About this Course

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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 the 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.

Note: videos are in Chinese (Simplified) with English subtitles. All other materials are in English.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Beginner Level

Approx. 29 hours to complete

English

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Skills you will gain

Python Programming
Numpy
Pandas
Wxpython

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Beginner Level

Approx. 29 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Instructor rating

4.47/5 (52 Ratings)

ZHANG Li

associate professor

Department of Computer Science

179,148 Learners

2 Courses

Syllabus - What you will learn from this course

Content Rating92%(2,415 ratings)

Week1

Week 1

24 minutes to complete

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 based on SciPy, Requests, Beautiful Soup libraries etc. Many cases are provided to enable you to easily and happily learn how to use Python to process data in many fields.

24 minutes to complete

1 video (Total 4 min), 2 readings

8 hours to complete

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!

8 hours to complete

16 videos (Total 170 min), 5 readings, 3 quizzes

16 videos

1 Introduction to Python5m2 The First Python Program16m3 Basics of Python Syntax15m4 Data Types of Python9m5 Basic Operations of Python10m6 Functions, Modules and Packages of Python8m1.1 Extension: Build a Python Environment4m1 Conditions12m2 range5m3 Loops15m4 break, continue and else in Loops11m5 Self-defined Functions14m6 Recursion11m7 Scope of Variable4mA1: Standard Library Functions14mA2: Exceptions10m

5 readings

1.1 Walk into Python slides10m1.1 References10m1.1 Programming exercises(Not Graded)10m1.2 Multi-dimensional View of Python slides10m1.2 Control structure & function exercises(9 questions)10m

2 practice exercises

Walk into Python quiz30mMore About Python quiz24m

Week2

Week 2

4 hours to complete

Data Acquisition and Presentation

4 hours to complete

10 videos (Total 139 min), 5 readings, 1 quiz

10 videos

1 Local Data Acquisition22m2 Network Data Retrieval20m2.1 Extension: RE introduction16m2.1 Extension: Dynamic web crawling example5m1 Sequence8m2 String17m3 List14m4 Tuple7m2.2 Extension: IO&functional programming15m2.2 Extension: Mutable objects modify issue9m

5 readings

2 Data Retrieval and Represent slides10m2.1 Internet Data Retrival Programming exercise(Not Graded, update on Oct 17, 2020))10m2.1 code snippets for reference only10mSequence fuctions practice10mSequences and Files Programming Exercise(8 questions)10m

1 practice exercise

Data Acquisition and Presentation quiz30m

Week3

Week 3

3 hours to complete

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.

3 hours to complete

9 videos (Total 109 min), 6 readings, 1 quiz

9 videos

1 Why Are Dictionaries Needed13m2 Dictionary Use15m3 Set11m3.1 Extension: dict and set programming examples12m1 Extension Library SciPy6m2 ndarray18m3 Series7m4 DataFrame8m3.2 Extension: Common numpy applications16m

6 readings

Data Structure Selection10m3 Powerful Data Structure and Software Ecosystem slides10m3.1 Programming exercise(Not Graded)10m3.1 Classic dict programming(2 questions, update on Jan 16, 2022)10m3.2 Programming exercise for DataFrame(Not Graded)10m3.2 Modify the DataFrames10m

1 practice exercise

Powerful Data Structures and Python Extension Libraries quiz30m

Week4

Week 4

10 hours to complete

Python Data Statistics and Mining

Welcome to learn Module 04 “Python data statistics and mining”! 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 preprocess the data and 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.

10 hours to complete

13 videos (Total 240 min), 13 readings, 3 quizzes

13 videos

1 Convenient and Fast Data Acquisition18m2 Fundamentals of Python Plotting23m3 Data Clean of Data Exploration and Preprocessing20m4 Data Transformation of Data Precessing22m5 Data Reduction of Data Preproccessing18mCopy of 1 Convenient and Fast Data Acquisition18m1 Basic Data Characteristics Analysis of Data Exploration24m2 Data Statistics and Analysis Based on pandas27m3 Cluster Analysis14m4 Aplications of Python into Science and Engineering Fields7m5 Applications into Humanities and Social Sciences Fields7m4.2 Extension: An Analysis of the Differences between Males and Females on Film Ratings17m4.2 Extension: Classification of Red Wine Data Based on Random Forest Model21m

13 readings

4.1 Data retrieval and preprocessing of Python Slides10m4.1 References10m4.1.1 code snippets for reference only10m4.1.3: Analyze test results using Box-plot10mWeb API - TuShare and Data Analysis task30m4.1 Titanic Data Set Acquisition10m4.2 Data Statistics, Mining and Application Slides10m4.2 code snippets for reference only10m4.2.1 K-means algorithm an discussion on K value10m4.2.1 Extension: Scikit-learn Machine Learning Basics10m4.2.6 Project- —Linear Regression for Boston houses price prediction10m4.2.6 Extension: Introduction to WAV audio processing10m4.2.7 Learn More about NLTK10m

2 practice exercises

Data retrieval and preprocessing of Python quiz16mData Statistics, Mining and Application quiz20m

Reviews

4.1

110 reviews

5 stars
60.57%
4 stars
17.30%
3 stars
7.37%
2 stars
3.20%
1 star
11.53%

TOP REVIEWS FROM DATA PROCESSING USING PYTHON

by YQNov 26, 2017

课程深入浅出，学到了很多东西！非常值得！然后由于做了笔记，也一边操作，所以以后会经常翻过来查看笔记。

by SWMay 3, 2017

Professor talks too fast. Maybe since it is not in English, I feel like that.

by JLSep 11, 2017

It's a basic Python lesson, but providing some data analysis and GUI concepts, which needs you to explore after this class or in the future.

by JGJan 5, 2019

It's actually too simple for me, but I think it's good for the beginners.

View all reviews

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Data Processing Using Python