Introduction to Data Science and Machine Learning Algorithms

Silahtaroğlu, Gökhan; Dınçer, Hasan; Yüksel, Serhat

doi:10.1007/978-3-030-74176-1_1

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…It is reasonable to assume that the more data leads to more complexity. Recently, Llaha and Aliu [17] discussed the application of data visualization and machine learning algorithms for better decision making in criminology. However similar approaches can be taken in education for decision making at any level.…”

Section: Related Workmentioning

confidence: 99%

Leveraging Visualization and Machine Learning Techniques in Education: A Case Study of K-12 State Assessment Data

Taylor,

Gupta,

Jung

2024

Preprint

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As data driven models gain importance in driving decisions and processes, recently it becomes increasingly important to visualize the data with both speed and accuracy. A massive amount of data is presently generated in the educational sphere from various learning platforms, tools, and institutions. Visual analytics of educational big data has capability to improve student learning, develop strategies for personalized learning, and improve faculty productivity. How-ever, there are limited advancements in the education domain for data-driven decision making leveraging the recent advancements in the field of machine learning. Some of the recent tools such as Tableau, Power BI, Microsoft Azure suite, Sisense etc. leverage artificial intelligence and machine learning techniques to visualize data and generate insights out of it, however their ap-plicability in educational advances is limited. This paper focuses on leveraging machine learn-ing and visualization techniques to demonstrate their utility through a practical implementation using k-12 state assessment data compiled from the institutional websites of the State of Texas and Louisiana. Effective modeling and predictive analytics are the focus of the sample use case presented in this research. Our approach demonstrates the applicability of web technology in conjunction with machine learning to provide a cost-effective and timely solution to visualize and analyze big educational data. Additionally, ad-hoc visualization provides contextual anal-ysis within areas of concern for Education Agencies (EA).

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Section: Related Workmentioning

confidence: 99%

Leveraging Visualization and Machine Learning Techniques in Education: A Case Study of K-12 State Assessment Data

Taylor,

Gupta,

Jung

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Using data visualization to analyze data and present valuable insights is not only vital to engineering and computer science disciplines, but also to business disciplines. For instance, disciplines such as finance and accounting utilize visualization techniques to analyze and present financial and accounting data in ways that are easily digestible to stakeholders According to author of [3] Data visualization involves presenting data in graphical or pictorial form which makes the information easy to take in. It helps to explain facts and determine courses of action.…”

Section: Related Workmentioning

confidence: 99%

Impact Of Data Visualization In Data Analysis To Improve The Efficiency Of Machine Learning Models

Govind Shinde,

Shivthare

2024

JAZ

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An essential component of machine learning is data visualization, which helps analysts comprehend and interpret patterns, connections, and trends in data. Data visualization is a crucial aspect of machine learning that enables analysts to understand and make sense of data patterns, relationships, and trends. Through data visualization, insights and patterns in data can be easily interpreted. This research paper explores the significant impact of data visualization on the efficiency of machine learning (ML) models during the data analysis phase. Data visualization serves as a powerful tool for data scientists and ML practitioners by offering intuitive insights into complex datasets, facilitating a deeper understanding of the underlying data characteristics, and guiding the decision-making process in model development. The visual techniques enhance various aspects of the data analysis phase, including exploratory data analysis (EDA), feature selection and engineering, anomaly detection, and assumption validation, ultimately leading to the development of more accurate and efficient machine learning models.

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Introduction to Data Science and Machine Learning Algorithms

Cited by 2 publications

References 3 publications

Leveraging Visualization and Machine Learning Techniques in Education: A Case Study of K-12 State Assessment Data

Leveraging Visualization and Machine Learning Techniques in Education: A Case Study of K-12 State Assessment Data

Impact Of Data Visualization In Data Analysis To Improve The Efficiency Of Machine Learning Models

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