Learn how to clean data using pandas.
Learn how to do basic data preprocessing.
Learn how to handle quantitative data (numeric data) and qualitative data (text data) with pandas.
Note 1: If you are familiar with pandas or you want to work with real world data , check out the intermediate course here: https://www.coursera.org/projects/intermediate-pandas-python-library-data-science Note 2: Pandas is not used for development. It was designed purely for data manipulation. So you will not build anything during the course of this project. Note 3: The video content is meant to be within an hour as per Coursera's guidlines. It is meant to demonstrate coding. The theory is covered in detail in the reading module titled "Additional features and Summary"provided after the video content. Make sure you read it before attempting the final quiz. This guided project is for college students or those who have not heard of pandas before and want to learn about the syntax in pandas, one of the most important python libraries for data analysis. By the end of this project, you will master the basics of pandas. You will be able to gain insight into the data, clean it, and do basic preprocessing to get the most value out of your data. Special Features: 1) This project provides plenty of challenges with solutions to encourage you to practice using pandas. 2) Libraries are automatically imported each time you begin a new session. Just open the project and start learning! 3) The real world applications of each function is explained. 4) After you complete this project, you get a jupyter notebook of all the work you covered (including gifs). It acts as a useful learning tool that you can refer to at any time in the future. 5) Best practices and tips are provided to ensure that you learn how to use pandas efficiently. 6) Animated gifs are used to aid in the learning process. 7) Important terminology and definitions are explained. 8) Simple language is used throughout the project, so that you can focus on coding. (Eg: Quantitative data is referred to simply as numeric data.) Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Three methods of creating a series.
Two methods of creating data frames.
Importing/exporting different types of data files and viewing rows.
Get a summary of the data & view column names and data types.
Calculate mean & cumulative sum. Determine minimum & maximum values.
String operations such as converting to uppercase letters , lowercase letters, swap case, finding the length of a string, splitting strings and detecting unique values.
For an hour of python coding, I think this is perfect, but it could have been a bit longer and introduced to some complex topics.
the moment when the instructor mentioned "challenge time"...will be signing up for the intermediate project
Course for beginners. Just enough introduction of the topic. Prerequisites: Python, Basic Statistics.
The course could have been more detailed about the methods and functions we use.
购买指导项目后，您将获得完成指导项目所需的一切，包括通过 Web 浏览器访问云桌面工作空间，工作空间中包含您需要了解的文件和软件，以及特定领域的专家提供的分步视频说明。
我能直接通过 Web 浏览器来完成此指导项目，而不必安装特殊软件吗？