This course is designed for non-computer majors to acquire comprehensive and practical knowledge of Python. Starting with the basic syntax of Python, the course will guide students step by step on how to local and network data acquision, data presentation, statistic analysis and visualization of data, and finally a design of a simple GUI to present and process data. 

This course, as a whole, based on Finance data and through the establishment of popular cases one after another, enables learners to feel the simplicity, elegance, and robustness of Python more vividly. Also, it discusses the fast, convenient and efficient data processing capacity of Python in humanities and social sciences fields like literature, sociology and journalism and science and engineering fields like mathematics and biology, in addition to business fields. Theory and methodolody in this course can be applied to problems in other fields flexibly.

The course is mainly for non-computer majors but can also serve as a good reference for others too.

Note: Videos are in Chinese with English subtitles and all other materials are in English in this course.

Love Python, love data analysis and love Python for data analysis!

This course is mainly for non-majors, not limited to a certain professional and educational level. You need to have some knowledge of a programming language (not necessarily Python) or have some basic concepts of programming such as some basic algorithms(for example, prime number judgment). Learners who don't have such kind of knowledge need to complete relevant knowledge and conduct programming exercises according to the course's own progress during the course.

1. Python IDE

(1) Anaconda (the most convenient way)

https://www.anaconda.com/download

(2) PyCharm

https://www.jetbrains.com/pycharm/

(3) Visual Studio Code

https://code.visualstudio.com/Download

2. References

(1) Magnus Lie Hetland, Beginning Python From Novice to Professional (Third Edition).

(2) Python 3.8.2 documentation: https://docs.python.org/3/

(3) SciPy ecosystem: https://www.scipy.org/

(4) Wes McKinney, Python for Data Analysis.

(5) Python tutorial

(6) Classic Python learning websites and forums: realpython (https://realpython.com/),  PythonGeeks (https://pythongeeks.org/) or the forum of StackOverflow (https://stackoverflow.com/questions/)

Q: Can non-computer majors understand this course?

A: This course is mainly for the learners who have learned programming languages or have basic concepts of programs such as data structure, sequence, selection and loop control structure and common algorithms, but are not limited to computer majors. In the course, we try to use simple cases to let the learners understand how to process data using Python. As long as you learn the content in the video and reference materials carefully, complete the homework and conduct more programming practice after class, most of the content can be mastered.

Q: Can I fully grasp the course content if I complete watching the course video?

A: The learning of MOOC course has its particularity for example the duration is shorter and the basis of each learner is different, so we provide more in-video quiz, programming questions, small projects cases and reference answers for futher learning. We recommend that you complete those quizs, question and projects to understand the content after the video learning as needed.