데이터 분석 시작 -https://cn.udacity.com/
CSV
enrollments.csv:
Data about a random subset of Data Analyst Nanodegree students who complete
their first project and a random subset of students who do not.
Columns:
- account_key: A unique identifier for the account of the student who
enrolled.
- status: The enrollment status of the student at the time the data
was collected. Possible values are 'canceled' and
'current'.
- join_date: The date the student enrolled.
- cancel_date: The date the student canceled, or blank if the student has
not yet canceled.
- days_to_cancel: The number of days between join_date and cancel_date, or
blank if the student has not yet canceled.
- is_udacity: True if the account is a Udacity test account, False
otherwise.
- is_canceled: True if the student had canceled this enrollment at the time the data was collected, False otherwise.
-------------------------------------------------------------------------------
daily_engagement.csv:
Data about engagement within Data Analyst Nanodegree courses for each student in
the enrollment table on each day they were enrolled. Includes a record even if
there was no engagement
that day. Includes engagement data from both the
supporting courses for the Nanodegree program, and the corresponding freely
available courses with the same content.
Columns:
- acct: A unique identifier for the account of the student
whose engagement data this is.
- utc_date: The date for which the data was collected.
- num_courses_visited: The total number of Data Analyst Nanodegree courses
the student visited for at 2 minutes on this day.Nanodegree courses and freely available courses
with the same
content are counted separately.
- total_minutes_visited: The total number of minutes the student spent
taking Data Analyst Nanodegree courses on this day.
- lessons_completed: The total number of lessons within Data Analyst
Nanodegree courses on this day.
- projects_completed: The total number of Data Analyst Nanodegree
projects the student completed on this day.
-------------------------------------------------------------------------------
project_submissions.csv:
Data about submissions for Data Analyst Nanodegree projects for each student in
the enrollment table.
Columns:
- creation_date: The date the project was submitted.
- completion_date: The date the project was evaluated.
- assigned_rating: This column has 4 possible values:
blank - Project has not yet been evaluated.
INCOMPLETE - Project did not meet specifications.
PASSED - Project met specifications.
DISTINCTION - Project exceeded specifications.
UNGRADED - The submission could not be evaluated
(e.g. contained a corrupted file)
- account_key: A unique identifier for the account of the student who submitted the project.
- lesson_key: A unique identifier for the project that was submitted.
- processing_state: This column has 2 possible values:
CREATED - Project has been submitted but not evaluated.
EVALUATED - Project has been evaluated.
-------------------------------------------------------------------------------
daily_engagement_full.csv:
Similar to daily_engagement.csv, but with engagement further broken down by
course and with more columns available. This file is about 500 megabytes, which
is why the smaller
daily_engagement.csv file was created. This dataset is
optional; it is not needed to complete the course.
In addition to the following columns, this table also contains all the same
columns as
daily_engagement.csv, except with has_visited instead of
num_courses_visited.
Columns:
- registration_date: Date the account was registered.
- subscription_start: Date paid subscription for the account started.
- course_key: Course in which activity is recorded.
- sibling_key: Free course with the same free content as course_key.If course_key is a free course, course_key and
sibling_key are the same.
- course_title: Title of the course.
- has_visited: 1 if the student visited this course for at least 2
minutes on this day.
이 내용에 흥미가 있습니까?
현재 기사가 여러분의 문제를 해결하지 못하는 경우 AI 엔진은 머신러닝 분석(스마트 모델이 방금 만들어져 부정확한 경우가 있을 수 있음)을 통해 가장 유사한 기사를 추천합니다:
[Core Javascript] JS 분석 from scratch: 데이터, 변수, 메모리 관련 기본지식이런 언어의 기반이 되는 지식을 알아야 나중에 더 능숙하게 다룰 수 있겠다 싶었습니다. 그렇다면 기본형과 참조형 데이터를 구분하는 기준은 무엇일까요? 이것을 이해하기 위해 알아야할 배경지식들이 있습니다. 변수와 식별...
텍스트를 자유롭게 공유하거나 복사할 수 있습니다.하지만 이 문서의 URL은 참조 URL로 남겨 두십시오.
CC BY-SA 2.5, CC BY-SA 3.0 및 CC BY-SA 4.0에 따라 라이센스가 부여됩니다.