DATA 101: Making Predictions with Data
2023W1 Course Syllabus
We respectfully acknowledge the Syilx (Okanagan) Peoples and their peoples, in whose unceded territory UBC Okanagan is situated.
Course Information
Section 1: DATA 101 - 001
Instructor: Dr. Irene Vrbik Office: SCI 104 email: irene.vrbik@ubc.ca
Location & Time: ART 103 - Mon Wed 12:30 14:00
Office Hours: SCI 104 - Wed 14:00 15:00
Section 2 (DATA 101 - 002)
Instructor: Ladan Tazik email: ladan.tazik@ubc.ca
Location & Time: ART 103 - Tue Thu 12:30 14:00
Office Hours: Tuesday 3:30-4:30 at FIP 309
Course Objectives
DATA 101 (3) Making Predictions with Data:
Introduction to the techniques and software for handling real-world data. Topics include data cleaning, visualization, simulation, basic modelling, and prediction making. [3-1-0]
Course Format
The course is delivered through 3 hours of lectures and 1 hour of labs. Please check your registration to determine your lab/tutorial section and time. You must register for a lab and attend the one you are registered in.
Course Overview
In this course students will acquire the necessary skills to collect, visualize, analyze, and interpret data to make informed decision. This introductory course will be using R and does not require any previous coding experience.
This course introduces students to the data science workflow which involves: asking a research question, obtaining data, exploratory data analysis, data modelling, and effective communication and visualization of the results. While a multitude of modelling approaches exist, this course will cover the introductory aspects of supervised machine learning, such as regression and classification. Students will be introduced to a variety of data sets, highlighting that the same concepts can be applied to several fields of study. Simulations are designed to emphasize the link between the application and theory. There will be an emphasis on intelligent and reproducible workflow, and clear communications of findings.
Learning Outcomes
By the end of the course, students will be able to:
- Import data into R from various sources
- Clean, manipulate, and transform data from the original format to one appropriate for a variety of downstream purposes such as analytics
- Create and interpret effective visualizations from data
- Conduct statistical techniques for identify patterns and trends in data
- Build a regression model and make predictions
- Build a simple classifier and interpret the output
- Effectively communicate findings in a reproducible RMarkdown document
Textbook
We are using an open source textbook available free on the web:
- Timbers, T., Campbell, T., Lee, M. (2022). Data Science: A First Introduction. United States: CRC Press. https://datasciencebook.ca/
Other useful textbooks include:
Grolemund, G. (2014). Hands-On Programming with R: Write Your Own Functions and Simulations. United States: O’Reilly Media. https://rstudio-education.github.io/hopr/
Wickham, H., Grolemund, G. (2016). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. United States: O’Reilly Media. https://r4ds.had.co.nz/
Peng, R. D. (2016). R Programming for Data Science. United States: Lulu.com. https://bookdown.org/rdpeng/rprogdatascience/
Tentative Schedule
Below is the tentative course schedule for lectures.
| Week | Lectures | |
| 1 | Introduction To R, RStudio and common packages | |
| 2 | R Syntax, Data Types, Operations and Function in R | |
| 3 | Reading in data locally and from the web | |
| 4 | Cleaning and wrangling data | |
| 5 | Review/Holiday (Oct 2) + Quiz 1 | |
| 6 | Holiday (Oct 9) + Effective data visualization and data summary | |
| 7 | Introduction to prediction models - Classification | |
| 8 | Regression I | |
| 9 | Quiz 2 + Regression II : simple linear regression | |
| 10 | Introduction to clustering using K-means | |
| Midterm break | ||
| 11 | Statistical Inference | |
| 12 | Quiz 3 + Introduce sampling and estimation for sample means and proportions. | |
| 13 | Review/ Catch up |
Please note the important Dates and Deadlines
- Start of classes: Tuesday, September 5
- Last day to drop without a W standing through the SSC 1: September 18, 2023
- Midterm break: November 13 – 17
- Last day of classes: Thursday, December 7
- Exams Start Sunday, December 10
- Exams Finish Thursday, December 21
There will be no class, student hours, or labs on the following Statutory holidays:
- Monday, September 4: Labour Day
- Saturday, September 30: National Day for Truth and Reconciliation
- Monday, October 2: National Day for Truth and Reconciliation (observed)
- Monday, October 9: Thanksgiving Day
- Saturday, November 11: Remembrance Day
- Monday, November 13: Remembrance Day (observed)
If you celebrate any other holidays that are not listed above, please feel free to contact me directly if you feel that they will potentially conflict with the outlined course structure.
Evaluation
| Grade Item | Weights |
|---|---|
| Assignments | 25 |
| Quizzes | 30 |
| Exam | 45 |
Passing/Grading Criteria: Students MUST attain a grade of at least 50% according to the Grading Scheme weights outlined in the table above.
Assignments: There will be approximately seven assignments. Assignments will be submission to Canvas.
Quizzes:There will be 3 quizzes, (invigilated in-person) at the same time & location of the lectures. Tentative Quizzes’ schedule:
| Quiz Number | Date (DATA 101 001) | Date (DATA 101 002) |
|---|---|---|
| Quiz 1 | October 4, 2023 | October 5, 2023 |
| Quiz 2 | November 1, 2023 | November 2, 2023 |
| Quiz 3 | November 22, 2023 | November 23, 2023 |
Late policy
Late submissions for assignments are accepted only within 2 days after the deadline. Assignments that are up to 24 hours are automatically subject to a 10% reduction. Assignments between 24 – 48 hours are automatically subject to a 20% reduction. No arrangements can be made to upload your solution for assignments at alternate times.
Missed Quizzes
If you miss a quiz, the weight of that quiz will be shifted to the final exam. No make-up quizzes will be given.
Academic Integrity
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 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 usually result in a failing grade or mark of zero on the assignment or in the course. Careful records are kept to monitor and prevent recidivism.
A more detailed description of academic integrity, including the University’s policies and procedures, may be found in the Academic Calendar.integrity. At the most basic level, this means submitting only original work
Final Examinations
You can find the Senate-approved term and examination dates here. Except in the case of examination clashes and hardships (three or more formal examinations scheduled within a 27-hour period) or unforeseen events, students will be permitted to apply for out-of-time final examinations only if they are representing the University, the province, or the country in a competition or performance; serving in the Canadian military; observing a religious rite; working to support themselves or their family; or caring for a family member. Unforeseen events include (but may not be limited to) the following: ill health or other personal challenges that arise during a term and changes in the requirements of an ongoing job.
Further information on Academic Concession can be found under Policies and Regulation in the Okanagan Academic Calendar.
Grading Practices
Faculties, departments, and schools reserve the right to scale grades in order to maintain equity among sections and conformity to University, faculty, department, or school norms. Students should therefore note that an unofficial grade given by an instructor might be changed by the faculty, department, or school. Grades are not official until they appear on a student’s academic record.
Further information on Grading Practices can be found in the Okanagan Academic Calendar.
Resources to Support Student Success:
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Email: learning.hub@ubc.ca
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