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

Context

This is a case study from Coursera: Google Data Analytics Professional Certificate. Cyclistic is a fictional bike-share company and the data is obtained from
Divvy.

Content

The data used for this case study is the trip data of Divvy from June 2020 to May 2021 License. The original data can be access here.

Acknowledgements

Thanks to Gaurav Dutta's note 'How to integrate Tableau with Kaggle Notebook', helped me with embeding Tableau visualizations.

If you see any mistake or anything to improve, feel free to point that out. Excited to learn more about Data Analysis!

Introducing the largest and most comprehensive collection of Mongo DB Dataset! This meticulously curated dataset brings together a wealth of information from various domains, including ecommerce, aviation, biology, zoology, literature, history, and more. Meticulously gathered from numerous reliable sources, this dataset has been expertly transformed into a unified format, making it an invaluable resource for researchers, data scientists, and enthusiasts alike. Each domain contributes its unique insights and knowledge, providing a diverse range of information for exploration and analysis. With its enriched content and extensive coverage, this Mongo DB Dataset opens up endless possibilities for uncovering hidden patterns, conducting groundbreaking research, and gaining profound insights across multiple disciplines.

This dataset captures the annual trends of the Secondary School Certificate (SSC) examination results in Bangladesh, covering a 25-year period from 2001 to 2025.

Columns: - Year: Year of examination (2001–2025) - Total_Examinees: Number of registered students who took the SSC exam - Pass_Rate: Percentage of students who passed the exam - GPA_5_Count: Number of students who achieved the highest possible GPA (GPA 5)

Why this dataset? - Provides a long-term view of SSC results trends in Bangladesh - Useful for educators, policymakers, data scientists, and students - Enables analysis of how educational performance has changed over time

Possible questions to explore: - How has the overall pass rate changed over the last two decades? - Is there a correlation between the number of examinees and pass rate? - How has the number of GPA 5 achievers evolved over time?

Source: Compiled from official education board reports, government publications, and publicly available data.

Feel free to use this dataset for your analysis, visualization, or educational projects - and please share your findings!

"IMDb Top 250 Movies Dataset: Ultimate Movie Ranking Collection"

Description: The "IMDb Top 250 Movies Dataset: Ultimate Movie Ranking Collection" is an extensive and meticulously curated dataset, available on Kaggle, that offers a comprehensive compilation of the top 250 movies as ranked by IMDb. This dataset serves as an invaluable resource for movie enthusiasts, researchers, and data scientists seeking to explore and analyze the world of cinema.

With a wide array of attributes, the dataset provides detailed information for each movie entry. The columns include:

1. Sl_No: A unique serial number assigned to each movie in the dataset.
2. Name: The title of the movie.
3. Release_Year: The year when the movie was released.
4. Duration: The duration of the movie in minutes.
5. Certificate: The certification or rating assigned to the movie.
6. Rating: The IMDb rating of the movie, reflecting its overall user-generated score.
7. Votes: The number of votes received by the movie on IMDb.
8. Director: The name of the director(s) of the movie.
9. Stars: The main actors or actresses who starred in the movie.
10. Description: A brief summary or description of the movie.

This dataset empowers users to conduct in-depth analyses, extract meaningful insights, and unravel patterns within the IMDb Top 250 movies. It enables researchers to explore correlations between attributes such as ratings, votes, and duration. Furthermore, it facilitates the examination of the impact of directors, the popularity of stars, and the influence of certification on movie rankings.

Whether you're interested in studying the evolution of film ratings over time, identifying common themes or genres among top-rated movies, or analyzing the characteristics of acclaimed directors and actors, the "IMDb Top 250 Movies Dataset: Ultimate Movie Ranking Collection" provides a robust foundation for comprehensive research, predictive modeling, and captivating visualizations.

Coffee Quality Institute The Coffee Quality Institute (CQI) is a non-profit organization that works to improve the quality and value of coffee worldwide. It was founded in 1996 and has its headquarters in California, USA.

CQI's mission is to promote coffee quality through a range of activities that include research, training, and certification programs. The organization works with coffee growers, processors, roasters, and other stakeholders to improve coffee quality standards, promote sustainability, and support the development of the specialty coffee industry.

Data CQI maintains a web database that serves as a resource for coffee professionals and enthusiasts who are interested in learning about coffee quality and sustainability. The database includes a range of information on coffee production, processing, and sensory evaluation. It also contains data on coffee genetics, soil types, and other factors that can affect coffee quality.

Sensory evaluations (coffee quality scores) Aroma: Refers to the scent or fragrance of the coffee. Flavor: The flavor of coffee is evaluated based on the taste, including any sweetness, bitterness, acidity, and other flavor notes. Aftertaste: Refers to the lingering taste that remains in the mouth after swallowing the coffee. Acidity: Acidity in coffee refers to the brightness or liveliness of the taste. Body: The body of coffee refers to the thickness or viscosity of the coffee in the mouth. Balance: Balance refers to how well the different flavor components of the coffee work together. Uniformity: Uniformity refers to the consistency of the coffee from cup to cup. Clean Cup: A clean cup refers to a coffee that is free of any off-flavors or defects, such as sourness, mustiness, or staleness. Sweetness: It can be described as caramel-like, fruity, or floral, and is a desirable quality in coffee. PLEASE NOTE: 'Total Cup Points' is literally the total of 10 features given above. There were some notebooks trying to predict the total cup points given these features. We know the exact function underlying the total cup points.

Defects Defects are undesirable qualities that can occur in coffee beans during processing or storage. Defects can be categorized into two categories: Category One and Category Two defects.

Category One defects are primary defects that can be perceived through visual inspection of the coffee beans. These defects include Black beans, sour beans, insect-damaged beans, fungus-damaged beans, etc.

Category Two defects are secondary defects that are more subtle and can only be detected through tasting. These defects include Over-fermentation, staleness, rancidness, chemical taste, etc.

Data Scraping On this part, great thanks to James LeDoux. His repo coffee-quality-database from 2018 is efficiently written and well documented. To scrape the data, Fatih B. used most of his code, but due to some changes on the website, Fatih B. modified some of the lines. Also, some practices on modules were deprecated and deleted, updated those codes also. Therefore, in May-2023 we can use this updated Python program to scrape data from this database.

Only data was collected for the arabica type.