For personal and confidential matters send an email to the instructors. and concept-check quizzes for lecture videos (2% total) The course web site will be used for posting section notes and links to Accommodations for students with disabilities. world. There are two types of sections: An Introduction to Statistical Learning by James, Witten, Hastie, Tibshirani. CS 109b: Data Science II: Advanced Topics in Data Science Note that Homework Zero will be graded. Ethical behavior is an important trait of a Data Scientist, from ethically handling data to attribution of code and work of others. This course is the first half of a oneyear course in data science. Details in the assignment. The course focuses on the analysis of messy, real-life data to perform predictions using statistical and machine learning methods. The course will include advanced sections for 209a students and will cover a different topic per week. If still unsatisfied with first regrading outcome, you may submit a reason via email to the Helpline with subject line Regrade HW1: Second request within 2 days of receiving the initial regarding response. Students It should be a report. Location: SEC 1.321 Lecture Hall About CS 181 provides a broad and rigorous introduction to machine learning, probabilistic reasoning and decision making in uncertain environments. you need to write your solutions entirely on your own or with your collaborator (e.g., not entirely from Google search results). Homeworks will be submitted on Gradescope. Our graders and instructors make every effort in grading accurately and in giving you a lot of feedback. ","stylingDirectives":null,"csv":null,"csvError":null,"dependabotInfo":{"showConfigurationBanner":false,"configFilePath":null,"networkDependabotPath":"/Harvard-IACS . You will be given the higher of the two. The main grading components of the course are the Grading errors If you believe there has been a grading error, Remember, not all assignments will permit group submissions. Quizzes are completed on Ed as well as all feedback forms. After that there will be a penalty of -1 point for both members of the group provided the submission was on time. In particular: Each student is allowed up to 3 late days over the semester with at most 1 day applied to any single homework. We will focus on the analysis of data to perform predictions using statistical and machine learning methods. 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. Important note: make sure you have your settings set so you can receive emails from Canvas. your interest to turn in partial or late homework rather than not anyone you worked with in your write-up. Go to Office Hours; this is the best way to get direct help. 1. Topics include data scraping, data management, data visualization, regression and classification methods, and deep neural networks. look at the solutions if you haven't yet turned in your CS 109a, AC 209a, Stat 121a, or CSCI E-109a, Pavlos Protopapas (SEAS), Kevin Rader (Statistics), & Chris Tanner (SEAS), Lectures: Mon, Wed, Fri at 9am-10:15am and 3pm-4:15pm, Sections: Fri 1:30-2:45 pm and Mon 8:30-9:45 pm. Thus, in CS109A we give a strong emphasis to Academic Honesty. The class meets, virtually, three days a week for lectures (M, W, F). should be comfortable with writing non-trivial programs (e.g. Quoting one of our favorite superheroes: with cs109a hw1.pdf - 9/22/2017 Walter Dwayne Final Walter See Calendar for specific dates. Sessions will be accompanied by relevant examples to clarify key concepts and techniques. 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. (includes text and code) or jointly type up an assignment. or equivalent). time, we understand that sometimes life throws a set of circumstances that two midterms (15% each, in March and April), To submit after Canvas has closed or to ask for an extension, send an email to the Helpline with subject line "Submit HW1: Reason=the flu" replacing 'HW1' with the name of the current assignment and "the flu" with your reason. NOTE: make sure you adjust your account settings so you can receive emails from Canvas. 18 Best Free AI Training Courses for 2023: Upskill Yourself Today - Tech.co Do not divide and conquer. You are responsible for understanding Harvard Extension School policies on academic integrity (https://extension.harvard.edu/for-students/student-policies-conduct/academic-integrity/)and how to use sources responsibly. For more detailed expectations, please refer to the Collaborations section above. CS109a: Introduction to Data Science. All homework and quizzes will be posted and submitted through Canvas, as well as all feedback forms. friends from previous years). If you prefer to speak with someone outside of the course, you may find helpful resources at the Harvard Office of Diversity and Inclusion. Syllabus; Schedule; Materials; FAQ; Preparation; Resources. your local clock is slow. Syllabus | CS181 - GitHub Pages Sections 3. Each module will integrate the five key facets of an investigation using data: 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. Accommodation Requests. We will discuss the motivations behind common machine learning algorithms, and the properties that determine whether or not they will work well for a particular task. The two labs have identical contents and you should plan to attend one of the two. School Harvard University Course Title CS 109 Uploaded By benpap45801207 Pages 8 This preview shows page 1 - 3 out of 8 pages. An Introduction to Statistical Learning by James, Witten, Hastie, Tibshirani. For questions about homework, course content, package installation, and after you have tried to troubleshoot yourselves, the process to get help is: We encourage you to talk and discuss the assignments with your fellow students (and on Piazza), but you are not allowed to look at any other students assignment or code outside of your pair. For homeworks beyond that we feel that you should be familiar with the process of joining groups. Sections will employ a flipped classroom format, in which students There will be quizzes at the end of each lecture to assess the understanding of the material that will help us identify gaps. All learning instanceslectures, labs, and sectionswill be recorded. submit a regrade request through Gradescope. Students who have previously taken CS 109, AC 209, or Stat 121 cannot take CS 109a, AC 209a, or Stat 121a for credit. As a student your best guidelines are to be reasonable and fair. code, or looking at your notebooks. For students not having access to canvas as yet, HW 0 is cs109a_hw0.ipynb in this folder. Watch on. (identical material) [starts 9/11], Advanced Sections: Wed at 12pm [starts 9/23]. Class announcements will be through Canvas. Consult the course calendar for exact dates. CS181: in the subject line. Coordinates: 422228N 710701W Harvard University is a private Ivy League research university in Cambridge, Massachusetts. Should this Also all feedback forms. is a useful resource for mathematical For questions about homework, course content, package installation, JupyterHub, and after you have tried to troubleshoot yourselves, the process to get help is: 1. For private matters send an email to the Helpline: cs109a2021@gmail.com. Lectures will be recorded and made available real time for DCE students and 24 hours later for in-campus students via Canvas. Only one of CS 109a, AC 209a, or Stat 121a can be taken for credit. We do not want to see code copied verbatim from the above sources. Team http://www-bcf.usc.edu/~gareth/ISL/ (Links to an external site). analysis exercise applying the concepts from lecture/readings to a small example. CS 109a: Data Science I: Introduction to Data Science. If you feel like your performance in the class is being impacted by your experiences outside of class, please do not hesitate to come and talk with us. Founded in 1636 as Harvard College and named for its first benefactor, the Puritan clergyman John Harvard, it is the oldest institution of higher learning in the United States. We (like many people) are still learning about diverse perspectives and identities. Lectures will include one or more coding exercises focused on the newly introduced material; there will be no AC209a content in the exercises. 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. 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. On weeks with new assignments, the assignments will be released by Wednesday 3pm. You will get ample practice through weekly homework assignments. Please contact us (in person or electronically) or submit anonymous feedback if you have any suggestions to improve the diversity of the course materials. 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). Students will create a solution in the form of a software package, which will require varying levels of research. the staff to provide detailed feedback. an honor code violation to look up solutions to the 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. Fall 2021 - Harvard University, Institute for Applied Computational Science. Remember that you can also submit anonymous feedback (which will lead to us making a general announcement to the class, if necessary, to address your concerns). Harvard CS109A | Materials - GitHub Pages be mindful of your mic settings after returning to the main room from a break-out room, as it tends to un-mute each person's mic. It is an honor code violation to Note for Simultaneous Enrollment: Students considering simultaneous enrollment must note that regular attendance able to fill in gaps in their knowledge. Many are getting accustomed to Zoom for the first time. Part I of the textbook great power (to run any kind of analysis) comes great responsibility However, please note that a) we will regrade the 3/1 to Thursday 3/3 cover content from the lectures on Thursday 2/24 and For personal and confidential matters send an email to the. 1 & 2: From Basics to Practice by Andrew Glassner, Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville (MIT Press, 2016). HOLLIS: http://link.springer.com.ezp-prod1.hul.harvard.edu/book/10.1007%2F978-1-4614-7138-7. Chris: Wednesday 3-4pm, Maxwell-Dworkin B125. The tex source and code will be used Students interested in a more advanced, optimization-based 1. data collection data wrangling, cleaning, and sampling to get a suitable data set 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). A lecture will have the following pedagogy layout which will be repeated: 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. CS109a focuses on the analysis of data to perform predictions using statistical and machine learning methods. if you work with a fellow student and want to submit the same assignment, you need to form a group prior to the submission. This will include, reading from the textbooks or other sources, watching videos to prepare you for the class. and bonuses for an especially creative or successful approach. Welcome to CS109a/STAT121a/AC209a, also offered by the DCE as CSCI E-109a, Introduction to Data Science. Free electronic version: http://www-bcf.usc.edu/~gareth/ISL/ (Links to an external site). As a participant in course discussions, you should also strive to honor the diversity of your classmates. In general, we expect you to use your late days first the whole point If you find that you have used up all your In particular: There are no late days in homework submission. Fall 2021 - Harvard University, Institute for Applied Computational Science. 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. The teaching staff monitors the posts. Fall 2021 - Harvard University, Institute for Applied Computational Science. The section cycle restarts each Tuesday after lecture, when a new section 4. questions, but you must cite your sources (and you should be ready to Material covered will integrate the five key facets of an investigation using data: 1. data collection - data wrangling, cleaning, and sampling to get a suitable. Courses | Institute for Applied Computational Science - Harvard University students and can be accessed through the Zoom section on Canvas. You have the option to work and submit in pairs for all the assignments except HW3 and HW6, which you will do individually. Your final grade will be calculated twice: one including exercise grades and one without. The class meets for lectures twice a week (M & W). 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). The course covers a broad range of topics in data science, including data cleaning, visualization, analysis, and machine learning. The lectures will be live-streamed and can be accessed through the Zoom section on Canvas. We will discuss the motivations behind common machine learning algorithms, Failure to do so may result in the Course Head's inability to respond in a timely manner. enforced by the site, so submit early enough Go to Office Hours, this is the best way to get help. You should also be careful not to share information about the midterm with Do not remove any original copyright notices and headers. The material covered on Friday and Monday is identical. Note: Advanced Sections are not held every week. The course covers a broad range of topics in data science, including data cleaning, visualization, analysis, and machine learning. Introduction to Data Science CS 109A, STAT 121A, AC 209A, CSCI E-109A Syllabus - Fall 2019 Welcome to CS109a/STAT121a/AC209a, also offered by the DCE as CSCI E-109a, Introduction to Data Science. Pavlos: Monday 6:30-7:30 PM [IACS Office]; 7:30-8 PM [Online]. Live online instruction followed by a short Q/A session. There will be 7 graded homework assignments. 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. 2. data management accessing data quickly and reliably 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. common methods, as well as apply machine learning to challenges with real and practical directions. Advanced Sections are held Weds 4:30-5:45 pm at Maxwell Dworkin G115. Lab sessions are held Thur 4:30-5:45 pm in Pierce 301. The material covered on Friday and Monday is identical. The CS 181 team consists of the course instructor Weiwei Pan as the instructors, or members of the teaching staff, with any concerns. 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. Welcome to CS109a/STAT121a/AC209a, also offered by the DCE as CSCI E-109a, Introduction to Data Science. 3. exploratory data analysis generating hypotheses and building intuition Databases and newsletters and courses, oh my! HLS - Harvard Law School Preparing for this course Syllabus Introduction to Data Science (Fall 2020) CS 109a, AC 209a, Stat 121a, or CSCI E-109a Course Heads Pavlos Protopapas(SEAS), Kevin Rader(Statistics), & Chris Tanner(SEAS) Instructor:Eleni Kaxiras (SEAS) Lectures:Mon, Wed, Fri at 9am-10:15am and 3pm-4:15pm There will be a quiz at the end of the class based on what was discussed in lecture. One can cause real harm by pursuing a good cause via You need a HUID to be included to Canvas. 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. 10 minutes of Q&A regarding the pre-class exercises and/or review of homework and quiz questions. The material covered in the advanced sections is required for all AC209a students. 1. We take great care in making sure all homework assignments are graded properly. be submitted in LaTeX and will be returned with grades and solutions. Important Note: once regrading is done, you may receive a grade that is higher or lower than the initial grade. We want everyone to be comfortable in the course and empowered Start early and plan ahead! You are expected to be intellectually honest and give credit where credit is due. Course Instructor SAT/UNSAT Performance SAT/UNSAT Performance 10a Harvard Racliffe Orchestra Cortese M. 7:15 - 9:15, F. 3-5:30 (Sanders) 10b Harvard Radcliffe Orchestra Cortese M. 7:15 - 9:15, F. 3-5:30 (Sanders) Contribute to Harvard-IACS/2018-CS109A development by creating an account on GitHub. The Helpline is monitored by the teaching staff. AC209a students will have additional homework content for most assignments worth 1 point. Harvard CS109A | Lab 09: - GitHub Pages Harvard's CS109A course is an introductory course in data science, designed for students with some prior programming experience. If something was said in class (by anyone) that made you feel uncomfortable, please talk to us about it. 2018-CS109A/syllabus.md at master Harvard-IACS/2018-CS109A These qualities take cultivation and effort. environment. HOLLIS: http://link.springer.com.ezp-prod1.hul.harvard.edu/book/10.1007%2F978-1-4614-7138-7. The lowest 1/3 of your quiz scores will be dropped. still be some bugs, and if you find any please be a good citizen and put Only one of CS 109a, AC 209a, or Stat 121a can be taken for credit. Standard assignments are graded out of 5 points. As a student your best guidelines are to be reasonable and fair. Data Science and Computer Science have historically been representative of only a small sliver of the population. Welcome to CS109a/STAT121a/AC209a, also offered by the DCE as CSCI E-109a, Introduction to Data Science. Upon completion of this challenging project, students will be better equipped to conduct research and enter the professional world. Institute for Applied Computational Science. A tag already exists with the provided branch name. (to do it properly)! 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.) Lecture 2 - Prediction, k-Nearest Neighbors. Developed by IACS Scientific Program Director,Pavlos Protopapas, the Capstone Research course is a group-based research experience where students work directly with a partner from industry, government, academia, or an NGO to solve a real-world data science/ computation problem. You have the option to work and submit in pairs for all the assignments except HW4 and HW7 which you will do individually. Students will work in groups of 2-4 to complete a final group project, due during the Exams period. 2. Academic Integrity. Up to two late days The homework are graded on a scale 1 to 5, where 5 is the highest grade. To help accomplish this: If you have a name and/or set of pronouns that differ from those in your official Harvard records, please let us know! is a concept quiz for you to check your understanding - the quizzes will be graded based on Our course will discuss diversity, inclusion, and ethics in data science. This course is the first half of a oneyear course to data science. 2. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. For more detailed expectations, please refer to the Collaborations section above. Instructions for Setting up Your Environment. We will accept late submissions only for medical (if accompanied by a doctor's note) or other official University-excused reasons. Harvard CS109A | Syllabus Students will work in groups of 2-4 to complete a final group project, due during the Exams period. At the same Course Website Students interested primarily of one of the synchronous interactive course components (lectures or sections) is required. We will focus on the analysis of 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. Students should The class material integrates the five key facets of an investigation using data: Please refer to Academic Honesty in The CS109A Grade linked here The CS109A Grade. Please note that auditors may not submit assignments for grading or make use of other limited student resources such as office hours. 5. communication summarizing results through visualization, stories, and interpretable summaries and the properties that determine whether or not they will work well for a Students as expected to be actively engaged with the course. All homework and will be posted and submitted through Canvas. Attending and participating in lectures is a crucial component of learning the material presented in this course. 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. Note for Homework 0: Homework 0 will be graded for completion (i.e. in theory may prefer STAT 195 and other learning theory offerings. discretion. Discussion is encouraged; copying is not allowed. Attendance is optional, however it is strongly encouraged. To help accomplish this: If you have a name and/or set of pronouns that differ from those in your official Harvard records, please let us know! Labs are held on Thur 4:30-6:00 pm and Fri 10:30-11:45 am in Pierce 301. 9/22/2017 Walter+Dwayne+Final Walter Thornton working with Dwayne Kennemore CS 109A/STAT 121A/AC 209A/CSCI E-109A: Homework done with one other student, and that is more open-ended in nature. More details in class. If still unhappy with the initial response, then submit a reason via email to the Helpline with subject line "Regrade HW1: Second request" within 2 days of receiving the initial response. Thesetwo courses are the core of an introduction todata scienceat Harvard. and what you send may be seen by the entire course staff. You will be allowed to bring Offered: 2017 Artificial Intelligence (AI) is an exciting field that has enabled a wide range of cutting-edge technology, from driverless cars to grandmaster-beating Go programs. If you prefer to speak with someone outside of the course, you may find helpful resources at the Harvard Office of Diversity and Inclusion. Go to Office Hours; this is the best way to get direct help. We expect you to adhere to the Harvard Honor Code at all times. Each weeks section covers the previous week's Thursday lecture content docs plugins themes themes_old .gitignore LICENSE README.md config.py requirement.txt README.md 2020-CS109A Repository background (specifically Sections 2.1-2.6; 3.1-3.5; 4.1-4.2; 5.1-5.6; 6.3). orientation may prefer CS 183. Textbook Amazon: https://www.amazon.com/Introduction-Statistical-Learning-Applications-Statistics/dp/1461471370 (Links to an external site). The lectures are pre-recorded Failure to do so may result in the Course Head's inability to respond in a timely manner. CS 109a, AC 209a, Stat 109a, or CSCI E-109a, Pavlos Protopapas (SEAS) and Natesh Pillai (Statistics), Wed (starts 9/21) 2:15-3:30pm [SEC 1.321] (Not every, week; see course schedule on Canvas for exact dates), Welcome to CS109a/STAT109a/AC209a, also offered by the DCE as CSCI E-, messy, real-life data to perform predictions using statistical and machine learn-. Students will have a limited amount of time to complete the quiz (DCE students will have 72 hours). Institute for Applied Computational Science. The course also includes a significant group project component, where students work in teams to apply their skills to a real-world data science problem. and made available at the beginning of each week. homework and the midterms. Lectures This course follows a flipped classroom structure. All discussions will remain confidential. The goal of CS 181 is to combine mathematical derivation and coding appropriately adjust). Successful completion of this assignment will show that this course is suitable for you. We want to be a resource for you. way to handle the situation. their support of your extra needs (we would not need a doctor's note especially if you work with a fellow student but decide to submit individual assignments, include the name of each other in the designated area of the submission. The goal of this course is to introduce the ideas and techniques underlying the design of intelligent computer systems. Ed rather than email. Do not divide and conquer. Some of them will be due in a week (1, 2, 5, 8) and some of them in two weeks (3, 4, 6, 7). Class announcements will be through Ed. We will have in class quizzes to assess your understanding of the material and to help us identify gaps. Topics include data scraping, data management, data visualization, regression and classification methods, and deep neural networks. You are not allowed to work with partner. The Harvard Law School Library is here for you as you prepare for a new semester! honor code violation to look at the solutions if you haven't yet turned Preparation Syllabus TENTATIVE SYLLABUS SUBJECT TO CHANGE 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 The goal of the course is to instill a strong technical background for you used to bump up grades for students on grade boundaries. Recordings will then be made available to all students within 24 hours via Canvas. Students looking for specialized topics This is a strict deadline, The quizzes will be available until the next lecture. HW0 is designed to test your knowledge on the prerequisites. 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, Part one of a two part series, the curriculum for this course builds throughout, the academic year. impact your performance in the course beyond what the late day policy can If you do not receive a response from course staff on your extension request Lectures will include one or more coding exercises focused on the, newly introduced material; there will be no AC209a content in the exercises.

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