 13 |
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| Fri, Mar 13 | Project EDA work session | ||||
| 9 | Tue, Mar 17 | Hypothesis testing with randomization | π ims - ch 11 π ims - ch 14 |
π½οΈ slides 16 β¨οΈ ae 14 β ae 14 |
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| Wed, Mar 18 | |||||
| Thu, Mar 19 | Quantifying uncertainty with the bootstrap | π ims - ch 12 | π½οΈ slides 17 β¨οΈ ae 15 β ae 15 |
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| Fri, Mar 20 | Quiz 02 | π quiz 02 β¨οΈ hw 06 |
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| 10 | Tue, Mar 24 | Linear regression with a single predictor | π ims - ch 8 π ims - ch 24 |
π½οΈ slides 18 β¨οΈ ae 16 β ae 16 |
Project EDA at 11:59pm |
| Wed, Mar 25 | β hw 06 | HW 06 at 11:59pm | |||
| Thu, Mar 26 | Linear regression with multiple predictors | π ims - ch 9 π ims - ch 25 |
π½οΈ slides 19 β¨οΈ ae 17 β ae 17 |
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| Fri, Mar 27 | No class | ||||
| 11 | Tue, Mar 31 | No class (Spring Break) | |||
| Thu, Apr 2 | No class (Spring Break) | ||||
| Fri, Apr 3 | No class (Spring Break) | ||||
| 12 | Tue, Apr 7 | Models for discrete outcomes | π ims - ch 9 | π½οΈ slides 20 β¨οΈ ae 18 β ae 18 |
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| Wed, Apr 8 | |||||
| Thu, Apr 9 | Introduction to machine learning | π tmwr - ch 4-6, 10 | π½οΈ slides 21 β¨οΈ ae 19 β ae 19 |
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| Fri, Apr 10 | Refine project hypotheses | β¨οΈ hw 07 | |||
| 13 | Tue, Apr 14 | Build better training data | π tmwr - ch 7-9 | π½οΈ slides 22 β¨οΈ ae 20 β ae 20 |
Project preregistration at 11:59pm |
| Wed, Apr 15 | β hw 07 | HW 07 at 11:59pm | |||
| Thu, Apr 16 | Tree-based inference and hyperparameter optimization | π tmwr - ch 12-14 | π½οΈ slides 23 β¨οΈ ae 21 β ae 21 |
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| Fri, Apr 17 | No class | π quiz 03 β¨οΈ hw 08 |
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| 14 | Tue, Apr 21 | An introduction to LLMs | Project draft at 11:59pm | ||
| Wed, Apr 22 | HW 08 at 11:59pm | ||||
| Thu, Apr 23 | Programming with LLMs | ||||
| Fri, Apr 24 | Quiz 03 | ||||
| 15 | Tue, Apr 28 | Prompt engineering and augmented generation | |||
| Thu, Apr 30 | Tool calling | ||||
| Fri, May 1 | Project presentations | Project presentations in class | |||
| 16 | Tue, May 5 | Wrap-up: Where to go from here | Project report + reproducibility at 11:59pm Extra credit at 11:59pm |
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| Wed, May 13 | Final exam | Final exam at 2pm (location TBD) |