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.
The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
- 5 stars66.26%
- 4 stars24.48%
- 3 stars5.37%
- 2 stars1.89%
- 1 star1.98%
來自INTRODUCTION TO DATA SCIENCE IN PYTHON的熱門評論
To be an introductory course I struggled a lot, is a very practical course, and the assignements encourage you to learn more. This is the best technical course I have taken. Lo recomiendo ampliamente
This is the practical course.There is some concepts and assignments like: pandas, data-frame, merge and time. The asg 3 and asg4 are difficult but I think that it's very useful and improve my ability.
Enjoyed every bit of it. The timings for the assignment could be a little bit more though. Also the expected output could be provided for validation, rather than the grader printing cryptic messages.
It is a great course to get started in the field of data science. It just require basic knowledge of python. This course teaches you basics of numpy and pandas and how to apply them in data science
關於 借助 Python 应用数据科学 專項課程
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.