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Python and Other Technologies for Data Science
Lecture, three hours; discussion, one hour. Enforced requisite: course 20. Covers use of Python and other technologies for data analysis and data science. Focus on programming with Python and selection of its libraries: NumPy, pandas, matplotlib, and scikit-learn, for purpose of data processing, data cleaning, data analysis, and machine learning. Other technologies covered include Jupyter notebook and Git. Intended for Data Theory majors as introduction to Python language and libraries most frequently used in data science. Letter grading.
Review Summary
- Clarity
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6.7 / 10
- Organization
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8.3 / 10
- Time
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5-10 hrs/week
- Overall
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6.7 / 10
Reviews
Great lecture structure and homework. The final was extremely difficult. I had a 99.8 going into the final, received a 60% after the curve, and ended with an 89.7%.
Great class. Very well organized and clear. Miles is awesome
This class was extremely useful, as we learned the tools (Python) needed for data analysis and data science. I really felt more equipped with my data-related skills after taking the course. Professor Chen was also very clear and organized with his lecture, and his homework were great practice for what we needed to know for Python.
Miles gives very clear and structured lectures. The course does what it sets out to do and gives the barebones needed to get started with data science using Python, Git, SQL, etc. The homework was not difficult, but the final was extremely difficult. Overall, it was an enjoyable class.
Displaying all 4 reviews
Course
Grading Information
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No group projects
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Attendance required
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No midterms
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Finals week final
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50% recommend the textbook
Previous Grades
Grade distributions not available.