Sept-Dec 2019, Tues/Thurs 12:30 - 2:00 pm, ORCH 4074
Use of Data Science tools to summarize, visualize, and analyze data. Sensible workflows and clear interpretations are emphasized.
MATH 12
We are using an open source textbook available free on the web: https://ubc-dsci.github.io/introduction-to-datascience/
In recent years, virtually all areas of inquiry have seen an uptake in the use of Data Science tools. Skills in the areas of assembling, analyzing, and interpreting data are more critical than ever. This course is designed as a first experience in honing such skills. Students who have completed this course will be able to implement a Data Science workflow in the R programming language, by “scraping” (downloading) data from the internet, “wrangling” (managing) the data intelligently, and creating tables and/or figures that convey a justifiable story based on the data. They will be adept at using tools for finding patterns in data and making predictions about future data. There will be an emphasis on intelligent and reproducible workflow, and clear communications of findings. No previous programming skills necessary; beginners are welcome!
Students will learn to perform their analysis using the R programming language. Worksheets and tutorial problem sets as well as the final project analysis, development, and reports will be done using Jupyter Notebooks. Students will access the worksheets and tutorials in Jupyter Notebooks through Canvas. Students will require a laptop, chromebook or tablet in both lectures and tutorials. If a student does not their own laptop or chromebook, students may be able to loan a laptop from the UBC library.
By the end of the course, students will be able to:
- Download and scrape data off the world-wide-web.
- Wrangle data from their original format into a fit-for-purpose format.
- Create, and interpret, meaningful tables from wrangled data.
- Create, and interpret, impactful figures from wrangled data.
- Apply, and interpret the output of, a simple classifier.
- Make and evaluate predictions using a simple classifier.
- Apply, and interpret the output of, a simple clustering algorithm.
- Apply, and interpret the output of, a regression model.
- Make and evaluate predictions using a regression model.
- Distinguish between in-sample prediction, out-of-sample prediction, and cross-validation.
- Apply and interpret a bootstrap analysis in a regression context.
- Accomplish all of the above using workflows and communication strategies that are sensible, clear, reproducible, and shareable.
Learning outcomes per lecture are available here.
| Position | Name | office hours | office location | |
|---|---|---|---|---|
| Instructor | Tiffany Timbers | [email protected] | ||
| Instructor | Trevor Campbell | [email protected] | Thursday 2pm | ESB3116 |
| TA | Daniel Alimohd | |||
| TA | Alex Chow | |||
| TA | Jordan Bourak | |||
| TA | Grandon Seto | |||
| TA | Petal Vitis |
| Deliverable | % grade |
|---|---|
| Lecture worksheets | 5 |
| Tutorial problem sets | 15 |
| Group project | 20 |
| Three quizzes | 60 |
| Deliverable | % grade |
|---|---|
| Proposal | 3 |
| Peer review | 2 |
| Final report | 10 |
| Team work | 5 |
- It is necessary to pass the final examination to pass the course.
- Specific dates for each assessment item are listed here and will be posted on Canvas.
Lectures are held on Thursdays. The tutorials happen on Tuesdays and build on the concepts learned in lecture.
| Lecture date | Topic | Description | Lecture pre-reading |
|---|---|---|---|
| Chapter 1: Introduction to Data Science | Learn to use the R programming language and Jupyter notebooks as you walk through a real world Data Science application that includes downloading data from the web, wrangling the data into a useable format and creating an effective data visualization. | Introduction to Data Science | |
| Chapter 2: Reading in data locally and from the web | Learn to read in various cases of data sets locally and from the web. Once read in, these data sets will be used to walk through a real world Data Science application that includes wrangling the data into a useable format and creating an effective data visualization. | Reading in data locally and from the web | |
| Chapter 3: Cleaning and wrangling data | This week will be centered around tools for cleaning and wrangling data. Again, this will be in the context of a real world Data Science application and we will continue to practice working through a whole case study that includes downloading data from the web, wrangling the data into a useable format and creating an effective data visualization. | ||
| Chapter 4: Effective data visualization | Expand your data visualization knowledge and tool set beyond what we have seen and practiced so far. We will move beyond scatter plots and learn other effective ways to visualize data, as well as some general rules of thumb to follow when creating visualations. All visualization tasks this week will be applied to real world data sets. Again, this will be in the context of a real world Data Science application and we will continue to practice working through a whole case study that includes downloading data from the web, wrangling the data into a useable format and creating an effective data visualization. | ||
| Transition week | Quiz 1 | ||
| Chapter 6: Classification | Introduction to classification using K-nearest neighbours (k-nn) | ||
| Chapter 7: Classification, continued | Classification continued | ||
| Chapter 8: Regression | Introduction to regression using K-nearest neighbours (k-nn). We will focus on prediction in cases where there is a response variable of interest and a single explanatory variable. | ||
| Transition week | Quiz 2 | ||
| Chapter 9: Regression, continued | Continued exploration of k-nn regression in higher dimensions. We will also begin to compare k-nn to linear models in the context of regression. | ||
| Chapter 10: Bootstrap applied to regression | This week will introduce the bootstrap, first by visualizing bootstrap samples and their fitted regression lines for cases where there is a response variable of interest and a single explanatory variable. An intuitive case will be made for what the ensemble of slopes represents, Then we work through examples from multiple regression, emphasizing the scientific interpretation and relevance of the mix of negative/positive slopes. We will emphasize that this is a jumping off point for the study of statistical inference. | ||
| Chapter 11: Clustering | Introduction to clustering using K-means | ||
| Data Science wrap-up & Work on group project in class |
Regular attendance to lecture and tutorials is expected of students. Students who are unavoidably absent because of illness or other reasons should inform the instructor(s) of the course as soon as possible, preferably, prior to the start of the lecture/tutorial. Students who miss a quiz or assignment need to provide a doctor’s note and make arrangements (e.g., schedule an oral make-up quiz) with the Instructor as soon as possible. Failing to present a doctor’s note may result in a grade of zero.
A late submission is defined as any work submitted after the deadline. For a late submission, the student will receive a 50% deducation of their grade for the first occurrence. Hence a maximum attainable grade for the first piece of work submitted late is 50%. Any additional pieces of work that are submitted late will receive a grade of 0 for subsequent occurrences.
If you have concerns about the way your work was graded, please contact the TA who graded it within one week of having the grade returned to you. After this one-week window, we may deny your request for re-evaluation. Also, please keep in mind that your grade may go up or down as a result of re-grading.
The academic enterprise is founded on honesty, civility, and integrity. As members of this enterprise, all students are expected to know, understand, and follow the codes of conduct regarding academic integrity. At the most basic level, this means submitting only original work done by you and acknowledging all sources of information or ideas and attributing them to others as required. This also means you should not cheat, copy, or mislead others about what is your work. Violations of academic integrity (i.e., misconduct) lead to the breakdown of the academic enterprise, and therefore serious consequences arise and harsh sanctions are imposed. For example, incidences of plagiarism or cheating may result in a mark of zero on the assignment or exam and more serious consequences may apply if the matter is referred to the President’s Advisory Committee on Student Discipline. Careful records are kept in order to monitor and prevent recurrences.
A more detailed description of academic integrity, including the University’s policies and procedures, may be found in the Academic Calendar at http://calendar.ubc.ca/vancouver/index.cfm?tree=3,54,111,0.
Students must correctly cite any code that has been authored by someone else or by the student themselves for other assignments. Cases of code plagiarism may include, but are not limited to:
- the reproduction (copying and pasting) of code with none or minimal reformatting (e.g., changing the name of the variables)
- the translation of an algorithm or a script from a language to another
- the generation of code by automatic code-generations software
An “adequate acknowledgement” requires a detailed identification of the (parts of the) code reused and a full citation of the original source code that has been reused.
Parts of this syllabus (particularly the policies) have been copied and derived from the UBC MDS Policies.