CS423 Parallel Computing
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| Course Code | Course Title | Weekly Hours* | ECTS | Weekly Class Schedule | ||||||
| T | P | |||||||||
| CS423 | Parallel Computing | 3 | 2 | 6 | Tuesday 3pm-5:50pm | |||||
| Prerequisite | CS302, CS307 | It is a prerequisite to | None |
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| Lecturer | Office Hours / Room / Phone | Currently not available |
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| Assistant | Assistant E-mail | |||||||||
| Course Objectives | The goal of this course is to equip students with Python basics, data analysis, data structures and visualization in Python. A student who successfully completes this course will have achieved the foundation to become a junior data scientist, capable of preprocessing, analyzing, visualizing and drawing useful insights from various datasets. The course does not assume any knowledge of Python per se, but it assumes prior programming experience, knowledge of basic probability, data structures and algorithms. |
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| Textbook | The main textbook: Python for Data Analysis, Wes McKinney, O'Reilly, 2013 (PDA) The secondary textbook: High Performance Python, Micha Gorelick and Ian Oszvald, O'Reilly, 2014 (HPP) | |||||||||
| Additional Literature |
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| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
| Teaching Methods | Practical and active problem-solving by the instructor and students, capstone data science project, exams. | |||||||||
| Teaching Method Delivery | Online | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Course introduction | N/A --- materials from lecture | ||||||||
| Week 2 | Environment setup/Jupyter overview / Python crash course intro | N/A --- materials from lecture | ||||||||
| Week 3 | Python crash course: Lists and tuples | HPP Ch 3 | ||||||||
| Week 4 | Python crash course: Dictionaries and sets | HPP Ch 4 | ||||||||
| Week 5 | Python for Data Analysis: NumPy | PDA Ch 4 | ||||||||
| Week 6 | Python for Data Analysis: Pandas | PDA Ch 5 | ||||||||
| Week 7 | Python for Data Analysis: Pandas | PDA Ch 5 | ||||||||
| Week 8 | Data loading, storage and file formats | PDA Ch6 | ||||||||
| Week 9 | Mid-term I | |||||||||
| Week 10 | Python for Data Visualization: Matplotlib // Data Capstone project out | PDA Ch8 (and more materials) | ||||||||
| Week 11 | Python for Data Visualization: Seaborn | PDA Ch8 (and more materials) | ||||||||
| Week 12 | Python for Data Visualization: Plotly / Python built-in visualization | PDA Ch8 (and more materials) | ||||||||
| Week 13 | Cufflinks, geographical plotting | PDA Ch8 (and more materials) | ||||||||
| Week 14 | Time series | PDA Ch10 | ||||||||
| Week 15 | Mid-term 2 // Data Capstone Project due | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs |
| Final Exam (Data Capstone Project)) | 1 | 30 | ||
| Semester Evaluation Components | ||||
| Mid-term I | 1 | 25 | ||
| Mid-term II | 1 | 25 | ||
| Tutorial weekly exercises | 1 | 20 | ||
| *** ECTS Credit Calculation *** | ||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lectures | 3 | 15 | 45 | Tutorials | 2 | 15 | 30 | |||
| In-term exam prep | 7 | 2 | 14 | Weekly self study | 3 | 15 | 45 | |||
| Data Capstone | 16 | 1 | 16 | |||||||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 06/09/2021 | |||||||||