About this Course
최근 조회 682,269

다음 전문 분야의 1개 강좌 중 1번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 18시간 필요

권장: 7 hours/week...

영어

자막: 중국어 (번체자), 포르투갈어 (브라질), 베트남어, 한국어, 영어, 히브리어...

배울 내용

  • Check

    Describe common Python functionality and features used for data science

  • Check

    Explain distributions, sampling, and t-tests

  • Check

    Query DataFrame structures for cleaning and processing

  • Check

    Understand techniques such as lambdas and manipulating csv files

귀하가 습득할 기술

Python ProgrammingNumpyPandasData Cleansing

다음 전문 분야의 1개 강좌 중 1번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 18시간 필요

권장: 7 hours/week...

영어

자막: 중국어 (번체자), 포르투갈어 (브라질), 베트남어, 한국어, 영어, 히브리어...

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 3시간 필요

Week 1

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.

...
11 videos (Total 58 min), 4 readings, 1 quiz
11개의 동영상
Data Science7m
The Coursera Jupyter Notebook System3m
Python Functions8m
Python Types and Sequences8m
Python More on Strings3m
Python Demonstration: Reading and Writing CSV files3m
Python Dates and Times2m
Advanced Python Objects, map()5m
Advanced Python Lambda and List Comprehensions2m
Advanced Python Demonstration: The Numerical Python Library (NumPy)7m
4개의 읽기 자료
Syllabus10m
Help us learn more about you!10m
50 years of Data Science, David Donoho (optional)1시 30분
Notice for Auditing Learners: Assignment Submission10m
1개 연습문제
Week One Quiz12m
2
완료하는 데 3시간 필요

Week 2

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. The module ends with a programming assignment and a discussion question.

...
8 videos (Total 45 min), 2 quizzes
8개의 동영상
The Series Data Structure4m
Querying a Series8m
The DataFrame Data Structure7m
DataFrame Indexing and Loading5m
Querying a DataFrame5m
Indexing Dataframes5m
Missing Values4m
3
완료하는 데 3시간 필요

Week 3

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.

...
6 videos (Total 35 min), 1 quiz
6개의 동영상
Pandas Idioms6m
Group by6m
Scales7m
Pivot Tables2m
Date Functionality5m
4
완료하는 데 6시간 필요

Week 4

In this week of the course you'll be introduced to a variety of statistical techniques such a distributions, sampling and t-tests. The majority of the week will be dedicated to your course project, where you'll engage in a real-world data cleaning activity and provide evidence for (or against!) a given hypothesis. This project is suitable for a data science portfolio, and will test your knowledge of cleaning, merging, manipulating, and test for significance in data. The week ends with two discussions of science and the rise of the fourth paradigm -- data driven discovery.

...
4 videos (Total 25 min), 1 reading, 2 quizzes
4개의 동영상
Distributions4m
More Distributions8m
Hypothesis Testing in Python10m
1개의 읽기 자료
Post-course Survey10m
4.5
2614개의 리뷰Chevron Right

32%

이 강좌를 수료한 후 새로운 경력 시작하기

33%

이 강좌를 통해 확실한 경력상 이점 얻기

Introduction to Data Science in Python의 최상위 리뷰

대학: SIMar 16th 2018

overall the good introductory course of python for data science but i feel it should have covered the basics in more details .specially for the ones who do not have any prior programming background .

대학: AVJan 1st 2017

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

미시건 대학교 정보

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....

Python과 함께하는 응용 데이터 과학 전문 분야 정보

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....
Python과 함께하는 응용 데이터 과학

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

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