課程信息

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學生職業成果

33%

完成這些課程後已開始新的職業生涯

35%

通過此課程獲得實實在在的工作福利

10%

加薪或升職
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完成後獲得證書
100% 在線
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第 1 門課程(共 5 門)
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中級
完成時間大約為16 小時
英語(English)
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您將學到的內容有

  • 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

您將獲得的技能

Python ProgrammingNumpyPandasData Cleansing

學生職業成果

33%

完成這些課程後已開始新的職業生涯

35%

通過此課程獲得實實在在的工作福利

10%

加薪或升職
可分享的證書
完成後獲得證書
100% 在線
立即開始,按照自己的計劃學習。
第 1 門課程(共 5 門)
可靈活調整截止日期
根據您的日程表重置截止日期。
中級
完成時間大約為16 小時
英語(English)
字幕:中文(繁體), 巴西葡萄牙語, 越南語, 韓語, 英語(English), 希伯來語...

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密歇根大学 徽標

密歇根大学

教學大綱 - 您將從這門課程中學到什麼

內容評分Thumbs Up91%(46,859 個評分)Info
1

1

完成時間為 3 小時

Week 1

完成時間為 3 小時
11 個視頻 (總計 58 分鐘), 4 個閱讀材料, 1 個測驗
11 個視頻
Data Science7分鐘
The Coursera Jupyter Notebook System3分鐘
Python Functions8分鐘
Python Types and Sequences8分鐘
Python More on Strings3分鐘
Python Demonstration: Reading and Writing CSV files3分鐘
Python Dates and Times2分鐘
Advanced Python Objects, map()5分鐘
Advanced Python Lambda and List Comprehensions2分鐘
Advanced Python Demonstration: The Numerical Python Library (NumPy)7分鐘
4 個閱讀材料
Syllabus10分鐘
Help us learn more about you!10分鐘
50 years of Data Science, David Donoho (optional)1 小時 30 分
Notice for Auditing Learners: Assignment Submission10分鐘
1 個練習
Week One Quiz12分鐘
2

2

完成時間為 3 小時

Week 2

完成時間為 3 小時
8 個視頻 (總計 45 分鐘), 1 個閱讀材料, 2 個測驗
8 個視頻
The Series Data Structure4分鐘
Querying a Series8分鐘
The DataFrame Data Structure7分鐘
DataFrame Indexing and Loading5分鐘
Querying a DataFrame5分鐘
Indexing Dataframes5分鐘
Missing Values4分鐘
1 個閱讀材料
Common Assignment Pitfalls10分鐘
3

3

完成時間為 3 小時

Week 3

完成時間為 3 小時
6 個視頻 (總計 35 分鐘)
6 個視頻
Pandas Idioms6分鐘
Group by6分鐘
Scales7分鐘
Pivot Tables2分鐘
Date Functionality5分鐘
4

4

完成時間為 6 小時

Week 4

完成時間為 6 小時
4 個視頻 (總計 25 分鐘), 1 個閱讀材料, 2 個測驗
4 個視頻
Distributions4分鐘
More Distributions8分鐘
Hypothesis Testing in Python10分鐘
1 個閱讀材料
Post-course Survey10分鐘

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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. 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....
借助 Python 应用数据科学

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