Data
Development-Category-(10k-courses)-from-Udemy

Development-Category-(10k-courses)-from-Udemy

active ARFF CC0: Public Domain Visibility: public Uploaded 24-03-2022 by Elif Ceren Gok
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Context A compilation of all the development related courses ( 10 thousand courses) which are available on Udemy's website. Under the development category, there are courses from Web Development, Data Science, Mobile App, Programming Languages, Game Development, Databases, Software Testing, Software Engineering, Development Tools and E-commerce each having multiple courses under it's domain. All the details can be found on Udemy's website as well! Content Here, I have extracted data related to 10k courses which come under the development category on Udemy's website. The 17 columns in the dataset can be used to gain insights related to: id : The course ID of that particular course. title : Shows the unique names of the courses available under the development category on Udemy. url: Gives the URL of the course. is_paid : Returns a boolean value displaying true if the course is paid and false if otherwise. num_subscribers : Shows the number of people who have subscribed that course. avg_rating : Shows the average rating of the course. avg rating recent : Reflects the recent changes in the average rating. num_reviews : Gives us an idea related to the number of ratings that a course has received. num_ published_lectures : Shows the number of lectures the course offers. num_ published_ practice_tests : Gives an idea of the number of practice tests that a course offers. created : The time of creation of the course. published_time : Time of publishing the course. discounted_ price_amount : The discounted price which a certain course is being offered at. discounted_ price_currency : The currency corresponding to the discounted price which a certain course is being offered at. price_ detail_amount : The original price of a particular course. price_ detail_currency : The currency corresponding to the price detail amount for a course.

17 features

idnumeric9932 unique values
0 missing
titlestring9891 unique values
0 missing
urlstring9932 unique values
0 missing
is_paidnominal1 unique values
0 missing
num_subscribersnumeric5625 unique values
0 missing
avg_ratingnumeric2092 unique values
0 missing
avg_rating_recentnumeric9640 unique values
0 missing
ratingnumeric9640 unique values
0 missing
num_reviewsnumeric1560 unique values
0 missing
num_published_lecturesnumeric414 unique values
0 missing
num_published_practice_testsnumeric7 unique values
0 missing
createdstring9930 unique values
0 missing
published_timestring9931 unique values
0 missing
discount_price__amountnumeric39 unique values
168 missing
discount_price__currencystring1 unique values
168 missing
price_detail__amountnumeric34 unique values
0 missing
price_detail__currencystring1 unique values
0 missing

19 properties

9932
Number of instances (rows) of the dataset.
17
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
336
Number of missing values in the dataset.
168
Number of instances with at least one value missing.
10
Number of numeric attributes.
1
Number of nominal attributes.
5.88
Percentage of binary attributes.
1.69
Percentage of instances having missing values.
0.2
Percentage of missing values.
Average class difference between consecutive instances.
58.82
Percentage of numeric attributes.
0
Number of attributes divided by the number of instances.
5.88
Percentage of nominal attributes.
Percentage of instances belonging to the most frequent class.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
1
Number of binary attributes.

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