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.
提供:
このコースについて
学習内容
Understand techniques such as lambdas and manipulating csv files
Describe common Python functionality and features used for data science
Query DataFrame structures for cleaning and processing
Explain distributions, sampling, and t-tests
習得するスキル
- Python Programming
- Numpy
- Pandas
- Data Cleansing
提供:

ミシガン大学(University of Michigan)
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.
シラバス - 本コースの学習内容
Fundamentals of Data Manipulation with Python
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.
Basic Data Processing with Pandas
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.
More Data Processing with Pandas
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.
Answering Questions with Messy Data
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.
レビュー
- 5 stars66.25%
- 4 stars24.51%
- 3 stars5.36%
- 2 stars1.88%
- 1 star1.98%
INTRODUCTION TO DATA SCIENCE IN PYTHON からの人気レビュー
Great course! I liked how the focus was mainly on the practical aspects of data science. No 'dry' course material. I know much more about the practical side of data analysis than before! Thank you!
Great course, make sure you are comfortable in Python before diving in. Covers basic DataFrame work including cleaning, generating new data from existing data, and how to execute merge/join/update.
Um curso intenso e bastante prazeroso. Gostei de todas as etapas, os videos funcionam bem e estão construidos numa base introdutória, mas o desafio é pesquisar e pesquisar. Muito interessante mesmo!
A very nice introduction to libraries/skills used by data scientists. The auto-grader was extremely annoying though. Also, I felt that some of the questions on the assignments were a bit ambiguous.
Python 応用データサイエンス専門講座について
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.

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