Learning Data Visualization Tools Market Outlook

According to our latest research, the global Learning Data Visualization Tools Market size reached USD 2.8 billion in 2024, demonstrating robust growth driven by the increasing demand for data literacy and analytics skills across various sectors. The market is expected to grow at a CAGR of 13.7% from 2025 to 2033, projecting a value of USD 8.8 billion by 2033. This surge is primarily attributed to the rapid digitization of education and corporate learning environments, the proliferation of big data, and the critical need for interactive, accessible analytical tools to foster effective data comprehension and decision-making.

One of the most significant growth factors for the Learning Data Visualization Tools Market is the widespread integration of data-driven decision-making processes within organizations and educational institutions. As businesses and academic settings increasingly rely on data to guide strategies, there is a parallel surge in the demand for professionals who possess strong data visualization skills. This has led to a marked increase in the adoption of user-friendly data visualization tools such as Tableau, Power BI, and Google Data Studio in both formal education and corporate training programs. The ability of these tools to simplify complex datasets into intuitive visual representations is a key driver, enabling learners to grasp intricate concepts more efficiently and apply them in real-world scenarios.

Technological advancements and the evolution of cloud-based learning platforms have further propelled the market. The shift toward digital and remote learning, especially post-pandemic, has accelerated the adoption of cloud-based data visualization tools, which offer scalability, accessibility, and seamless integration with other e-learning resources. Cloud deployment eliminates geographical barriers, allowing learners and organizations from diverse regions to access advanced visualization tools and resources at any time. Additionally, the increasing availability of free and open-source visualization libraries such as D3.js has democratized access to these technologies, further expanding the market’s reach across different socioeconomic segments.

Another crucial growth driver is the rising emphasis on upskilling and reskilling initiatives across industries. As automation and artificial intelligence reshape job requirements, data literacy has become a fundamental skill for both students and working professionals. Enterprises are investing heavily in learning platforms that incorporate data visualization tools to train their workforce, ensuring they remain competitive in the digital economy. The trend is mirrored in higher education, where curricula are being revamped to include data visualization modules, reflecting the growing recognition of its importance in fostering analytical and critical thinking skills among learners.

From a regional perspective, North America dominates the Learning Data Visualization Tools Market, accounting for the largest revenue share in 2024. This can be attributed to the presence of leading technology providers, a mature e-learning ecosystem, and high levels of digital adoption in both educational and corporate sectors. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid digital transformation, government initiatives to enhance digital literacy, and the increasing penetration of internet and mobile devices. Europe also contributes significantly, with a strong focus on educational innovation and enterprise training. These regional dynamics are shaping the competitive landscape and driving the global expansion of learning data visualization tools.

Tool Type Analysis

The Tool Type segment of the Learning Data Visualization Tools Market is highly diverse, encompassing established platforms like Tableau, Power BI, and Qlik, as well as newer entrants such as Google Data Studio and open-source solutions like D3.js. Tableau remains a market leader due to its intuitive drag-and-drop interface, robust analytics capabilities, and widespread adoption in both academic and corporate settings. Its ability to handle large datasets and integrate seamlessly with various data sources makes it a preferred choice for institutions aiming to provide hands-on, practical training in data visualization. Power BI, backed by Microsoft’s ecosystem, is gaining significant traction, particularly among enterpr

The Data Visualization Tools Market size was valued at USD 10.39 billion in 2023 and is projected to reach USD 22.12 billion by 2032, exhibiting a CAGR of 11.4 % during the forecasts period. This unprecedented growth is attributed to the increasing demand for real-time data analysis, the need for effective decision-making, and the rising adoption of cloud-based data visualization tools. Additionally, government initiatives aimed at improving data literacy and the implementation of data visualization solutions in various industry verticals are further fueling market growth. Data visualization tools enable businesses and individuals to transform complex data into insightful visual representations. Tools like Tableau, Power BI, and Google Data Studio offer user-friendly interfaces to create interactive charts, graphs, and dashboards. They help analyze trends, patterns, and correlations, aiding decision-making processes. Advanced features include real-time data updates, collaboration capabilities, and integration with various data sources like databases and cloud services.

Graph and download economic data for Sources of Revenue: Sound Recording Studio Rental and Leasing for Sound Recording Studios, All Establishments, Employer Firms (REVSRLEF51224ALLEST) from 2013 to 2022 about recording, employer firms, accounting, leases, revenue, establishments, rent, services, and USA.

United States - Sources of Revenue: Studio Recording for Sound Recording Studios, All Establishments, Employer Firms was 662.00000 Mil. of $ in January of 2022, according to the United States Federal Reserve. Historically, United States - Sources of Revenue: Studio Recording for Sound Recording Studios, All Establishments, Employer Firms reached a record high of 699.00000 in January of 2015 and a record low of 546.00000 in January of 2018. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Sources of Revenue: Studio Recording for Sound Recording Studios, All Establishments, Employer Firms - last updated from the United States Federal Reserve on November of 2025.

United States - Sources of Revenue: Sound Recording Studio Rental and Leasing for Sound Recording Studios, All Establishments, Employer Firms was 61.00000 Mil. of $ in January of 2022, according to the United States Federal Reserve. Historically, United States - Sources of Revenue: Sound Recording Studio Rental and Leasing for Sound Recording Studios, All Establishments, Employer Firms reached a record high of 66.00000 in January of 2017 and a record low of 42.00000 in January of 2011. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Sources of Revenue: Sound Recording Studio Rental and Leasing for Sound Recording Studios, All Establishments, Employer Firms - last updated from the United States Federal Reserve on November of 2025.

Trims Studio Mandarin Source Limited H Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

🌌 WISE–NEOWISE Asteroid & Comet Segmented Dataset

https://img.shields.io/badge/Author-pinuto-blueviolet?style=for-the-badge"> https://img.shields.io/badge/License-CC--BY--4.0-brightgreen?style=for-the-badge"> https://img.shields.io/badge/Source-NASA%20WISE%2FNEOWISE-blue?style=for-the-badge&logo=nasa"> https://img.shields.io/badge/Category-Astronomy%20%26%20Data%20Science-black?style=for-the-badge&logo=databricks"> https://img.shields.io/badge/ML%20Ready-Yes-ff69b4?style=for-the-badge&logo=tensorflow"> https://img.shields.io/badge/Data%20Size-202%20records-orange?style=for-the-badge&logo=google-data-studio"> https://img.shields.io/badge/Python-Compatible-yellow?style=for-the-badge&logo=python"> https://img.shields.io/badge/Jupyter-Notebook-red?style=for-the-badge&logo=jupyter"> https://img.shields.io/badge/Kaggle-Available-20beff?style=for-the-badge&logo=kaggle"> https://img.shields.io/badge/GitHub-Open%20Source-success?style=for-the-badge&logo=github">

🪐 Overview

This repository contains an enhanced, log-scaled, and segmented version of NASA’s official WISE/NEOWISE discovery dataset of asteroids and comets. It was curated to bridge the gap between astronomical research and modern data science — combining physical, orbital, and derived features for machine learning, visualization, and orbital dynamics.

The dataset refines NASA’s original discovery statistics with additional transformations:

• 🌠 Cleaned & Normalized numeric fields
• 🔢 Log-scaled outlier-heavy columns (e.g., period, aphelion)
• 🧭 Derived Metrics: semi-major axis (a), eccentricity (e), Tisserand parameter
• 🪐 Segmented Labels: orbit, eccentricity, and Tisserand-based classifications

📊 Dataset Highlights

✅ 202 celestial objects (NEAs + Comets) ✅ 14 core scientific columns ✅ 7 derived metrics ✅ 3 segmentation systems (orbit, eccentricity, Tisserand) ✅ 7 log-scaled parameters for ML preprocessing ✅ 100% public NASA data integrity

📂 Dataset Schema

🧮 Derived Metrics Explained

# Semi-major axis (AU)
df['a_au'] = (df['q_au'] + df['Q_au']) / 2

# Orbital eccentricity
df['e'] = (df['Q_au'] - df['q_au']) / (df['Q_au'] + df['q_au'])

# Tisserand parameter (w.r.t. Jupiter)
df['tisserand_jupiter'] = 5.2/df['a_au'] + 2 * np.sqrt(df['a_au']/5.2 * (1 - df['e']**2)) * np.cos(np.radians(df['i_deg']))

🧠 Use Cases

• 🌍 Astronomical Research: Analyze asteroid family dynamics and resonance behavior.
• 🤖 Machine Learning: Predict potential hazards or classify orbit types.
• 📈 Visualization: Plot a–e–i relationships or 3D orbital planes.
• 🧩 Feature Engineering: Combine orbital and physical metrics for hybrid models.

📡 Scientific Context

The WISE/NEOWISE Mission conducted a full-sky infrared survey, identifying thousands of near-Earth objects (NEOs). These observations have become fundamental in understanding planetary defense, orbital evolution, and compositional diversity. This dataset extends NASA’s mission data for computational analysis.

🌍 Data Source & Credits

• 📦 Original Source: [NASA Open Data Portal – WISE NEA/Comet Discovery Statistics](https://data.nasa.gov/dataset/WISE-NEA-COMET-DISCOVERY-STATISTICS/7qz6-zrq...

Pixar Movies Dataset

A comprehensive dataset of Pixar movies, including details on their release dates, directors, writers, cast, box office performance, and ratings. This dataset is gathered from official sources, including Pixar, Rotten Tomatoes, and IMDb, to provide accurate and relevant information for anyone interested in analyzing Pixar's films.

About Pixar Movies

Pixar Animation Studios, known for its quality animation and storytelling, has produced a series of animated movies that have captivated audiences around the world. This dataset captures key details from Pixar’s filmography, including box office earnings, critical ratings, and character information, making it a valuable resource for those analyzing trends in animation, its movie plot lines and beloved characters, and movie ratings. For more information, visit Pixar, Rotten Tomatoes, and IMDb.

Dataset Information

• Source: Data is compiled from public sources, including official information from Pixar, Rotten Tomatoes, IMDb, and Wikipedia. Cells are each derived from one or more sources and then selected/verified.
• Purpose: The dataset is intended for research, educational, and analytical purposes.
• Accuracy: Efforts have been made to ensure accuracy, though users are encouraged to verify individual data points for critical use.
• Updates: This dataset captures information available up to the latest Pixar releases.

Data Structure

Dataset Columns

This data was compiled, enriched, reviewed, and curated using Research by Rummage Labs. Research by Rummage Labs enables you to curate verified datasets to power your enterprise. Read more here: https://rummagelabs.com/.

A reach of the Sacramento River near Glenn, California, was selected as a field site to test a sensor payload developed by the U.S. Geological Survey and the National Aeronautics and Space Administration for estimating surface flow velocities in rivers. The payload, called the River Observing System (RiOS), can be deployed from an uncrewed aircraft system (UAS). RiOS includes visible and thermal cameras, a laser range finder, an inertial navigation system, an embedded computer for storing and processing data, and a wireless link for transmitting data to a ground station. This data release includes thermal imagery acquired by RiOS and stored in Robot Operating System (ROS) *.bag files. The bag files are organized into two separate zip archives, one for each date of data collection. Foxglove Studio, an open-source data visualization tool, can be used to view the thermal images contained within the bag files (see link in Related External Sources). Once the thermal bag file is loaded ...

Contains data and code for the manuscript 'Mean landscape-scale incidence of species in discrete habitats is patch size dependent'. Raw data consist of 202 published datasets collated from primary and secondary (e.g., government technical reports) sources. These sources summarise metacommunity structure for different taxonomic groups (birds, invertebrates, non-avian vertebrates or plants) in different types of discrete metacommunities including 'true' islands (i.e., inland, continental or oceanic archipelagos), habitat islands (e.g., ponds, wetlands, sky islands) and fragments (e.g., forest/woodland or grass/shrubland habitat remnants). The aim of the study was to test whether the size of a habitat patch influences the mean incidences of species within it, relative to the incidence of all species across the landscape. In other words, whether high-incidence (widespread) or low-incidence (narrow-range) species are found more often than expected in smaller or larger patches. To achieve th..., Details regarding keyword and other search strategies used to collate the raw database from published sources were presented in Deane, D. C. & He, F. (2018) Loss of only the smallest patches will reduce species diversity in most discrete habitat networks. Glob Chang Biol, 24, 5802-5814 and in Deane, D.C. (2022) Species accumulation in small-large vs large-small order: more species but not all species? Oecologia, 200, 273-284. Minimum data requirements were presence absence records for all species in all patches and area of each habitat patch. The database consists of 202 published datasets. The first column in each dataset is the area of the patch in question (in hectares), other columns record presence and absence of each species in each patch. In the study, a metric was calculated for every patch that quantifies how the incidence of species in each patch compares with an expectation derived from the occupancy of all species in all patches (called mean species landscape-scale incid..., All provided files are intended for use within the R-programming environment. The raw database records required to run the analysis from scratch, along with processed data used to run regression models are saved as R data objects (i.e., extension '.RData'). The fitted model obtained in analysis and used to generate results is also an R object, but of class 'brmsfit' (requiring R package brms is loaded into the R-workspace). Both object types can be opened in R (R Studio, etc). , # Data from 'Species representation in discrete habitats is patch size dependent'

Contains the raw data and code used to reproduce the analysis and results in the manuscript.

The simplest way to do this is to save all files provided to a single folder. Code needed to run the analyses in the paper are in scr_R_code_Dryad_R01.txt. Change the file extension from .txt to .R and then the script can be opened directly in R/R Studio and this includes code to load all objects described and run all analyses.

Description of the data and file structure

Description of data files:

Data: Datha.RData - an R object of class 'list', each element of the list representing one of 202 p/a datasets obtained from published sources. Datasets are saved as sites x species dataframes, with patch area (in hectares) in the first column and species data in column numbers 2 to N+1, where N is the total number of species in that dataset (the +1 reflects the area data in the first column). Note, the nu...

Merged LeRobot Dataset

This dataset was created by merging multiple LeRobot datasets using the LeRobot Data Studio merge tool.

  Source Datasets

This merged dataset combines the following 2 datasets:

jackvial/koch_screwdriver_attach_orange_panel_ls_4_e5 jackvial/koch_screwdriver_attach_orange_panel_ls_6_e5

  Merge Details

Merge Date: Generated automatically Source Count: 2 datasets Episode Renumbering: Episodes are renumbered sequentially starting from 0… See the full description on the dataset page: https://huggingface.co/datasets/jackvial/mergetest11.

LaFresCat Multiaccent

We present LaFresCat, the first Catalan multiaccented and multispeaker dataset.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Commercial use is only possible through licensing by the voice artists. For further information, contact langtech@bsc.es and lafrescaproduccions@gmail.com.

Dataset Details

Dataset Description

The audios from this dataset have been created with professional studio recordings by professional voice actors in Lafresca Creative Studio. This is the raw version of the dataset, no resampling or trimming has been applied to the audios. Audios are stored in wav format at 48khz sampling rate

In total, there are 4 different accents, with 2 speakers per accent (female and male). After trimming, accumulates a total of 3,75h (divided by speaker IDs) as follows:

• Balear

  • olga -> 23.5 min
  • quim -> 30.93 min

• Central

  • elia -> 33.14 min
  • grau -> 37,86 min

• Occidental (North-Western)

  • emma -> 28,67 min
  • pere -> 25,12 min

• Valencia

  • gina -> 22,25 min
  • lluc -> 23,58 min

Uses

The purpose of this dataset is mainly for training text-to-speech and automatic speech recognition models in Catalan accents.

Languages

The dataset is in Catalan (ca-ES).

Dataset Structure

The dataset consists of 2858 audios and transcriptions in the following structure:

lafresca_multiaccent_raw
├── balear
│ ├── olga
│ ├── olga.txt
│ ├── quim
│ └── quim.txt
├── central
│ ├── elia
│ ├── elia.txt
│ ├── grau
│ └── grau.txt
├── full_filelist.txt
├── occidental
│ ├── emma
│ ├── emma.txt
│ ├── pere
│ └── pere.txt
└── valencia
├── gina
├── gina.txt
├── lluc
└── lluc.txt

Metadata of the dataset can be found in the file `full_filelist.txt` , each line represents an audio and follows the format:

audio_path | speaker_id | transcription

The speaker ids have the following mapping:

"quim": 0,
"olga": 1,
"grau": 2,
"elia": 3,
"pere": 4,
"emma": 5,
"lluc": 6,
"gina": 7

Dataset Creation

This dataset has been created by members of the Language Technologies unit from the Life Sciences department of the Barcelona Supercomputing Center, except the valencian sentences which were created with the support of Cenid, the Digital Intelligence Center of the University of Alicante. The voices belong to professional voice actors and they've been recorded in Lafresca Creative Studio.

Source Data

The data presented in this dataset is the source data.

Data Collection and Processing

These are the technical details of the data collection and processing:

• Microphone: Austrian Audio oc818

• Preamp: Focusrite ISA Two

• Audio Interface: Antelope Orion 32+

• DAW: ProTools 2023.6.0

Processing:

• Noise Gate: C1 Gate

• Compression BF-76

• De-Esser Renaissance

• EQ Maag EQ2

• EQ FabFilter Pro-Q3

• Limiter: L1 Ultramaximizer

Here's the information about the speakers:

Who are the source data producers?

The Language Technologies team from the Life Sciences department at the Barcelona Supercomputing Center developed this dataset. It features recordings by professional voice actors made at Lafresca Creative Studio.

Annotations

In order to check whether or not there were any errors in the transcriptions of the audios, we created a Label Studio space. In that space, we manually listened to subset of the dataset, and compared what we heard with the transcription. If the transcription was mistaken, we corrected it.

Personal and Sensitive Information

The dataset consists of professional voice actors who have recorded their voice. You agree to not attempt to determine the identity of speakers in this dataset.

Bias, Risks, and Limitations

Training a Text-to-Speech (TTS) model by fine-tuning with a Catalan speaker who speaks a particular dialect presents significant limitations. Mostly, the challenge is in capturing the full range of variability inherent in that accent. Each dialect has its own unique phonetic, intonational, and prosodic characteristics that can vary greatly even within a single linguistic region. Consequently, a TTS model trained on a narrow dialect sample will struggle to generalize across different accents and sub-dialects, leading to reduced accuracy and naturalness. Additionally, achieving a standard representation is exceedingly difficult because linguistic features can differ markedly not only between dialects but also among individual speakers within the same dialect group. These variations encompass subtle nuances in pronunciation, rhythm, and speech patterns that are challenging to standardize in a model trained on a limited dataset.

Funding

This work has been promoted and financed by the Generalitat de Catalunya through the Aina project, in addition the Valencian sentences have been created within the framework of the NEL-VIVES project 2022/TL22/00215334.

Dataset Card Contact

langtech@bsc.es

Merged LeRobot Dataset

This dataset was created by merging multiple LeRobot datasets using the LeRobot Data Studio merge tool.

  Source Datasets

This merged dataset combines the following 4 datasets:

guanfengliu/so101_main_bin_2cameras_new_1 guanfengliu/so101_main_bin_2cameras_new_2 guanfengliu/so101_main_bin_2cameras_new_3 guanfengliu/so101_main_bin_2cameras_new_4

  Merge Details

Merge Date: Generated automatically Source Count: 4 datasets Episode… See the full description on the dataset page: https://huggingface.co/datasets/guanfengliu/leon_so101_main_bin_2cameras_new.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F19062145%2F025ccf521f62db512b4a98edd0b3508a%2FKimia_Farma_Dashboard.jpg?generation=1748428094441761&alt=media" alt="">This project analyzes Kimia Farma's performance from 2020 to 2023 using Google Looker Studio. The analysis is based on a pre-processed dataset stored in BigQuery, which serves as the data source for the dashboard.

Project Scope

The dashboard is designed to provide insights into branch performance, sales trends, customer ratings, and profitability. The development is ongoing, with multiple pages planned for a more in-depth analysis.

Current Progress

✅ The first page of the dashboard is completed
✅ A sample dashboard file is available on Kaggle
🔄 Development will continue with additional pages

Dataset Overview

The dataset consists of transaction records from Kimia Farma branches across different cities and provinces. Below are the key columns used in the analysis: - transaction_id: Transaction ID code - date: Transaction date - branch_id: Kimia Farma branch ID code - branch_name: Kimia Farma branch name - kota: City of the Kimia Farma branch - provinsi: Province of the Kimia Farma branch - rating_cabang: Customer rating of the Kimia Farma branch - customer_name: Name of the customer who made the transaction - product_id: Product ID code - product_name: Name of the medicine - actual_price: Price of the medicine - discount_percentage: Discount percentage applied to the medicine - persentase_gross_laba: Gross profit percentage based on the following conditions:
Price ≤ Rp 50,000 → 10% profit
Price > Rp 50,000 - 100,000 → 15% profit
Price > Rp 100,000 - 300,000 → 20% profit
Price > Rp 300,000 - 500,000 → 25% profit
Price > Rp 500,000 → 30% profit
- nett_sales: Price after discount - nett_profit: Profit earned by Kimia Farma - rating_transaksi: Customer rating of the transaction

Files Provided

📌 kimia farma_query.txt – Contains SQL queries used for data analysis in Looker Studio
📌 kimia farma_analysis_table.csv – Preprocessed dataset ready for import and analysis

📢 Published on : My LinkedIn

Merged LeRobot Dataset

This dataset was created by merging multiple LeRobot datasets using the LeRobot Data Studio merge tool.

  Source Datasets

This merged dataset combines the following 2 datasets:

BobShan/insert_cube0 BobShan/insert_cube2

  Merge Details

Merge Date: Generated automatically Source Count: 2 datasets Episode Renumbering: Episodes are renumbered sequentially starting from 0

  Dataset Structure

meta/info.json: { "codebase_version":… See the full description on the dataset page: https://huggingface.co/datasets/BobShan/insert_cube.

Merged LeRobot Dataset

This dataset was created by merging multiple LeRobot datasets using the LeRobot Data Studio merge tool.

  Source Datasets

This merged dataset combines the following 2 datasets:

BobShan/pick_bear2 BobShan/pick_bear0

  Merge Details

Merge Date: Generated automatically Source Count: 2 datasets Episode Renumbering: Episodes are renumbered sequentially starting from 0

  Dataset Structure

meta/info.json: { "codebase_version":… See the full description on the dataset page: https://huggingface.co/datasets/BobShan/pick_bear.

Merged LeRobot Dataset

This dataset was created by merging multiple LeRobot datasets using the LeRobot Data Studio merge tool.

  Source Datasets

This merged dataset combines the following 2 datasets:

pweids/record-test pweids/record-test-023

  Merge Details

Merge Date: Generated automatically Source Count: 2 datasets Episode Renumbering: Episodes are renumbered sequentially starting from 0

  Dataset Structure

meta/info.json: { "codebase_version":… See the full description on the dataset page: https://huggingface.co/datasets/pweids/record-merged.

About

This dataset is for only ASR testing in Vietnamese. We collect data from various sources. This is the first version of the dataset.

This dataset contains a comprehensive collection of anime titles spanning the years 1970 to 2024. The data was collected from MyAnimeList using web scraping techniques. It includes essential information about each anime, such as its unique ID, title, genre, description, studio, release year, and user ratings. The dataset offers a valuable resource for exploring the evolution of anime over the decades and understanding trends in the industry. Researchers, anime enthusiasts, and data analysts can use this dataset to analyze various aspects of anime production and consumption, including popular genres, top-rated studios, and changes in audience preferences over time. The dataset is presented in a CSV format and is suitable for a wide range of data analysis and machine learning applications.

Columns in the data.json :

mal_id: Unique identifier for the anime entry. titles: List of titles associated with the anime. In this case, "Attack No.1". type: Type of the anime, e.g., TV series. source: Source material of the anime, here it's based on a manga. episodes: Number of episodes in the anime (104 in this case). rating: Audience rating category, PG-13 in this example. score: Average score given to the anime by users. scored_by: Number of users who have scored the anime. rank: Ranking of the anime based on score or popularity. popularity: Popularity ranking of the anime. members: Number of members who have added this anime to their list. favorites: Number of users who have favorited this anime. synopsis: Plot summary or synopsis of the anime. studios: Production studio responsible for creating the anime. genres: List of genres the anime belongs to (e.g., Drama, Sports). themes: List of themes present in the anime (e.g., Team Sports).

Columns in the user_recommendation.csv :

mal_id: This column represents the ID of an anime that users have watched or interacted with. mal_id_recomm: This column lists the IDs of anime recommended by users for a specific mal_id. votes: The votes column indicates the number of votes or recommendations given by users for the recommendation of mal_id_recomm for mal_id.

The dataset is ready for exploration, analysis, and visualization to uncover insights into the world of anime and its dynamic landscape.

This dataset contains 30,000 YouTube video analytics records, created to simulate realistic YouTube Studio performance data from the last 12 months. It provides per-video metrics such as impressions, click-through rate (CTR), average view duration, watch time, likes, comments, and traffic sources.

This dataset is useful for:

YouTube trend analysis Predictive modeling Engagement analysis Audience retention studies Recommender systems Machine learning and EDA Content performance optimization

All upload dates fall within the previous 365 days, making the dataset aligned with recent YouTube trends.

COLUMN DESCRIPTIONS

Post_ID – Unique video ID used to join with other tables. Upload_Date – Video upload date within the last 1 year. Video_Duration_Min – Total length of the video in minutes. Avg_View_Duration_Sec – Average watch time per viewer. Avg_View_Percentage – Percentage of the video that users watched. Subscribers_Gained – Number of subscribers gained from this video. Traffic_Source – How viewers discovered the video (Search, Suggested, Browse, External, etc.). CTR_Percentage – Click-through rate of the thumbnail impressions. Impressions – How many users saw the video thumbnail across YouTube surfaces. Likes – Total number of likes received. Comments – Number of comments posted. Shares – Number of times the video was shared. Total_Watch_Time_Hours – Total accumulated watch time in hours (critical YouTube ranking signal).

WHY THIS DATASET MATTERS

YouTube’s recommendation system prioritizes: high watch time high CTR strong audience retention strong engagement (likes, comments, shares)

This dataset includes all of these metrics, allowing deep analysis of: what makes videos perform well which traffic sources are strongest how video length affects watch time how engagement influences discoverability seasonal or monthly patterns in video performance