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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/python-data-analysis
课程评论：没有评论
课程名称：Python数据科学入门 课程概述：本课程旨在向学习者介绍Python编程环境的基础知识，包括基本的Python编程技术，如lambda表达式、读取和处理CSV文件，以及numpy库。课程还将介绍使用流行的Python数据科学库pandas进行数据处理和清洗的技术，并介绍Series和DataFrame作为数据分析的核心数据结构。学员将学习如何有效使用groupby、merge和数据透视表等函数。通过本课程的学习，学生将能够处理表格数据，包括清理、操作数据，并进行基本的推断统计分析。 该课程建议在其他应用数据科学Python课程（如应用绘图、图表和数据表示、应用机器学习、应用文本挖掘、应用社会网络分析）之前完成。 课程大纲： 1. 数据处理基础：本周将介绍数据科学的领域，复习数据科学家常用的Python功能和特性，并介绍Coursera Jupyter Notebook用于讲座。所有课程的信息，包括评分、先决条件和期望，都在课程大纲中，关于Jupyter Notebooks的更多信息可在课程资源页面找到。 2. 使用Pandas进行基本数据处理：这一周将学习Python中最重要的数据清理和处理工具包之一——pandas的基础知识。学习如何将数据读取到DataFrame结构中，如何查询这些结构，以及关于这些结构如何索引的细节。 3. 更深入的Pandas数据处理：在这一周中，您将加深对Python pandas库的理解，学习如何合并DataFrames、生成汇总表、将数据分组和操作日期。此外，还将刷新对数据量级的理解，并讨论创建分析指标时遇到的问题。本周结束时会有一个较为重要的编程作业。 4. 用杂乱数据回答问题：在这一周中，将介绍多种统计技术，如分布、抽样和t检验。本周将以两次关于科学和第四范式——数据驱动发现的讨论作为结束。
Name:Fundamentals of Data Manipulation with Python
Description:In this week you'll get an introduction to the field of data science, review common Python functionality and features which data scientists use, and be introduced to the Coursera Jupyter Notebook for the lectures. All of the course information on grading, prerequisites, and expectations are on the course syllabus, and you can find more information about the Jupyter Notebooks on our Course Resources page.
Name:Basic Data Processing with Pandas
Description:In this week of the course you'll learn the fundamentals of one of the most important toolkits Python has for data cleaning and processing -- pandas. You'll learn how to read in data into DataFrame structures, how to query these structures, and the details about such structures are indexed.
Name:More Data Processing with Pandas
Description:In this week you'll deepen your understanding of the python pandas library by learning how to merge DataFrames, generate summary tables, group data into logical pieces, and manipulate dates. We'll also refresh your understanding of scales of data, and discuss issues with creating metrics for analysis. The week ends with a more significant programming assignment.
Name:Answering Questions with Messy Data
Description:In this week of the course you'll be introduced to a variety of statistical techniques such a distributions, sampling and t-tests. The week ends with two discussions of science and the rise of the fourth paradigm -- data driven discovery.
This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.
