Applied Data Science with Python Specialization

开始时间: 06/21/2022 持续时间: Approximately 5 months to complete Suggested pace of 7 hours/week

所在平台: Coursera专项课程

课程类别: 计算机科学

大学或机构: CourseraNew



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The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.

具有Python专长的应用数据科学:密歇根大学的这5门专业课程通过python编程语言向学习者介绍数据科学。这种基于技能的专业化课程面向有基本python或编程背景,并希望通过流行的python工具包(例如pandas,matplotlib,scikit-s)应用统计,机器学习,信息可视化,文本分析和社交网络分析技术的学习者。学习,使用nltk和networkx来深入了解其数据。 Python数据科学概论(课程1),应用绘图,制图和绘图;应该按顺序进行该专业中的其他课程,然后优先采用Python中的数据表示(课程2)和Python中的应用机器学习(课程3)。完成这些课程后,可以按照任意顺序学习课程4和5。所有5个人都必须获得证书。


Course: 1

Course Link:

Title:Introduction to Data Science in Python

Description: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.

Course: 2

Course Link:

Title:Applied Plotting, Charting & Data Representation in Python

Description:This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will be a tutorial of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data.

Course: 3

Course Link:

Title:Applied Machine Learning in Python

Description:This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis.

Course: 4

Course Link:

Title:Applied Text Mining in Python

Description:This course will introduce the learner to text mining and text manipulation basics. The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text. The second week focuses on common manipulation needs, including regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processes. The third week will apply basic natural language processing methods to text, and demonstrate how text classification is accomplished. The final week will explore more advanced methods for detecting the topics in documents and grouping them by similarity (topic modelling).



密歇根大学的Python数据科学应用专项课程系列(Applied Data Science with Python),这个系列的目标主要是通过Python编程语言介绍数据科学的相关领域,包括应用统计学,机器学习,信息可视化,文本分析和社交网络分析等知识,并结合一些流行的Python工具包进行讲授,例如pandas, matplotlib, scikit-learn, nltk以及networkx等Python工具。感兴趣的同学可以关注:Gain new insights into your data-Learn to apply data science methods and techniques, and acquire analysis skills.


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