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Top Programming Books Dataset
Retail & Consumer Behavior
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This dataset features a curated collection of 270 highly-rated books within the fields of computer science and programming. It provides valuable information such as book descriptions, page counts, types, and pricing details. The compilation was meticulously assembled by selecting the most popular titles from numerous online book rating platforms, making it an excellent resource for insights into popular and well-regarded technical literature.
Columns
- Rating: Represents the user rating for each book, on a scale from 0 to 5. The average rating observed is approximately 4.07, with a standard deviation of 0.29, indicating a generally high rating for the included books.
- Reviews: Indicates the total number of reviews a book has received. The average number of reviews stands at around 186, with a significant standard deviation of 551, suggesting a wide range in review counts across the collection.
- Book_title: The name of the book. Each of the 271 entries in this column is unique, ensuring no duplicate titles within the dataset.
- Description: A short synopsis or explanatory text about the book's content. There are 270 distinct descriptions provided for the books.
- Number_Of_Pages: The total number of pages in the book. The average page count is approximately 475, with a standard deviation of 306, highlighting variation in book length.
- Type: Specifies the format of the book. Common types include Paperback, making up 58% of the collection, and Hardcover, accounting for 35%. Other less common formats are also present.
- Price: The average price of the book in US Dollars. This average is calculated from five different web sources, with an overall mean price of about $54.50 and a standard deviation of $35.60.
Distribution
The dataset is presented as a CSV file, named
prog_book.csv, and has a file size of 124.4 kB. It is structured with 7 distinct columns and contains 271 individual records, with no missing values identified across any of the fields.Usage
This dataset is ideal for various applications, including:
- Analysing current trends and popular topics within computer science and programming literature.
- Developing and testing book recommendation algorithms based on user ratings and review volume.
- Conducting market research on the pricing and preferred formats of technical books.
- Investigating correlations between book attributes such as page count, ratings, and number of reviews.
- Assisting in the selection of educational resources for academic curricula or personal learning paths.
Coverage
The dataset's scope is strictly confined to books focused on computer science and programming topics. There is no specific geographic restriction mentioned, suggesting the data reflects a global selection of titles. The sources do not specify a particular time range for data collection or any demographic notes regarding the audience for whom the data was gathered.
License
CC0: Public Domain
Who Can Use It
This dataset is well-suited for:
- Data Scientists and Analysts: For performing exploratory data analysis, building predictive models, and extracting statistical insights related to book characteristics.
- Researchers and Academics: To study educational literature, identify influential texts, and analyse publishing trends in technical fields.
- Developers and Programmers: To discover highly-rated and relevant resources for learning new technologies or enhancing existing skills.
- Students: To identify popular and well-regarded textbooks, reference materials, and supplementary reading for their studies.
- Online Retailers and Publishers: For market intelligence, understanding customer preferences, and informing inventory or publishing strategies in the technical book sector.
Dataset Name Suggestions
- Top Programming Books Dataset
- Highly-Rated Computer Science Books
- CS & Programming Book Collection
- Popular Tech Books Data
- Global Computer Science Books
Attributes
Original Data Source: Top Programming Books Dataset
QUALITY