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.
Data Processing Using Python
oferecido por
Universidade de Nanquim
Informações sobre o curso
4.4
108 classificações
Habilidades que você terá
Python ProgrammingNumpyPandasWxpython
Programa - O que você aprenderá com este curso
Semana
124 minutos para concluir
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.
1 vídeo (total de (Total 4 mín.) min), 2 leituras
1 vídeos
Promotion Video3min
2 leituras
Teaching Methods10min
FAQ10min
7 horas para concluir
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 vídeos (total de (Total 145 mín.) min), 3 leituras, 3 testes
15 videos
2 The First Python Program15min
3 Basics of Python Syntax11min
4 Data Types of Python9min
5 Basic Operations of Python10min
6 Functions, Modules and Packages of Python7min
1 Conditions10min
2 range5min
3 Loops6min
4 break, continue and else in Loops11min
5 Self-defined Functions14min
6 Recursion7min
7 Scope of Variable4min
A1: Standard Library Functions14min
A2: Exceptions10min
3 leituras
1.1 References10min
1.1 Programming exercises(Not Graded)10min
1.2 Control structure & function exercises(9 questions)10min
2 exercícios práticos
Walk into Python quiz20min
More About Python quiz24min
Semana
23 horas para concluir
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 vídeos (total de (Total 79 mín.) min), 5 leituras, 1 teste
6 videos
2 Network Data Retrieval21min
1 Sequence8min
2 String17min
3 List9min
4 Tuple7min
5 leituras
2.1 References(re)10min
2.1 Internet Data Retrival Programming exercise(Not Graded)10min
2.1 code snippets for reference only10min
Sequence fuctions practice10min
Sequences and Files Programming Exercise(8 questions)10min
1 exercício prático
Data Acquisition and Presentation quiz30min
Semana
33 horas para concluir
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 vídeos (total de (Total 70 mín.) min), 5 leituras, 1 teste
7 videos
2 Dictionary Use12min
3 Set11min
1 Extension Library SciPy6min
2 ndarray12min
3 Series7min
4 DataFrame7min
5 leituras
3.1 Programming exercise(Not Graded)10min
3.1 Classic dict programming(2 questions)10min
3.2 References10min
3.2 Programming exercise for DataFrame(Not Graded)10min
3.2 Modify the DataFrames10min
1 exercício prático
Powerful Data Structures and Python Extension Libraries quiz28min
Semana
48 horas para concluir
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 vídeos (total de (Total 110 mín.) min), 12 leituras, 3 testes
14 videos
2 Data Preparations6min
3 Data Display4min
4 Data Selection8min
5 Simple Statistics and Processing8min
6 Grouping4min
7 Merge8min
1 Cluster12min
2 Basics of Matplotlib Plotting7min
3 Control of Matplotlib Image Attributes9min
4 Plotting with pandas6min
5 Data Access4min
6 Applications of Python into Science and Engineering Fields7min
7 Applications into Humanities and Social Sciences Fields7min
12 leituras
4.1 References10min
4.1.1 code snippets for reference only10min
4.1.2 code snippets for reference only10min
Web API - TuShare and Data Analysis ta30min
4.2 code snippets for reference only10min
4.2 Programming exercise for comparing the stock data(No Graded)10min
4.2.1K-means algorithm an discussion on K value10min
4.2.1 Extension: Scikit-learn Machine Learning Basics10min
4.2.4&4.2.5: Analyze test results using Box-plot10min
4.2.6 Extension: Introduction to WAV audio processing10min
4.2.6 Project- —Linear Regression for Boston houses price prediction10min
4.2.7 Learn More about NLTK10min
2 exercícios práticos
Basic Data Statistics of Python quiz16min
Advanced Data Processing and Visualization of Python quiz20min
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Melhores avaliações
The course provides an insight into the basic structure of Python. It will help you in navigating the areas where Python is robust and effective.
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.
Instrutores
ZHANG Li
associate professorDepartment of Computer Science
Sobre Universidade de Nanquim
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.
Perguntas Frequentes – FAQ
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