DATA 100 001 - Introduction to Data Science in Python (3)
DATA 100 (3) Introduction to Data Science in Python (3)
Fundamentals of data science and programming with an emphasis on problem solving, testing, debugging, and working with data sets. Real-world applications from disciplines in the sciences, humanities, medicine, engineering, social sciences, business and others. No prior computing background is required.
Prerequisite: None
Equivalence: COSC 100
Upon successful completion of this course, students will be able to:
- use the Python programming language to complete everyday tasks.
- identify and use different Python data types to accomplish a variety of data science tasks.
- practice the creation of loops, conditionals, and functions to analyze data using Python.
- develop the ability to use programming principles to solve problems, conduct exploratory data analyses, create data visualizations, recognize patterns in data, and detect errors in code.
- appraise the quality of data and assess its limitations in answering questions.
- understand the role of testing and version control to writing sustainable code.
- apply common Python workflows to load, process, clean, and analyze data ethically.
- create reproducible, ethical, and sustainable data analyses.
| Item | Weight | Frequency |
|---|---|---|
| Learning Logs | 5% | Weekly |
| Labs | 25% | Weekly |
| Guided Project | 25% | Weekly |
| Tests | 25% | Bi-weekly |
| Final Exam | 20% | Scheduled during the exam period |
All students must satisfy ALL conditions to pass the course:
- Pass the Labs with an average grade of at least 50%, with no more than 4 missed labs.
- Pass the Tests with an average grade of at least 50%.
- Pass the Guided Project with a grade of at least 50%.
- Pass the Final Exam with a grade of at least 50%
- Pass the Course overall with a grade of at least 50%.
If a student does not satisfy the appropriate requirements, the student will be assigned the lower of their earned course grade or, a maximum overall grade of 45 in the course.
Portions of the following (open source) textbooks will be assigned as reading:
Eventually, an open textbook will be developed using open resources.
| Wk | Starting | Topics | Guided Project | Lab | Learning Logs | Tests |
|---|---|---|---|---|---|---|
| 1 | Week 1 | Introduction to Data Science | LL 1 | |||
| 2 | Week 2 | Terminal and Jupyter Notebook | L1 | LL 2 | Test 1 | |
| 3 | Week 3 | Version Control with Git | PM1 | L2 | LL 3 | |
| 4 | Week 4 | Introduction to Python | L3 | LL 4 | Test 2 | |
| 5 | Week 5 | Loading and working with data | PM2 | L4 | LL 5 | |
| 6 | Week 6 | Data Types: Lists and Dictionaries | L5 | LL 6 | Test 3 | |
| 7 | Week 7 | Computation with numpy |
PM3 | L6 | ||
| 8 | Week 8 | Controlling the flow | LL 7 | Test 4 | ||
| 9 | Week 9 | Organizing your code | PM4 | L7 | LL 8 | |
| 10 | Week 10 | Objects in Python | LL 9 | Test 5 | ||
| 11 | Week 11 | Data analysis with scipy and pandas |
PM5 | L8 | LL 10 | |
| 12 | Week 12 | Data visualization | L9 | LL 11 | ||
| 13 | Week 13 | Releases and Reproducibility | PM6 | L10 | LL 12 |