Introduction to Data Science (Fall 2021)

CS 109a, AC 209a, Stat 121a, or CSCI E-109a

Course Heads

Pavlos Protopapas (SEAS) and Natesh Pillai (Statistics)

Lectures: Mon & Wed 9:45am-11am - SEC Room 1.321

Labs: Friday 9:45am-11am - 114 Western Ave., Allston Room 2.111

Advanced Sections: Wed 12:45-2pm [starts 9/29] - SEC LL2.229 (see schedule for dates)

Office Hours: Current Times With More To Come

Prerequisites: You are expected to have programming experience at the level of CS 50 or above, and statistics knowledge at the level of Stat 100 or above (Stat 110 recommended). HW #0 is designed to test your knowledge on the prerequisites. Successful completion of this assignment will show that this course is suitable for you. HW #0 will not be graded but you are required to submit.

Welcome to CS109a/STAT121a/AC209a, also offered by the DCE as CSCI E-109a, Introduction to Data Science. This course is the first half of a one‐year course in data science. The course focuses on the analysis of messy, real-life data to perform predictions using statistical and machine learning methods.

Throughout the semester, our content continuously centers around five key facets:

1. data collection ‐ data wrangling, cleaning, and sampling to get a suitable data set;

2. data management ‐ accessing data quickly and reliably;

3. exploratory data analysis – generating hypotheses and building intuition;

4. prediction or statistical learning; and

5. communication – summarizing results through visualization, stories, and interpretable summaries.

Only one of CS109a, AC209a, or STAT121a can be taken for credit. Students who have previously taken CS109, AC209, or STAT121 cannot take CS109A, AC 209A, or STAT121A for credit.

Course Components

The lectures, labs, and advanced sections will be recorded and accessed through the Zoom section on Canvas for Extension School students. Attendance is required for on campus students.


The class meets for lectures twice a week for lectures (M & W). Attending and participating in lectures is a crucial component of learning the material presented in this course.

What to expect Class Structure

A lecture will have the following pedagogy layout which will be repeated:


Lab will be held every Friday at the same time and place as lectures. Labs guided hands-on coding challenges which will prepare students for successfully completing the homework assignments.


At the end of each lecture, there will be a short, graded quiz that will cover the pre-class and in-class material; there will be no AC209a content in the quizzes. The quizzes will be available until the next lecture.

25% of the quizzes will be dropped from your grade.


Lectures will include one or more coding exercises focused on the newly introduced material; there will be no AC209a content in the exercises. The exercises are short enough to be completed during the time allotted in lecture but they will remain available until the beginning of the following lecture to accomodate those who cannot attend in real time.

Your final grade will be calculated twice: one including exercise grades and one without. You will be given the higher of the two. In this way, exercises can only help your grade.

Advanced Sections

The course will include advanced sections for 209a students and will cover a different topic per week. These are 75-min lectures and will cover advanced topics like the mathematical underpinnings of the methods seen in lecture and hands-on exercises, along with extensions of those methods. The material covered in the advanced sections is required for all AC209a students. But all students are welcome and encouraged to attendadvanced sections.

Note: Advanced sections are not held every week. Consult the course schedule for exact dates.


There will be a midterm exam on October 15th.


Students will work in groups of 2-4 to complete a final group project, due during the Exams period. See schedule for specific dates.

Homework Assignments

There will be 7 graded homework assignments. Some of them will be due one week after being assigned, and some will be due two weeks after being assigned. You have the option to work and submit in pairs for all the assignments except HW3 and HW6, which you will do individually.

You will be working in Jupyter Notebooks, which you can run in your own environment or in the SEAS JupyterHub cloud.

[Instructions for Setting up Your Environment] (coming soon)

[Instructions for Using JupyterHub] (coming soon)

On weeks with new assignments, the assignments will be released by Wednesday 3pm.

Standard assignments are graded out of 5 points.

AC209a students will have additional homework content for most assignments worth 1 point.

Instructor Office Hours

Natesh: (TBD)

Pavlos: Monday 6:30-7:30 PM [IACS Office]; 7:30-8 PM [Online]


Students are expected to be actively engaged with the course. This includes:

  1. Attending and participating in lectures
  2. Making use of office hours
  3. Participating in the Ed discussion forum — both through asking thoughtful questions and by answering the questions of others

Recommended Textbook

An Introduction to Statistical Learning by James, Witten, Hastie, Tibshirani.

The book is available here:

Free electronic version: (Links to an external site).


Amazon: (Links to an external site).

Course Policies

Getting Help

For questions about homework, course content, package installation, JupyterHub, and after you have tried to troubleshoot yourselves, the process to get help is:

1. Post the question in Ed and get a response from your peers. Note that in Ed questions are visible to everyone. The teaching staff monitors the posts.

2. Go to Office Hours; this is the best way to get direct help.

3. For private matters send an email to the Helpline: The Helpline is monitored by the teaching staff.

4. For personal and confidential matters send an email to the instructors.

Collaboration Policy

We expect you to adhere to the Harvard Honor Code at all times. Failure to adhere to the honor code and our policies may result in serious penalties, up to and including automatic failure in the course and reference to the ad board. If you work with a partner on an assignment make sure both parties solve all the problems. Do not divide and conquer. You are expected to be intellectually honest and give credit where credit is due. In particular:

Late or Wrongly Submitted Assignments

Each student is allowed up to 3 late days over the semester with at most 1 day applied to any single homework. Outside of these allotted late days, late homework will not be accepted unless there is a medical (if accompanied by a doctor's note) or other official University-excused reasons. There is no need to ask before using one of your late days.

If you forgot to join a Group with your peer and are asking for the same grade we will accept this with no penalty up to HW3. For homeworks beyond that we feel that you should be familiar with the process of joining groups. After that there will be a penalty of -1 point for both members of the group provided the submission was on time.

Grading Guidelines

Homework will be graded based on:

1. How correct your code is (the Notebook cells should run, we are not troubleshooting code)

2. How you have interpreted the results — we want text not just code. It should be a report.

3. How well you present the results.

The scale is 0 to 5 for each assignment.

Re-grade Requests

Our graders and instructors make every effort in grading accurately and in giving you a lot of feedback.

If you discover that your answer to a homework problem was correct but it was marked as incorrect, send an email to the Helpline with a description of the error. Please do not submit regrade requests based on what you perceive is overly harsh grading, The points we take off are based on a grading rubric that is being applied uniformly to all submissions.

If you decide to send a regrade request, send an email to the Helpline with subject line "Regrade HW1: Grader=johnsmith"  replacing 'HW1' with the current assignment and 'johnsmith' with the name of the grader within 48 hours of the grade release.

Communication from Staff to Students

Class announcements will be through Ed. All homework and will be posted and submitted through Canvas. Quizzes are completed on Ed as well as all feedback forms.

NOTE: make sure you adjust your account settings so you can receive emails from Canvas.

Submitting an assignment

Please consult [Homework Policies & Submission Instructions] (coming soon)

Course Grade

Your final score for the course will be computed using the following weights:

Assignment Final Grade Weight
Homework 0 1%
Paired Homework (5) 35% (7% per HW)
Individual Homework (2) 16% (8% per HW)
Midterm 10%
Quizzes 6%
Exercises 6%
Project 26%
Total 100%


We will be using Jupyter Notebooks, Python 3, and various python modules. You can access the notebook viewer either on your own machine by installing the Anaconda platform (Links to an external site) which includes Jupyter/IPython as well all packages that will be required for the course, or by using the SEAS JupyterHub from Canvas. Details in class.

Auditing the Class

If you would like to audit the class, please send an email to the Helpline indicating who you are and why you want to audit the class. You need a HUID to be included to Canvas. Please note that auditors may not submit assignments for grading or make use of other limited student resources such as office hours.

Academic Integrity

Ethical behavior is an important trait of a Data Scientist, from ethically handling data to attribution of code and work of others. Thus, in CS109A we give a strong emphasis to Academic Honesty. As a student your best guidelines are to be reasonable and fair. We encourage teamwork for problem sets, but you should not split the homework and you should work on all the problems together. For more detailed expectations, please refer to the Collaborations section above.

Accommodations for Students with Disabilities

Students needing academic adjustments or accommodations because of a documented disability must present their Faculty Letter from the Accessible Education Office (AEO) and speak with the professor by the end of the second week of the term, (fill in specific date). Failure to do so may result in the Course Head's inability to respond in a timely manner. All discussions will remain confidential, although Faculty are invited to contact AEO to discuss appropriate implementation.

Diversity and Inclusion Statement

Data Science and Computer Science have historically been representative of only a small sliver of the population. This is despite the contributions of a diverse group of early pioneers - see Ada Lovelace, Dorothy Vaughan, and Grace Hopper for just a few examples.

As educators, we aim to build a diverse, inclusive, and representative community offering opportunities in data science to those who have been historically marginalized. We will encourage learning that advances ethical data science, exposes bias in the way data science is used, and advances research into fair and responsible data science.

We need your help to create a learning environment that supports a diversity of thoughts, perspectives, and experiences, and honors your identities (including but not limited to race, gender, class, sexuality, religion, ability, etc.) To help accomplish this:

Our course will discuss diversity, inclusion, and ethics in data science. Please contact us (in person or electronically) or submit anonymous feedback if you have any suggestions for how we can improve.

Extension School Policies

Accommodation Requests. Harvard Extension School is committed to providing an inclusive, accessible academic community for students with disabilities and chronic health conditions. The Accessibility Services Office (ASO) ( offers accommodations and support to students with documented disabilities. If you have a need for accommodations or adjustments, contact Accessibility Services directly via email at or by phone at 617-998-9640.

Academic Integrity. You are responsible for understanding Harvard Extension School policies on academic integrity ( how to use sources responsibly. Stated most broadly, academic integrity means that all course work submitted, whether a draft or a final version of a paper, project, take-home exam, online exam, computer program, oral presentation, or lab report, must be your own words and ideas, or the sources must be clearly acknowledged. The potential outcomes for violations of academic integrity are serious and ordinarily include all of the following: required withdrawal (RQ), which means a failing grade in the course (with no refund), the suspension of registration privileges, and a notation on your transcript.

Using sources responsibly ( is an essential part of your Harvard education. We provide additional information about our expectations regarding academic integrity on our website. We invite you to review that information and to check your understanding of academic citation rules by completing two free online 15-minute tutorials that are also available on our site. (The tutorials are anonymous open-learning tools.)