Data Processing Using Python

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Description

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About this course: This course (The English copy of "用Python玩转数据" <https://www.coursera.org/learn/hipython/home/welcome>) is mainly for non-computer majors. It starts with the basic syntax of Python, to how to acquire data in Python locally and from network, to how to present data, then to how to conduct basic and advanced statistic analysis and visualization of data, and finally to how to design a simple GUI to present and process data, advancing level by level. This course, as a whole, based on Finance data and through establishment of popular cases one after another, enables learners to more vividly feel the simplicity, elegance and robustness of Python. Also, it discusses the fast, …

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When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: This course (The English copy of "用Python玩转数据" <https://www.coursera.org/learn/hipython/home/welcome>) is mainly for non-computer majors. It starts with the basic syntax of Python, to how to acquire data in Python locally and from network, to how to present data, then to how to conduct basic and advanced statistic analysis and visualization of data, and finally to how to design a simple GUI to present and process data, advancing level by level. This course, as a whole, based on Finance data and through establishment of popular cases one after another, enables learners to more vividly feel the simplicity, elegance and robustness of Python. 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. Similarly, it may also be flexibly applied into other fields. The course has been updated. Updates in the new version are : 1) the whole course has moved from Python 2.x to Python 3.x 2) Added manual webpage fetching and parsing. Web API is also added. 3) Improve the content order and enrich details of some content especially for some practice projects.

Created by:  Nanjing University
  • Taught by:  ZHANG Li, associate professor

    Department of Computer Science
Level Beginner Commitment 3-5 hours/week Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.0 stars Average User Rating 4.0See what learners said Coursework

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Nanjing University Nanjing University (NJU) is committed to excellence in teaching and research. Located on the prosperous eastern coast of China, NJU provides a dynamic environment that nurtures learning, creativity, and discovery on one of the most beautiful campuses in the country. Taking NJU's university offerings on Coursera will be a rewarding experience for learners from every corner of the world.

Syllabus


WEEK 1


Welcome to learn Data Processing Using Python!



Hi, guys, welcome to learn “Data Processing Using Python”(The English version of "用Python玩转数据", url is https://www.coursera.org/learn/hipython/home/welcome)!In this course, I tell in a manner that enables non-computer majors to understand how to utilize this simple and easy programming language – Python to rapidly acquire, express, analyze and present data. Many cases are provided to enable you to easily and happily learn how to use Python to process data in many fields.


1 video, 2 readings expand


  1. Video: Promotion Video
  2. Reading: Teaching Methods
  3. Reading: FAQ


Basics of Python



Hi, guys, welcome to learn Module 01 “Basics of Python”! I’ll first guide you to have a glimpse of its simplicity for learning as well as elegance and robustness. Less is more: the author of Python must know this idea well. After learning this module, you can master the basic language structures, data types, basic operations, conditions, loops, functions and modules in Python. With them, we can write some useful programs!


15 videos, 3 readings expand


  1. Video: 1 Introduction to Python
  2. Video: 2 The First Python Program
  3. Video: 3 Basics of Python Syntax
  4. Video: 4 Data Types of Python
  5. Video: 5 Basic Operations of Python
  6. Video: 6 Functions, Modules and Packages of Python
  7. Reading: 1.1 References
  8. Reading: 1.1 Programming exercises(Not Graded)
  9. Video: 1 Conditions
  10. Video: 2 range
  11. Video: 3 Loops
  12. Video: 4 break, continue and else in Loops
  13. Video: 5 Self-defined Functions
  14. Video: 6 Recursion
  15. Video: 7 Scope of Variable
  16. Reading: 1.2 Coding and programs reading(Not Graded)
  17. Video: A1: Standard Library Functions
  18. Video: A2: Exceptions
  19. Discussion Prompt: the characteristic of recursive algorithm

Graded: Walk into Python quiz
Graded: About Python quiz
Graded: find out the 6-th Monisen number(3 points)

WEEK 2


Data Acquisition and Presentation



Welcome to learn Module 02 “Data Acquisition and Presentation”! After learning this module, you can master the modes of acquiring local data and network data in Python and use the basic and yet very powerful data structure sequence, string, list and tuple in Python to fast and effectively present data and simply process data.


6 videos, 5 readings expand


  1. Video: 1 Local Data Acquisition
  2. Video: 2 Network Data Retrieval
  3. Reading: 2.1 References(re)
  4. Reading: 2.1 Internet Data Retrival Programming exercise(Not Graded)
  5. Reading: 2.1 code snippets for reference only
  6. Video: 1 Sequence
  7. Video: 2 String
  8. Video: 3 List
  9. Video: 4 Tuple
  10. Reading: Sequence fuctions practice
  11. Discussion Prompt: KO Math Whiz
  12. Reading: Sequences and Files Programming Exercise(No Graded)

Graded: Data Acquisition and Presentation quiz

WEEK 3


Powerful Data Structures and Python Extension Libraries



Welcome to learn Module 03 “Powerful Data Structures and Python Extension Libraries”! Have you felt you are closer to using Python to process data? After learning this module, you can master the intermediate-level and advanced uses of Python: data structure dictionaries and sets. In some applications, they can be very convenient. What’s special here is that, you can also feel the charm of such concise and efficient data structures: ndarray, Series and DataFrame in the most famous and widely applied scientific computing package SciPy in Python.


7 videos, 3 readings expand


  1. Video: 1 Why Are Dictionaries Needed
  2. Video: 2 Dictionary Use
  3. Video: 3 Set
  4. Reading: 3.1 Programming exercise(Not Graded)
  5. Video: 1 Extension Library SciPy
  6. Video: 2 ndarray
  7. Video: 3 Series
  8. Video: 4 DataFrame
  9. Reading: 3.2 References
  10. Discussion Prompt: ufunc functions
  11. Reading: 3.2 Programming exercise for DataFrame(Not Graded)

Graded: Powerful Data Structures and Python Extension Libraries quiz

WEEK 4


Python Data Statistics and Visualization



Welcome to learn Module 04 “Python Data Statistics and Visualization”! In this module, I will show you, over the entire process of data processing, the unique advantages of Python in data processing and analysis, and use many cases familiar to and loved by us to learn about and master methods and characteristics. After learning this module, you can fast and effectively mine your desired or expected or unknown results from a large amount of data, and can also present those data in various images. In addition, the data statistics modes of all third party packages in Python are extraordinarily and surprisingly strong, but we, as average persons, can still understand and possess them.


14 videos, 9 readings expand


  1. Video: 1 Convenient and Fast Data Acquisition
  2. Video: 2 Data Preparations
  3. Video: 3 Data Display
  4. Video: 4 Data Selection
  5. Video: 5 Simple Statistics and Processing
  6. Video: 6 Grouping
  7. Video: 7 Merge
  8. Reading: 4.1 References
  9. Reading: 4.1.1 code snippets for reference only
  10. Reading: 4.1.2 code snippets for reference only
  11. Reading: Chinese Web API - TuShare
  12. Video: 1 Cluster
  13. Video: 2 Basics of Matplotlib Plotting
  14. Video: 3 Control of Matplotlib Image Attributes
  15. Video: 4 Plotting with pandas
  16. Video: 5 Data Access
  17. Video: 6 Applications of Python into Science and Engineering Fields
  18. Video: 7 Applications into Humanities and Social Sciences Fields
  19. Reading: 4.2 Programming exercise for comparing the stock data(No Graded)
  20. Reading: 4.2 code snippets for reference only
  21. Reading: 4.2.1 Extension: Scikit-learn Machine Learning Basics
  22. Reading: 4.2.4&4.2.5: Analyze test results using Box-plot
  23. Reading: 4.2.6 Extension: Introduction to WAV audio processing

Graded: Basic Data Statistics of Python quiz
Graded: Advanced Data Processing and Visualization of Python quiz
Graded: Movies review programming exerciese(4 points)

WEEK 5


Object Orientation and Graphical User Interface



Welcome to Module 05 “Object Orientation and Graphical User Interface”! In this module, I will guide you to understand what object orientation is and the relationship between graphical user interface and object orientation. Learners are only required to understand the concepts so that you can more freely and easily pick up various new functions in future. No program writing is required here. Besides, you also need to master the basic framework of GUI, common components and layout management. After learning them, you will find development with GUI is actually not remote. It has an Easter egg, too ~~~


8 videos, 2 readings expand


  1. Video: 1 GUI and Object Orientation
  2. Video: 2 Abstraction
  3. Video: 3 Inheritance
  4. Video: 1 Basic Framework of GUI
  5. Video: 2 Common Components of GUI
  6. Video: 3 Layout Management
  7. Video: 4 Other GUI Libraries
  8. Video: 5 Comprehensive Applications
  9. Reading: 5.2 Comprehensive practice project
  10. Reading: 5 code snippets for reference only

Graded: Object Orientation and Graphical User Interface quiz
Graded: Examination
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