> Subject Guide - Applied Data Science

This guide provides an overview of the library resources available for Applied Data Science students.

Programme website

• Analytics
• API
• Big Data
• Business Intelligence
• Clustering
• Data Architect
• Data Engineer
• Data Mining
• Data Science
• Data Warehouse
• Database
• Deep Learning
• Graph Database
• Hadoop
• Machine Learning
• NoSQL
• Predictive Modeling
• Python
• R Language
• SQL
• Statistic
• Tensorflow
• Visualization

ScienceDirect is Elsevier's premier platform of peer-reviewed scholarly literature. It advances research and scholarship with the world's leading database of peer-reviewed, full-text scientific, technical and health literature. There are 21M articles & book chapters, 800 open-access journals and 3.3M open-access articles on the platform. It contains a large collection of Social Sciences and Humanities journals and books, highlighting historical context, current developments, theories, applications, trends and more.

ScienceDirect: Computer Science Collection provides coverage of all areas of Computer Science including Software Engineering, Theoretical Computer Science, Applied Computer Science, Signal Processing, Artifical Intelligence, Information System, Computer Networks and Communications, and more.

Taylor & Francis Group collaborate with researchers, scholarly societies, universities and libraries worldwide to bring knowledge to life. The Social Sciences & Humanities Library covers over 1,000 scholarly journals content spans various areas of Humanities and Social Sciences:

• Behavioral Science
• Computer Science
• Criminology & Law
• Media, Cultural & Communications Studies
• Sociology & Related Disciplines
• Sports, Leisure & Tourism

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GitHub is a web-based platform that serves as a hub for software development. It provides a collaborative environment for developers to work together on projects, track changes, and manage version control. GitHub is widely used for open-source projects, where developers worldwide can contribute code and collaborate on projects. It also offers various features such as bug tracking, project management, and code review tools. GitHub has become an essential tool for modern software development, enabling teams to work together efficiently and effectively.

Kaggle is a popular online platform that hosts data science competitions, datasets, and machine learning models. It was founded in 2010 and has since grown into a vibrant community of data scientists, machine learning engineers, and researchers. Kaggle provides a platform for individuals and teams to compete against each other in solving real-world data problems by using machine learning and statistical techniques. The competitions hosted on Kaggle cover a wide range of topics, including image recognition, natural language processing, and predictive modeling. In addition to competitions, Kaggle also provides access to a vast collection of high-quality datasets, notebooks, and tutorials, making it an excellent resource for learning and practicing data science skills. With its strong community and resources, Kaggle has become a go-to platform for data enthusiasts and professionals worldwide.

Data.world is a collaborative platform for data scientists, analysts, and enthusiasts to discover, share, and analyze data. It was founded in 2015 and has since grown into a thriving community of data professionals who use the platform to find and work with data. Data.world offers a variety of features, including a searchable repository of public datasets, tools for data analysis and visualization, and collaboration tools for teams. Users can upload their own datasets, collaborate with others on data projects, and share insights and findings with the broader community. The platform also offers integrations with popular data analysis tools like Tableau, R, and Python. With its focus on collaboration and community, Data.world has become a valuable resource for anyone looking to work with data, from beginners to seasoned professionals.

The UC Irvine Machine Learning Repository is a collection of datasets that are widely used in the machine learning community for research and education purposes. It was created in 1987 as a way to make datasets more widely available to researchers and has since grown to include over 500 datasets. The datasets cover a wide range of topics, including classification, regression, clustering, and recommendation systems. Many of the datasets have been preprocessed and cleaned, making them suitable for use in machine learning experiments. The repository also provides various tools for accessing and working with the data, including software libraries and data visualization tools. With its extensive collection of datasets and resources, the UC Irvine Machine Learning Repository has become a valuable resource for researchers, students, and machine learning practitioners worldwide.

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