Datasets For Big Data Projects

Datasets For Big Data Projects which is most prevalent research area which emerges rapidly with innovative topics and techniques are involved below. Among diverse areas, we provide a popular and notable list of highly beneficial datasets in which we have assisted for scholars. From machine learning to big data analytics, these datasets are widely utilized for broad scope of data science research:

Common Purpose Datasets

1. Kaggle Datasets

• Explanation: Encompassing the organized and unorganized data, this Kaggle dataset provides broad scope of datasets in diverse segments. It can be applicable for statistical analysis, deep learning and machine learning.
• Significant Applications: Exploratory data analysis, project prototyping and rivalries.

2. UCI Machine Learning Repository

• Explanation: For machine learning studies, it is a suitable and extensive library of datasets. Topics like clustering, regression and classification are encompassed.
• Significant Applications: Model training, algorithm standards and educational studies.

3. Google Dataset Search

• Explanation: Over the web which incorporates public, educational and government data repositories, it aids in detecting datasets and is a particularized search engine.
• Significant Applications: Data accumulation, cross-domain data research and detecting the intended dataset.

Healthcare Datasets

4. MIMIC-III

• Explanation: It is an open-source dataset and incorporates entire patient data like diagnoses, laboratory reports and populations by its significant care database.
• Significant Applications: Medical informatics, predictive modeling and healthcare analytics.

5. UCI Heart Disease Dataset

• Explanation: Considering the patients who are affected by heart diseases, this datasets includes data of patients. Demographic features and clinical records are involved.
• Significant Applications: Health data investigation, predictive modeling for healthcare results and susceptibility analysis.

6. National Health and Nutrition Examination Survey (NHANES)

• Explanation: Encompassing the dietary, population and health-based data, it is referred to as an extensive health care analysis.
• Significant Applications: Epidemiological research, analysis of health patterns and public health studies.

Finance and Economics Datasets

7. Yahoo Finance

• Explanation: Particularly for investment assets, stocks and inventory, past financial records are offered by this dataset.
• Significant Applications: Algorithmic trading frameworks, analysis of stock market and financial prediction.

8. World Bank Open Data

• Explanation: Incorporating the demographic statistics, financial indicators and more, it acts as an enriched library of global development data.
• Significant Applications: Research of policy implications, pattern analysis and financial studies.

9. Federal Reserve Economic Data (FRED)

• Explanation: From the S. Federal Reserve, FRED includes an extensive database of financial data. Economic and macroeconomic pointers are involved.
• Significant Applications: Exploration of economic policy, analysis of financial market and economic prediction.

Social Media and Web Data

10. Twitter API

• Explanation: For social media analytics in real-time, it enables Twitter data and involves trends, user profiles and tweets.
• Significant Applications: Social network analysis, trend analysis and sentiment analysis.

11. Reddit Datasets

• Explanation: Regarding the consumers for distributing diverse datasets, this dataset is highly beneficial along with those gathered from Reddit and is effective for exploration and studies.
• Significant Applications: Community detection, sentiment analysis and text analysis.

12. Common Crawl

• Explanation: This Common Crawl foundation encompasses the enriched library of web data. Text data, initial web page data and metadata are included.
• Significant Applications: Text analysis, NLP (Natural Language Processing) projects and web scraping.

Image and Video Datasets

13. ImageNet

• Explanation: In accordance with WordNet hierarchy, ImageNet arranges the datasets in a proper manner and is an extensive image database. For image classification programs, it is highly adaptable.
• Significant Applications: Computer vision analysis, object detection and image segmentation.

14. COCO Dataset

• Explanation: Images with classification masks, bounding boxes and object labels are included in this Common Objects in Context dataset.
• Significant Applications: Classification, visual recognition and object detection.

15. YouTube-8M

• Explanation: Millions of YouTube video IDs and characteristics are involved in an extensive labeled video dataset.
• Significant Applications: Activity recognition, multimedia retrieval and video analysis.

Geospatial and Environmental Datasets

16. Global Land Cover Facility (GLCF)

• Explanation: For exploring land cover and land usage modifications, it offers various geographical data and satellite images.
• Significant Applications: Geospatial analysis, planning of land usage and ecological monitoring.

17. OpenStreetMap (OSM)

• Explanation: It offers significant map characteristics and geographical data. In developing a free adjustable map of the world, OSM is considered as an interdisciplinary project.
• Significant Applications: Analysis of geographical data, urban planning and GIS projects.

18. NASA Earth Data

• Explanation: Especially from NASA’s Earth Observing System Data and Information System, this NASA Earth Data provides broad scope of datasets. Satellite images and climate data are efficiently involved here.
• Significant Applications: Ecological monitoring, remote sensing and climate change studies.

Text and Natural Language Processing (NLP) Datasets

19. 20 Newsgroups

• Explanation: Normally, 20 various newsgroups are structured from a set of about 20,000 newsgroup documents.
• Significant Applications: Topic modeling, NLP analysis and text segmentation.

20. Enron Email Dataset

• Explanation: From the Enron Corporation, this dataset provides an extensive group of emails. For exploring network analysis and email communication, it is very beneficial.
• Significant Applications: Social network analysis, categorization of email and text mining.

21. Gutenberg Books Dataset

• Explanation: Specifically from Project Gutenberg, it gathers the related texts. A broad scope of public domain books are involved in this dataset.
• Significant Applications: Language modeling, sentiment analysis and text analysis.

Other Captivating Datasets

22. Open Data on AWS

• Explanation: Including diverse fields such as transportation, climate and genomics, it gathers several freely accessible datasets which are conducted on Amazon Web Services.
• Significant Applications: Cross-domain analysis, big data projects and research on cloud-based data.

23. Stanford Large Network Dataset Collection (SNAP)

• Explanation: Encircling web graphs, social networks and collaboration networks, an extensive network datasets are offered through SNAP.
• Significant Applications: Graph algorithms, community detection and network analysis.

24. Open Data Commons

• Explanation: As regards diverse domains, it is referred to as a collaborative resource for detecting and distributing the open datasets.
• Significant Applications: Data investigation, policy analysis and research projects.

25. The World Bank’s World Development Indicators

• Explanation: It is one of the intriguing dataset and provides economic data across the world-wide. Ecological data, GDP, education and health are incorporated in this dataset.
• Significant Applications: Policy analysis, worldwide pattern analysis and growth economics.

What are some really helpful datasets for a data science project?

If you are performing a project on data science, choose appropriate and effective datasets which guide you effectively throughout the process. For algorithm-related data science projects, some of the recommended and efficient lists of datasets are suggested by us:

1. Classification and Regression

MNIST Handwritten Digit Database

• Explanation: Especially for segmentation programs, this database consists of 70,000 grayscale images of handwritten digits.
• Critical Applications: Design of deep learning model and image segmentation.
• Approach: MNIST Database

California Housing Prices

• Explanation: Incorporating the characteristics such as number of rooms, place and economic levels, it offers data regarding the process of house in California.
• Critical Applications: Feature engineering, predictive modeling and regression analysis.
• Approach: California Housing Dataset

CIFAR-10 and CIFAR-100

• Explanation: Accordingly, it consists of 10 and 100 classes of extensive datasets in small images.
• Critical Applications: Convolutional neural networks and image segmentation.
• Approach: CIFA-10 and CIFAR-100.

Wine Quality Dataset

• Explanation: As regards red and white wines, this dataset contains the specific chemical features and best ratings.
• Critical Applications: Regression, feature selection and categorization.

2. Natural Language Processing (NLP)

20 Newsgroups

• Explanation: Generally, 20 diverse groups are classified from a set of about 20,000 documents of newsgroups.
• Critical Applications: Sentiment analysis, topic modeling and text classification.
• Approach: 20 Newsgroups

Enron Email Dataset

• Explanation: From the Enron Corporation, it contains an extensive collection of emails. For exploring email communication, this dataset is highly beneficial.
• Critical Applications: Network analysis, text mining and email categorization.

Sentiment140

• Explanation: This dataset is specifically designed for evaluating the sentiment. It includes 1.6 million tweets which are efficiently tagged as positive or negative.
• Critical Applications: NLP model training, opinion mining and sentiment analysis.

3. Time Series Analysis

Yahoo Finance Stock Market Data

• Explanation: For diverse industries, this dataset offers past records on stock prices, financial parameters and volumes.
• Critical Applications: Financial modeling, outlier detection and time series prediction.

Electricity Load Diagrams Dataset

• Explanation: Across numerous years, it consists of data regarding the electricity usage from 370 consumers.
• Critical Applications: Demand prediction, clustering and time series analysis.

NOAA Climate Data

• Explanation: Particularly from the NOAA (National Oceanic and Atmospheric Administration), it offers time series data on the basis of humidity, temperature and various climate factors.
• Critical Applications: Ecological prediction, pattern analysis and climate modeling.

4. Image and Video Processing

ImageNet

• Explanation: In accordance with WordNet hierarchy, ImageNet arranges the extensive image databases in a proper manner. For image classification programs, it can be highly applicable.
• Critical Applications: Deep learning, object identification and image segmentation.

COCO Dataset

• Explanation: For object detection and classification, labeled images are offered by means of Common Objects in Context dataset.
• Critical Applications: Image segmentation, visual recognition and object detection.

UCF101 Action Recognition Dataset

• Explanation: Normally, 10l segments are categorized from 13,320 videos of human behaviors which are involved in this dataset.
• Critical Applications: Deep learning, action recognition and video segmentation.

5. Health and Biomedical Data

MIMIC-III Clinical Database

• Explanation: From significant care facilities such as diagnoses, lab findings and demographics, this database derives an extensive dataset of patient health details.
• Critical Applications: Machine learning in healthcare, predictive modeling and medical informatics.

Breast Cancer Wisconsin Dataset

• Explanation: Particularly from digitized images of fine needle aspirate of breast conditions, it encompasses specific characteristics.
• Critical Applications: Predictive modeling, medical studies and categorization.

Human Activity Recognition Using Smartphones Dataset

• Explanation: In smartphones which are used by consumers in the course of diverse events, it gathers data from the accelerometers and gyroscopes.
• Critical Applications: Wearable device analytics, signal processing and activity recognition.

6. Anomaly Detection

KDD Cup 1999 Dataset

• Explanation: For intrusion detection, it involves an extensive amount of network connection logs.
• Critical Applications: Analysis of network traffic, cybersecurity and outlier detection.

Credit Card Fraud Detection Dataset

• Explanation: In September 2013, the transactions carried out by European cardholders through credit cards are included in this dataset.
• Critical Applications: Segmentation, outlier detection and fraud identification.

NAB: Numenta Anomaly Benchmark

• Explanation: On the basis of time-series data, this considerable standard assesses the techniques of outlier detection.
• Critical Applications: Time series analysis, evaluation and outlier detection.

7. Geospatial Data

OpenStreetMap (OSM)

• Explanation: In developing a free adjustable map of the world, OSM is referred to as a cooperative project. It involves specific map characteristics and geospatial data.
• Critical Applications: Urban planning, geographical analysis and GIS projects.

US Census Bureau Geographic Data

• Explanation: For diverse places in the United States, this database offers demographic and geographic data.
• Critical Applications: Policy analysis, population analysis and geospatial analysis.

NOAA Global Surface Temperature Data

• Explanation: Considering the global surface temperature outliers, it consists of huge data.
• Critical Applications: Ecological monitoring, climate modeling and geospatial analysis.

8. Recommender Systems

MovieLens Dataset

• Explanation: Encompassing the demographic data of consumers, MovieLens Dataset includes a huge dataset of movie ratings.
• Critical Applications: collaborative filtering, analysis of user activities and recommender systems.

Amazon Product Reviews Dataset

• Explanation: From Amazon consumers, it derives the product opinions and ratings. In the process of developing recommendation systems, this dataset is broadly applicable.
• Critical Applications: Sentiment analysis, user activity analysis and product suggestion.

Goodreads Book Reviews

• Explanation: Primarily from Goodreads, it includes feedback and ratings on books.
• Critical Applications: User profiles, recommender systems and text analysis.

9. Social Networks

Facebook Large Page-Page Network

• Explanation: Encircling the edges which determines the common likes among pages, this dataset contains Facebook of page-page networks.
• Critical Applications: Graph algorithms, group detection and analysis of social networks.

Reddit Comments Dataset

• Explanation: The comments which are posted on Reddit are included in this dataset. For evaluating the patterns and communication, it is very productive.
• Critical Applications: Network analysis, text mining and sentiment analysis.

Twitter Social Network Dataset

• Explanation: Depending on the user relationships and communications on Twitter, it offers data.
• Critical Applications: Trend analysis, social network analysis and sentiment analysis.

10. Miscellaneous Data Sources

UCI Machine Learning Repository

• Explanation: For machine learning studies, this library contains extensive datasets. It encompasses topics such as clustering, regression and categorization.
• Critical Applications: Data analysis, model testing and algorithm creation.

Kaggle Datasets

• Explanation: Among diverse segments, an extensive set of datasets are included here. For statistical analysis, deep learning and machine learning, it is highly adaptable.
• Critical Applications: Data investigation, rivalries and project prototyping.
• Approach: Kaggle Datasets

Google Dataset Search

• Explanation: Over the web, involving the public, educational and government data repositories, it acts as a search engine to detect the preferable datasets.
• Critical Applications: Data accumulation, cross-domain studies and data investigation.

• Big Data Project Dissertation Topics & Ideas

• Big Data Project Dissertation Topics & Ideas are listed below, we have worked on the below listed ideas. Selecting an appropriate and effective dataset is a very critical process in carrying out your project. To assist you in this challenging task, we have offered topics and guide you with several datasets with applications.
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• Forecast of complex financial big data using model tree optimized by bilevel evolution strategy
• Schedulable capacity forecasting for electric vehicles based on big data analysis
• Big data fuzzy C-means algorithm based on bee colony optimization using an Apache Hbase
• Time series big data: a survey on data stream frameworks, analysis and algorithms
• Addressing big data variety using an automated approach for data characterization
• Governance and sustainability of distributed continuum systems: a big data approach
• A literature review on one-class classification and its potential applications in big data
• A comprehensive survey of anomaly detection techniques for high dimensional big data
• A survey on bandwidth-aware geo-distributed frameworks for big-data analytics
• IoT Big Data provenance scheme using blockchain on Hadoop ecosystem
• An environment safety monitoring system for agricultural production based on artificial intelligence, cloud computing and big data networks\
• Intelligent algorithms for cold chain logistics distribution optimization based on big data cloud computing analysis
• Improving lookup and query execution performance in distributed Big Data systems using Cuckoo Filter
• Big Data architecture for intelligent maintenance: a focus on query processing and machine learning algorithms
• Efficient and scalable patients clustering based on medical big data in cloud platform
• Enhancing correlated big data privacy using differential privacy and machine learning
• Anomaly detection optimization using big data and deep learning to reduce false-positive
• A graph-based big data optimization approach using hidden Markov model and constraint satisfaction problem
• Performance analysis model for big data applications in cloud computing
• Impact of rail transit station proximity to commercial property prices: utilizing big data in urban real estate