DATA 151: Introduction to Data Science, Fall 2023

This syllabus is subject to change based on specific class needs, especially the schedule. Significant deviations will be discussed in class. Individual exceptions to the policies and schedule are granted only in cases of true emergency. Please make arrangements with me if an emergency arises.




A complete introduction to the full data science workflow, spanning initial investigation and data acquisition to the communication of final results. Students will learn through case studies and hands-on experience. Includes a basic introduction to a high-level programming language, data exploration and wrangling, data summarization and visualization, basic statistical modeling, and working on and sharing projects collaboratively.

Student Learning Objectives


The course textbook will be:

Ismay, Chester and Kim, Albert Y. Modern Dive: An Introduction to Statistical and Data Sciences in R. Online:

We may also uses other resources, such as R for Data Science and How to Think Like a Data Scientist.

All resources used will be freely available online or provided to you.


There are several apps/accounts we will be using for this course.

  1. We will use the R programming language through the RStudio development environment. I recommend students install this software on their own machines. However, lab machines can be provided for student use if necessary.

  2. An account at Runestone Academy. Your account will be automatically created and you will be emailed your account information at the start of the semester.

  3. During class I will often ask interactive questions using the Socrative app. Students will need to create a free student account at While you can participate in Socrative sessions via a web browser, I recommend using the free iOS or Android apps available here.


Assignments and Workload

The weekly workload for this course will vary by student and by week but should be about 12.5 hours per week on average. The following table provides a rough estimate of the distribution of time over different course components for a 16 week semester, as well as detailing the type, amount, and relative value of all assignments.

Category Amount Final Grade Weight Time/Week (Hours)
Lectures ~41 10% (Participation) 2.5
Labs 7–10 15% 3
Homework 5–8 15% 1
Exam Study - - 1
Exams 4–5 30% -
Project 1 30% 3
Reading -   2
Total     12.5

Exams: All exams are weighted equally and will take approximately the same amount of time. Exams will generally focus on material covered since the previous exam but will be in some sense cumulative due to the nature of programming. Unless stated otherwise, assume that exams will be pencil and paper and that computers will not be available during the exam period.

Projects: One large-scale data analysis project will be undertaken during the semester. These projects will be group efforts and will require much more effort than the programs written in lab or as part of homework. Some lab periods may be dedicated to work on the project. It is highly recommend that all students make ample use of the time given on these projects.

Labs: Most weeks will include an in-class lab assignment. Students will sometimes be placed into pairs for “paired programming”, a programming practice where each member of the group takes turns typing while the other group member helps look for typos, bugs, and otherwise assists in the design of the code. Each group will submit their work at the end of the lab period regardless of the overall completeness of the assignment. The goal is to make good constructive progress on the assignment. Full credit can and will be given on unfinished work so long as it can be executed to complete some portion of the given task, shows evidence of purposeful progress, and the group made full use of the lab period.

Homework: Students will be also be assigned homework problems during some weeks. These problems are meant to guide reading, prepare the student for in-class problems, and survey the material covered by the exam. Each student will turn in their own set of solutions.


Your final grade is based on a weighted average of particular assignment categories. You can estimate your current grade based on your scores and these weights. You may always visit the instructor outside of class to discuss your current standing. Assignments and final grades use a standard grading scale shown below and will not be curved except in rare cases when deemed necessary by the instructor.

This courses uses a standard grading scale. Assignments and final grades will not be curved except in rare cases when its deemed necessary by the instructor. Percentage grades translate to letter grades as follows:

Score Grade
94–100 A
90–93 A-
88–89 B+
82–87 B
80–81 B-
78–79 C+
72–77 C
70–71 C-
68–69 D+
62–67 D
60–61 D-
0–59 F

You are always welcome to challenge a grade that you feel is unfair or calculated incorrectly. Mistakes made in your favor will never be corrected to lower your grade. Mistakes made not in your favor will be corrected. Basically, after the initial grading your score can only go up as the result of a challenge*.

You are always welcome to challenge a grade that you feel is unfair or calculated incorrectly. Mistakes made in your favor will never be corrected to lower your grade. Mistakes made not in your favor will be corrected. Basically, after the initial grading your score can only go up as the result of a challenge.

Lab and homework assignments are graded on a simple 3 point scale, marked with (in decreasing order) a check-plus, check, or check minus. Your final grade for these two assignment categories is then based on the respective averages.

Your participation grade is based on a variety of activities, but especially daily use of Socrative for in-class question and answer sessions. Questions will cover portions of the text that were assigned as reading and will range from simple checks to see if the reading was done to more challenging questions that follow from a close examination of the reading. For the most part, the only requirement is to provide an answer to every question and participate in the resultant discussions. On occasion, questions will be evaluated for their correctness and performance on 3 these questions will also factor into the course participation grade. Students who do the reading and start the homework as soon as possible will have very little to worry about.

While there is no strict attendance policy, the course participation grade is based in large part on engagement with socrative. Absent students cannot participate in socrative sessions. Students should avoid unexcused absences, as defined in the college-wide absence policy. Whenever possible, let the instructor know of the absence before it occurs. When unexcused absences do occur, it is the student’s responsibility to make up for the lost class time and to seek the permission of the instructor to hand-in or complete assignments that are late due to an unexcused absence.

This course is designed around the assumption that students engage in new ideas before they’re covered in class meetings. This means doing assigned reading, taking a stab at homework problems, and as a result coming to class and lab with some understand about a new idea or, just as likely, with a host of questions about something encountered in the reading and homework. Not attending class, skipping lab, and putting off work to the point that an extension is needed are signs that a student isn’t holding up their end of the bargain and is not prepared to participate in class.



The following tentative calendar should give you a feel for how work is distributed throughout the semester. Assignments and events are listed in the week they are due or when they occur. This calendar is subject to change based on the circumstances of the course.

Note: All readings should be done before the class period in which they are listed below.

Date Topic Assignment and Readings
Wed 08/23 (Week 1) Intro and Logistics  
Wed 08/23 (lab) Lab 0: RStudio and Google Sheets  
Fri 08/25 How to Think Like a Data Scientist HTTLADS ch. 1, 2.1
Mon 08/28 (Week 2) Case Study: The Happiness Report HTTLADS 2.2
Wed 08/30 (continued) HTTLADS 2.2
Wed 08/30 (lab) Lab 01: The Happiness Report HTTLADS 2.3, Homework 1
Fri 09/01 (continued) HTTLADS 2.4
(Mon 09/04) (Week 3) (Labor Day – no classes)  
Wed 09/06 Intro R, RStudio, R Packages MD 1
Wed 09/06 (lab) Lab 02: Intro to R and R Markdown  
Fri 09/08 Grammar of Graphics MD 2-2.3.1
Mon 09/11 (Week 4) Overplotting, Linegraphs MD 2.3.2-2.4
Wed 09/13 Histograms, Facts, Boxplots, Barplots MD 2.5-2.9
Wed 09/13 (lab) Lab 03: Data Viz with ggplot2  
Fri 09/15 Data Wrangling I MD 3.1-3.3
Mon 09/18 (Week 5) Exam 1 Review  
Wed 09/20 (No morning class)  
Wed 09/20 (lab) Exam 1 (during lab)  
Fri 09/22 (No class)  
Mon 09/25 (Week 6) Data Wrangling II MD 3.4-3.6
Wed 09/27 Data Wrangling III MD 3.7-3.9
Wed 09/27 (lab) Lab 04/Hwk 02: Data Wrangling  
Fri 09/29 Data Wrangling III Review MD 3, Project out
Mon 10/02 (Week 7) (No class – power outage)  
Wed 10/04 Importing; Tidy data MD 4.1-4.2
Wed 10/04 (lab) Lab 05/Hwk 03: Tidying  
Fri 10/06 Case Study MD 4.3-4.5
Mon 10/09 (Week 8) Simple Linear Regression MD 5.1
Wed 10/11 Hwk 03 Questions Projects groups and data due
Wed 10/11 (lab) (Reserved for project work)  
(Fri 10/13) (Fall Break)  
Mon 10/16 (Week 9) Regression with a categorical explanatory variable MD 5.2-5.4
Wed 10/18 Exam 2 Review  
Wed 10/18 (lab) Lab 06: Regression  
Fri 10/20 Exam 2  
Mon 10/23 (Week 10) Exam 2 Solutions  
Wed 10/25 Review of Regression with One Variable Review MD 5
Wed 10/25 (lab) Lab 07: More Regression  
Fri 10/27 Multiple Regression Project proposal due
Mon 10/30 (Week 11) Multiple Regression  
Wed 11/01 (No class)  
Wed 11/01 (lab) (Reserved for project work)  
Fri 11/03 Multiple Regression Project proposal peer review due
Mon 11/06 (Week 12) Modeling  
Wed 11/08 Exam 3 Review  
Wed 11/08 (lab) Lab 08: Multiple Regression  
Fri 11/10 Exam 3  
Mon 11/13 (Week 13) Exam 3 Solutions  
Wed 11/15 Sampling I MD 7.1-7.2
Wed 11/15 (lab) (Reserved for project work)  
Fri 11/17 Sampling II MD 7.3-7.6
Mon 11/20 (Week 14) Bootstrapping and Confidence Intervals MD 8, Initial project submission due
(Wed 11/22) (Thanksgiving Break)  
(Fri 11/24) (Thanksgiving Break)  
Mon 11/27 (Week 15) Hypothesis Testing MD 9
Wed 11/29 Hypothesis Testing II MD 9
Wed 11/29 (lab) (More Hypothesis Testing / Project Work)  
Fri 12/01 Inference for Regression MD 10, Project resubmission due
Mon 12/04 (Week 16) Ethical Considerations HTTLADS 7
Wed 12/06 Presentations Project presentations
Wed 12/06 (lab) (Review / Project Report Work)  
Sat 12/09 3:00 PM Exam 4 (Final)  

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