This course is a gentle introduction to Data Science. The following will be discussed:
Statistics knowledge at the level of STAT 1000 or above. No prior knowledge of programming required.
Baumer et al., Modern Data Science with R. CRC Press.
| Grade | Percentage |
|---|---|
| A+ | [97%,100%] |
| A | [93%,97%) |
| A- | [90%,93%) |
| B+ | [87%,90%) |
| B | [83%,87%) |
| B- | [80%,83%) |
| C+ | [77%,80%) |
| C | [73%,77%) |
| C- | [70%,73%) |
| D+ | [67%,70%) |
| D | [63%,67%) |
| D- | [60%,63%) |
| F | [0,60%) |
Students in this course will be expected to comply with the University of Pittsburgh’s Policy on Academic Integrity. Any student suspected of violating this obligation for any reason during the semester will be required to participate in the procedural process, initiated at the instructor level, as outlined in the University Guidelines on Academic Integrity. This may include, but is not limited to, the confiscation of the examination of any individual suspected of violating University Policy. Furthermore, no student may bring any unauthorized materials to an exam, including dictionaries and programmable calculators.
To learn more about Academic Integrity, visit the Academic Integrity Guide for an overview of the topic. For hands-on practice, complete the Understanding and Avoiding Plagiarism tutorial.
These materials may be protected by copyright. United States copyright law, 17 USC section 101, et seq., in addition to University policy and procedures, prohibit unauthorized duplication or retransmission of course materials. See Library of Congress Copyright Office and the University Copyright Policy.
To ensure the free and open discussion of ideas, students may not record classroom lectures, discussion and/or activities without the advance written permission of the instructor, and any such recording properly approved in advance can be used solely for the student’s own private use.
The University of Pittsburgh does not tolerate any form of discrimination, harassment, or retaliation based on disability, race, color, religion, national origin, ancestry, genetic information, marital status, familial status, sex, age, sexual orientation, veteran status or gender identity or other factors as stated in the University’s Title IX policy. The University is committed to taking prompt action to end a hostile environment that interferes with the University’s mission. For more information about policies, procedures, and practices, see: http://diversity.pitt.edu/affirmative-action/policies-procedures-and-practices.
I ask that everyone in the class strive to help ensure that other members of this class can learn in a supportive and respectful environment. If there are instances of the aforementioned issues, please contact the Title IX Coordinator, by calling 412-648-7860, or e-mailing titleixcoordinator@pitt.edu. Reports can also be filed online: https://www.diversity.pitt.edu/make-report/report-form. You may also choose to report this to a faculty/staff member; they are required to communicate this to the University’s Office of Diversity and Inclusion. If you wish to maintain complete confidentiality, you may also contact the University Counseling Center (412-648-7930).
If you have a disability for which you are or may be requesting an accommodation, you are encouraged to contact both your instructor and Disability Resources and Services (DRS), 140 William Pitt Union, (412) 648- 7890, drsrecep@pitt.edu, (412) 228-5347 for P3 ASL users, as early as possible in the term. DRS will verify your disability and determine reasonable accommodations for this course.
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Please visit https://www.coronavirus.pitt.edu/ and check your Pitt email for updates before each class.
| Class | Lecture | Day | Date | Topics | Reading | Due Day |
|---|---|---|---|---|---|---|
| 1 | 1 | F | Aug 27 | Introduction | MDSR Ch.1 | |
| 2 | 2 | M | Aug 30 | R Basics (1) | MDSR Appendix B | |
| 3 | 3 | W | Sep 1 | R Basics (2) | MDSR Appendix B | |
| 4 | F | Sep 3 | Lab 1: R Basics | |||
| – | M | Sep 6 | Labor Day: No Class | |||
| 5 | 4 | W | Sep 8 | Data Visualization (1) | MDSR Ch.2 | |
| 6 | F | Sep 10 | Lab 2: R Markdown | HW 1 | ||
| 7 | 5 | M | Sep 13 | Data Visualization (2) | MDSR Ch.3 | |
| 8 | 6 | W | Sep 15 | Data Visualization (3) | MDSR Ch.3 | |
| 9 | F | Sep 17 | Lab 3: Data Visualization | HW 2 | ||
| 10 | 7 | M | Sep 20 | Data Visualization (4) | MDSR Ch.3 | |
| 11 | 8 | W | Sep 22 | Data Wrangling: One Table (1) | MDSR 4.1,4.2 | |
| 12 | F | Sep 24 | Quiz 1: Data Visualization | HW 3 | ||
| 13 | 9 | M | Sep 27 | Data Wrangling: One Table (2) | MDSR 4.1,4.2 | |
| 14 | 10 | W | Sep 29 | Data Wrangling: Two Tables (1) | MDSR 4.3 | |
| 15 | F | Oct 1 | Lab 4: Visualization and Wrangling | |||
| 16 | 11 | M | Oct 4 | Data Wrangling: Two Tables (2) | MDSR 4.3 | |
| 17 | 12 | W | Oct 6 | Summary of Data Wrangling | MDSR 5.1, 5.2 | |
| 18 | F | Oct 8 | Lab 5: Data Wrangling to explore baseball database | HW 4 | ||
| 19 | 13 | M | Oct 11 | Statistical Foundation | MDSR 7.1, 7.2 | |
| 20 | 14 | W | Oct 13 | Sampling Distribution and Bootstrap | MDSR 7.3 | |
| – | F | Oct 15 | Fall Break: No Class | HW 5 | ||
| 21 | 15 | M | Oct 18 | Statistical Modeling: Regression (1) | MDSR 7.5 | |
| 22 | 16 | W | Oct 20 | Statistical Modeling: Regression (2) | MDSR 7.6 | |
| 23 | F | Oct 22 | Lab 6: Tidying data with tidyr |
|||
| 24 | 17 | M | Oct 25 | Supervised Learning (1) | MDSR 8.1 | |
| 25 | 18 | W | Oct 27 | Supervised Learning (2) | MDSR 8.1 | |
| 26 | F | Oct 29 | Quiz 2: Data Wrangling | HW 6 | ||
| 27 | 19 | M | Nov 1 | Supervised Learning (3) | MDSR 8.2 | |
| 28 | 20 | W | Nov 3 | Supervised Learning (4) | MDSR 8.2 | |
| 29 | F | Nov 5 | Lab 7: Visualizing regression results | |||
| 30 | 21 | M | Nov 8 | Supervised Learning (5) | MDSR 8.4 | |
| 31 | 22 | W | Nov 10 | Supervised Learning (6) | ||
| 32 | F | Nov 12 | Quiz 3: Regression | HW 7 | ||
| 33 | 23 | M | Nov 15 | Unsupervised Learning (1) | MDSR 9.1 | |
| 34 | 24 | W | Nov 17 | Unsupervised Learning (2) | MDSR 9.2 | |
| 35 | F | Nov 19 | Lab 8: Many Models (1) | |||
| – | Nov 22-26 | Thanksgiving Break: No Class | ||||
| 36 | 25 | M | Nov 29 | Machine Learning Summary | ||
| 37 | 26 | W | Dec 1 | Professional Ethics | MDSR Ch.6 | |
| 38 | F | Dec 3 | Quiz 4: Machine Learning | HW 8 | ||
| 39 | 27 | M | Dec 6 | R Programming (1) | ||
| 40 | 28 | W | Dec 8 | R Programming (2) | ||
| 41 | F | Dec 10 | Lab 9: Many Models (2) |