Nanjing University

Data Processing Using Python

Taught in English

103,396 already enrolled

Course

Gain insight into a topic and learn the fundamentals

ZHANG Li

Instructor: ZHANG Li

4.1

(345 reviews)

Beginner level
No prior experience required
29 hours to complete
3 weeks at 9 hours a week
Flexible schedule
Learn at your own pace

Details to know

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Assessments

8 quizzes

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There are 6 modules in this course

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.

What's included

1 video2 readings

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!

What's included

16 videos7 readings2 quizzes1 programming assignment1 discussion prompt

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.

What's included

10 videos5 readings1 quiz1 discussion prompt

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.

What's included

9 videos7 readings1 quiz1 discussion prompt

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.

What's included

13 videos14 readings2 quizzes1 programming assignment

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.

What's included

8 videos5 readings2 quizzes

Instructor

Instructor ratings
4.5 (62 ratings)
ZHANG Li
Nanjing University
2 Courses199,362 learners

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4.1

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VK
5

Reviewed on Jun 27, 2020

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5

Reviewed on Sep 11, 2017

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4

Reviewed on May 3, 2017

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