This dataset contains detailed information on all available Udemy courses on Oct 10, 2022. This data was provided in the "Course_info.csv" file. Also, over 9 million comments were collected and provided in the "Comments.csv" file. The information of over 209k courses was collected by web scraping the Udemy website. Udemy holds 209,734 courses and 73,514 instructors teaching courses in 79 languages in 13 different categories.

The related notebook was uploaded here. If you are interested in analytical data about online learning platforms, I recommend reading the below article to find attractive insight. https://lnkd.in/gjCBhP_P

Exercise: Machine Learning Competitions

When you click on Run / All, the notebook will give you an error: "Files doesn't exist" With this DataSet you fix that. It's the same from DanB. Please UPVOTE!

Enjoy!

The dataset contains basic information about Coursera courses, including details about the subject, title, institution, skills acquired, rating, reviews, level, learning product, and duration. This data was scrapped directly from the Coursera website, providing insights about courses such as subject, title, institution, skills acquired, rating, reviews, level, learning product, and duration.

Content

Here is a description for each column:

Subject: This column represents the academic or professional category or field of study of the course, such as Business, Data Science, Information Technology, or Computer Science.

Title: The specific name or title of the course or program, which gives an idea of what the course covers (e.g., "Business Analysis & Process Management," "Financial Markets").

Institution: The organization or platform offering the course, such as "IBM," "Yale University," or "Università Bocconi."

Gained Skills: The skills and knowledge learners are expected to gain upon completing the course, such as "Data Analysis," "Machine learning," or "Artificial intelligence."

Rate: The rate rating or score given by participants based on their experiences in the course. The scale ranges from 1 to 5 stars.

Reviews: The number of user reviews or ratings provided for the course.

Level: This column categorizes the difficulty level of the course, such as "Beginner," "Intermediate," or "Mixed"

Learning Product: The type of course or learning experience, such as "Guided Project" or "Course".

Duration: The length of time required to complete the course, which could be listed as "Less Than 2 Hours," "1 - 3 Months," etc.

Context

2021 has seen a boom in the MOOCs due to the Covid-19 Pandemic. With the availability of numerous paid and free resources on the internet, it becomes overwhelming for students to learn new skills. Therefore, this dataset can be used to create Recommender Systems and recommend courses to students based on the Skills and Difficulty Level entered by the student. The Course Link is also provided, which can be offered by the Recommender System for easy access.

Content

This dataset was scraped off the publicly available information on the EdX website in September 2021 and manually entered in the case where the data was improperly scraped. It can be used in Recommender Systems to promote EdX courses based on the Difficulty Level and the Skills needed.

Acknowledgements

The dataset was obtained from the publicly available information on the EdX website. I do not own any information.

Dataset

This dataset was created by Saloni Singhal

Contents

Dataset

This dataset was created by Asma Abeyat

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Dataset

This dataset was created by Sauravchauhan_FE_ENTCA

Released under MIT

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Dataset

This dataset was created by sandeepantoken

Released under CC BY-NC-SA 4.0

Contents

Student Engagement

Predicting Engagement and Exam Performance

By [source]

About this dataset

This dataset contains information on student engagement with Tableau, including quizzes, exams, and lessons. The data includes the course title, the rating of the course, the date the course was rated, the exam category, the exam duration, whether the answer was correct or not, the number of quizzes completed, the number of exams completed, the number of lessons completed, the date engaged, the exam result, and more

How to use the dataset

The 'Student Engagement with Tableau' dataset offers insights into student engagement with the Tableau software. The data includes information on courses, exams, quizzes, and student learning.

This dataset can be used to examine how students use Tableau, what kind of engagement leads to better learning outcomes, and whether certain course or exam characteristics are associated with student engagement

Research Ideas

• Creating a heat map of student engagement by course and location
• Determining which courses are most popular among students from different countries
• Identifying patterns in students' exam results

Acknowledgements

Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: 365_course_info.csv | Column name | Description | |:-----------------|:----------------------------------| | course_title | The title of the course. (String) |

File: 365_course_ratings.csv | Column name | Description | |:------------------|:---------------------------------------------------------| | course_rating | The rating given to the course by the student. (Numeric) | | date_rated | The date on which the course was rated. (Date) |

File: 365_exam_info.csv | Column name | Description | |:------------------|:-------------------------------------------------| | exam_category | The category of the exam. (Categorical) | | exam_duration | The duration of the exam in minutes. (Numerical) |

File: 365_quiz_info.csv | Column name | Description | |:-------------------|:----------------------------------------------------------------------| | answer_correct | Whether or not the student answered the question correctly. (Boolean) |

File: 365_student_engagement.csv | Column name | Description | |:-----------------------|:------------------------------------------------------------------| | engagement_quizzes | The number of times a student has engaged with quizzes. (Numeric) | | engagement_exams | The number of times a student has engaged with exams. (Numeric) | | engagement_lessons | The number of times a student has engaged with lessons. (Numeric) | | date_engaged | The date of the student's engagement. (Date) |

File: 365_student_exams.csv | Column name | Description | |:-------------------------|:---------------------------------------------------| | exam_result | The result of the exam. (Categorical) | | exam_completion_time | The time it took to complete the exam. (Numerical) | | date_exam_completed | The date the exam was completed. (Date) |

File: 365_student_hub_questions.csv | Column name | Description | |:------------------------|:----------------------------------------| | date_question_asked | The date the question was asked. (Date) |

File: 365_student_info.csv | Column name | Description | |:--------------------|:-------------------------------------------------------| | student_country | The country of the student. (Categorical) | | date_registered | The date the student registered for the course. (Date) |

File: 365_student_learning.csv | Column name | Description | |:--------------------|:------------------------------...

Dataset

This dataset was created by Katja Rodionova

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Dataset

This dataset was created by DanB

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Dataset

This dataset was created by Alexis Cook

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Dataset

This dataset was created by DanB

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by Sean Kim

Contents

Dataset contains details of DataCamp courses and individual chapters within each course.

Web scraping performed in Python using scrapy web-crawling framework.

Potential uses of dataset: -Sieve out desired courses through a trove of courses(my original intent) -Perform analysis on dataset (e.g. find out the most popular programming language) -View a comprehensive summary of a course based on individual chapters

DataCamp is an online learning platform for users to learn about data science through videos, exercises, and quizzes. Topics covered include R programming, Numpy, Pandas, SQL, and data science knowledge in general.

Source Code: https://github.com/shijietopkek/Webscraping/tree/master/DataCamp

this dataset contains 20 files in pdf format. each file consists to text transcripts for each lecture. this data can be used for creating question answering application using LLM.

Dataset

This dataset was created by Pallavi2808

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Dataset

This dataset was created by huuphat

Contents

You should not take this dataset seriously, as it is a synthetic representation based on true trends in education and career outcomes.

About the Dataset

This dataset provides insights into how different study habits, learning styles, and external factors influence student performance. It includes 10,000 records, covering details about students' study hours, online learning participation, exam scores, and other factors impacting academic success.

Dataset Features

• Student_ID – Unique identifier for each student
• Age – Student's age (18-30 years)
• Gender – Male, Female, or Other
• Study_Hours_per_Week – Hours spent studying per week (5-50 hours)
• Preferred_Learning_Style – Visual, Auditory, Reading/Writing, Kinesthetic
• Online_Courses_Completed – Number of online courses completed (0-20)
• Participation_in_Discussions – Whether the student actively participates in discussions (Yes/No)
• Assignment_Completion_Rate (%) – Percentage of assignments completed (50%-100%)
• Exam_Score (%) – Student’s final exam score (40%-100%)
• Attendance_Rate (%) – Percentage of classes attended (50%-100%)
• Use_of_Educational_Tech – Whether the student uses educational technology (Yes/No)
• Self_Reported_Stress_Level – Student’s stress level (Low, Medium, High)
• Time_Spent_on_Social_Media (hours/week) – Weekly hours spent on social media (0-30 hours)
• Sleep_Hours_per_Night – Average sleep duration (4-10 hours)
• Final_Grade – Assigned grade based on exam score (A, B, C, D, F)

Use Cases

• Predicting Student Performance – Analyze how different factors influence exam scores.
• Educational Insights – Understand the impact of study habits, learning styles, and external activities.
• Machine Learning Applications – Train predictive models for student success.

Analysis of ‘Coursera Course Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/siddharthm1698/coursera-course-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

This is a dataset i generated during a hackathon for project purpose. Here i have scrapped data from Coursera official web site. Our project aims to help any new learner get the right course to learn by just answering a few questions. It is an intelligent course recommendation system. Hence we had to scrap data from few educational websites. This is data scrapped from Coursera website. For the project visit: https://github.com/Siddharth1698/Coursu . Please do show your support by following us. I have just started to learn on data science and hope this dataset will be helpful to someone for his/her personal purposes. The scrapping code is here : https://github.com/Siddharth1698/Coursera-Course-Dataset Article about the dataset generation : https://medium.com/analytics-vidhya/web-scraping-and-coursera-8db6af45d83f

Content

This dataset contains mainly 6 columns and 890 course data. The detailed description: 1. course_title : Contains the course title. 2. course_organization : It tells which organization is conducting the courses. 3. course_Certificate_type : It has details about what are the different certifications available in courses. 4. course_rating : It has the ratings associated with each course. 5. course_difficulty : It tells about how difficult or what is the level of the course. 6. course_students_enrolled : It has the number of students that are enrolled in the course.

Inspiration

This is just one of my first scrapped dataset. Follow my GitHub for more: https://github.com/Siddharth1698

--- Original source retains full ownership of the source dataset ---