|Assignment 1||deadline: Sunday February 19 @ 11.59 pm|
|Assignment 2||deadline: Sunday March 26 @ 11.59 pm|
|Midterm exam||Monday April 3|
|Assignment 3||deadline: Sunday April 23 @ 11.59 pm|
|Presentations||Monday April 24 and Wednesday April 26|
|Final report||end of the semester|
In social systems, there is competition for prestige, recognition, awards, social status, popularity, leadership, wealth, fame, etc. What makes the difference in the achievement of these objectives? Is there any pattern behind success? For example, why an hashtag on twitter becomes popular, a song enters the hit parade, or a scientific paper becomes highly cited? This course will review quantitative studies aimed at measuring, predicting and understanding performance in social competitive arenas, ranging from social media to financial markets, from professional sports to scientific and technological innovation.
Students will learn:
• How to retrieve and extract performance data from the web or other electronic resources.
• How to measure and characterize performance from data with statistics and complex network theory.
• How to generate models for performance prediction.
• Participation: 10%.
Based upon attendance and participation.
• Quizzes: 5%.
Questions during the lectures.
• Assignments: 25%.
Homework assignments on data analysis and retrieval. They are part of the main research project.
• Midterm exam: 20%.
Format to be announced.
• Presentation and Discussion: 15%.
Students will present and lead the discussion of an article related to the class materials, or about the research project developed during the course (assignments and final report). This includes presenting concepts necessary to understand the article.
• Final Report: 25%
Final report about results obtained in the research project.
• Performance in professional sports.
• Scientific impact.
• Technological innovation.
• Popularity in social media.
• Measures of performance in financial markets.
• Basic knowledge of programming (e.g., python, perl).
• Basic knowledge of statistics.
• Use of laptops in class is allowed only during lab sessions. No email, facebook, games, or other distractions, please. • The main communication medium outside of class is Canvas. Students are expected to post their questions, answer other students' questions, post pointers to relevant technology news (do NOT copy and paste news articles!), and check Canvas daily for announcements. Postings must be signed in order to get participation credit. Direct email to instructors is to be used only for confidential matters. • Instructors cannot debug code via email. If you need help debugging, the best option is to go to office hours. • Students are responsible for making backups of all of their work! This includes any assignment and other materials you produce. • Students are responsible for the safe and ethical use of class accounts on shared servers, according to university policy and copyright law, and for the sole purpose of carrying out class assignments. Accounts will be monitored and any abuse will be reflected in the grades. • Students are responsible for assigned readings PRIOR to class discussions. • Students are required to attend class. • If you miss class, it is your responsibility to find out about any announcements or assignments you may have missed. • Late assignments will incur a penalty of 50% within 24 hours of the deadline, and no partial or make-up credit will be available after that. • Extenuating circumstances will normally include only serious emergencies or illnesses documented with a doctor's note. • Grades will be given out via Canvas only. • The instructor may take into account class trends in the assignment of final grades, but only to increase grades.
The principles of academic honesty and professional ethics will be vigorously enforced in this course, following the IU Code of Student Rights, Responsibilities, and Conduct, the Informatics Academic Regulations, and the CS Program Statement on Academic Integrity.
This includes the usual standards on acknowledgment of help, contributions and joint work, even when you are encouraged to build on libraries and other software written by other people. Any code or other assignment you turn in for grading and credit must be your individual work (except for group projects). Even if you work with a study group (which is encouraged), the work you turn in must be exclusively your own. If you turn in work done together with, or with the assistance of, anyone else other than the instructors, this is an instance of cheating.
Cases of academic misconduct (including cheating, fabrication, plagiarism, interference, or facilitating academic dishonesty) will be reported to the Office of the Dean of Students. The typical consequence will be an automatic F grade in the course.
Your submission of work to be graded in this class implies acknowledgement of this policy. If you need clarification or have any questions, please see the instructor during office hours.
We would like to hear from anyone who has a disability or other issues that may require some modifications or class adjustments to be made. The offices of Disability Services and Psychological Services are available for assistance to students. Please see the instructor after class or during office hours.
We would like to know early in the semester of any possible conflicts between course requirements/deadlines and religious observances, so that accommodations can be made. Please see the instructor after class or during office hours.
We welcome feedback on the class organization, material, lectures, assignments and exams. You can provide us with constructive criticism via the discussion forum. Please share your comments and suggestions so that we can improve the class.