Instructor
- Slobodan Vucetic, email: vucetic@temple.edu, Office: 314 SERC; Hours: Thursday 2:00pm-3:00pm
TA
- Section 001: Sai Shi, email: tul67232@temple.edu, Office: 303 SERC, Hours Wednesday 2:00-3:00 pm
- Section 002: Tamara Katic, email: tamarakatic@gmail.com, Office: 303 SERC, Hours Wednesday 2:00-3:00 pm
Time
- Lectures: Tuesday and Thursday 9:30am – 10:50am in SERC 110B
- Labs: Section 001 Monday 9:00am – 10:50am in SERC 357; Section 002 Monday 9:00am – 10:50am in SERC 359
Course Description
- Internet and advances in technology allow us to collect massive amounts of diverse types of data. There is an increasing recognition that data could be translated into valuable insights. A data scientist is a person who has the skills, knowledge, and ability to extract actionable knowledge from the data -- either for the good of society, advancement of science, or profit in business. This class is an introduction to the practice of data science. The student will leave the class with a broad set of practical skills for doing data science including collecting, cleaning, and preprocessing data, exploring the data, applying methods from machine learning to analyze the data, and communicating the results. The students will gain experience in dealing with “big data,” which are too big to fit in the computer’s memory.
Course Website
Prerequisites
- CIS 2166 or linear algebra, CIS 1051 or 1057 or 1068
Textbook
This course does not have a designated textbook. The readings will be assigned from free web sources as needed. The following books are recommended:
- Peter Bruce, “Practical Statistics for Data Scientists: 50 Essential Concepts,” 2017.
- Wes McKinney. “Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython.” O'Reilly Media, 2012.
- Foster Provost, Tom Fawcett. “Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking.” O'Reilly Media, 2013.
- Cole Nussbaumer Knaflic, “Storytelling with Data: A Data Visualization Guide for Business Professionals,” 2015.
Syllabus
- Using Python for data science
- Data preprocessing
- Exploratory data analysis
- Statistical analysis of data
- Dealing with big data
- Machine learning
- Unsupervised learning
- Supervised learning
- Evaluation
- Data visualization and communicating results
Exams, Project and Grading
- Class attendance and participation: 10%
- Labs and homework: 25%
- Quizzes: 20%
- Midterm: 25%
- Final project: 20%
Regulations
- Class and lab attendance is mandatory and will be recorded. Absences for legitimate professional activities and illnesses are acceptable only if prior notice is given to the instructor by e-mail or phone. Scheduling conflicts with your work, extra-curricular activities, or any other such activities is not a valid excuse.
- Class participation means that you attend class regularly and have completed your assigned readings. It means that you ask relevant questions and make informed comments in class.
- We will have weekly quizzes that test your progress.
- Late assignments are not accepted.
- All work submitted for credit must be your own. Feel free to use resources from the web, but make sure to acknowledge the sources. You may discuss the problems with your classmates, but you should acknowledge their help too. Do not copy their solutions. Please ask if you have any questions about the Honor Code. Violations of the honor code will be treated seriously. Please check the Temple University policy on Plagiarism and Academic Cheating.
Disability Disclosure
Students with disabilities, including "invisible" disabilities such as chronic diseases and learning disabilities, are encouraged to discuss with us any appropriate accommodations that we might make on their behalf. Student must provide me with a note from the office of Disability Resources and Services at in 100 Ritter Annex, 215-204-1280, regarding their disability.