Syllabus

• Course description
• Tentative Course Topics
• Course Objectives
• Course Components
  • Lectures
  • Labs
  • In-class Quizzes
  • Advanced Sections
  • Exams
  • Projects
  • Homework Assignments
• Course Resources
  • Online Materials
  • Recommended Textbooks
  • Getting Help
• Course Policies and Expectations
  • Grading
  • Collaboration Policy
  • Late or Wrongly Submitted Assignments
  • Re-grade Requests
  • Auditing the Class
  • Academic Integrity
  • Accommodations for students with disabilities
  • Diversity and Inclusion Statement

Course description

Data Science 2 is the second half of a one-year introduction to data science. Building upon the material in Data Science 1, the course introduces advanced methods for data wrangling, data visualization, and deep neural networks, statistical modeling, and prediction. Topics include big data and database management,  multiple deep learning subjects such as CNNs, RNNs, autoencoders, and generative models as well as basic Bayesian methods, nonlinear statistical models and unsupervised learning.

The programming language will be python.

Tentative Course Topics

• Feed Forward Neural Networks
• Regularization of Neural Network
• Optimization of Neural Networks
• Autoencoders
• Convolutional Neural Networks
• Recurrent Neural Networks
• Variational AutoEncoders
• Generative Adversarial Networks
• Smoothing and Additive Models
• Unsupervised Learning
• Bayesian Inference Models, LDA
• SQL
• AWS

Course Objectives

Course Components

Lectures

Labs

In-class Quizzes

Advanced Sections

Exams

Projects

Homework Assignments

Course Resources

Online Materials

https://github.com/Harvard-IACS/2019-CS109B. Assignments will only be posted on Canvas.

Working environment

Recommended Textbooks

• ISLR: An Introduction to Statistical Learning. by James, Witten, Hastie, Tibshirani (Springer:  New York, 2013)
• DL: Deep Learning by Goodfellow, Bengio and Courville.

Free electronic versions are available (ISLR), (DL) or hard copy through Amazon (ISLR), (DL).

Getting Help

• try to troubleshoot yourself by reading the lecture, lab, and section notes, and looking up online resources.
• go to office hours this is the best way to get help.
• post on the class Piazza; we want you and your peers to engage in helping each other. TFs also monitor Piazza and will respond within 24 hours. Note that Piazza questions are visible to everyone. If you are citing homework solution code you want to post privately so that only the staff sees your message.
• watch for official announcements via Canvas so make sure you have your Canvas notifications turned on. Piazza should always be your first resource for seeking answers to your content questions.
• send an email to the Helpline (cs109b2019@gmail.com) for administrative issues, regrade requests, and non-content specific questions we have this email account.
• for personal matters that you do not feel comfortable sharing with the TFs, you may send an email to either or both of the instructors.

Course Policies and Expectations

Grading for CS109b, STAT121b, and CS209b:

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:

• if you work with a fellow student but decide to submit different papers, include the name of each other in the designated area of the submission paper.
• if you work with a fellow student and want to submit the same paper you need to form a group prior to the submission. Details in the assignment. Not all assignments will permit group submissions.
• you need to write your solutions entirely on your own or with your collaborator
• you are welcome to take ideas from code presented in labs, lecture, or sections but you need to change it, adapt it to your style, and ultimately write your own. We do not want to see code copied verbatim from the above sources.
• if you use code found on the internet, books, or other sources you need to cite those sources.
• you should not view any written materials or code created by other students for the same assignment;
• you may not provide or make available solutions to individuals who take or may take this course in the future.
• if the assignment allows it you may use third-party libraries and example code, so long as the material is available to all students in the class and you give proper attribution. Do not remove any original copyright notices and headers.

Late or Wrongly Submitted Assignments

Re-grade Requests

Auditing the Class

Academic Integrity

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, like many fields of science, has historically only been represented by a small sliver of the population. This is despite some of the early computer scientist pioneers being women (see Ada Lovelace and Grace Hopper for two examples). Recent initiatives have attempted to overcome some barriers to entry: Made w/ Code. We would like to attempt to discuss diversity in data science from time to time where appropriate and possible. Please contact us (in person or electronically) or submit anonymous feedback if you have any suggestions to improve the diversity of the course materials. Furthermore, we would like to create a learning environment for our students that supports a diversity of thoughts, perspectives and experiences, and honors your identities (including race, gender, class, sexuality, religion, ability, etc.) To help accomplish this:

• If you have a name and/or set of pronouns that differ from those that appear in your official Harvard records, please let us know!
• If you feel like your performance in the class is being impacted by your experiences outside of class, please don’t hesitate to come and talk with us. We want to be a resource for you. Remember that you can also submit anonymous feedback (which will lead to me making a general announcement to the class, if necessary to address your concerns). If you prefer to speak with someone outside of the course, you may find helpful resources at the Harvard Office of Diversity and Inclusion.
• We (like many people) are still in the process of learning about diverse perspectives and identities. If something was said in class (by anyone) that made you feel uncomfortable, please talk to us about it. (Again, anonymous feedback is always an option.)
• As a participant in course discussions, you should also strive to honor the diversity of your classmates.