Hands-On Data Science for Biologists Using Python

Hasija, Yasha; Chakraborty, Rajkumar

doi:10.1201/9781003090113

“…It mentions Tableau as a tool for creating data visualizations and dashboards [15]. Finally, an article briefly mentions Tableau as one of the tools used for data visualization, alongside Python libraries and other tools [16]. In conclusion, the literature reveals that Tableau is widely used in the data analytics industry as a tool for data visualization.…”

Section: Tableaumentioning

confidence: 99%

A Comprehensive Review of Data Visualization Tools: Features, Strengths, and Weaknesses

Lavanya,

Sindhuja,

Gaurav

et al. 2023

IJCERT

3

0

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Data visualization tools have revolutionized processing, analysing, and communicating data. With the increasing amount of data available, it has become increasingly important to present data in an easily understandable and visually appealing way. As such, data visualization tools have become essential to data analysis and decisionmaking processes in various fields, including business, healthcare, social sciences, and engineering. This review paper aims to provide an overview of the various data visualization tools available and their features, strengths, and weaknesses. We begin by introducing the concept of data visualization and its importance in the data analysis process. We then provide a brief history of data visualization, highlighting its evolution over time from static charts to interactive and dynamic visualizations. We then discuss the available data visualization tools, including bar charts, line graphs, scatter plots, heat maps, tree maps, and network diagrams. For each type of visualization, we provide examples of when and how they can be used to present and analyse data effectively. Next, we examine the features and functionalities of popular data visualization tools, such as Tableau, Power BI, Google Data Studio, D3.js, and Python libraries like Matplotlib, Seaborn, and Plotly. We discuss the strengths and weaknesses of each tool and provide examples of realworld applications. In addition, we highlight the importance of choosing the right visualization tool based on the data type, audience, and purpose. We also discuss best practices for creating effective data visualizations, such as choosing the right colour scheme, designing for accessibility, and avoiding common pitfalls. Finally, we discuss future trends and developments in data visualization, such as using augmented and virtual reality for data visualization and incorporating machine learning and artificial intelligence in data visualization tools. In conclusion, data visualization tools have become an essential part of the data analysis process. This review paper overviews the available data visualization tools and their features, strengths, and weaknesses. By understanding the strengths and limitations of different visualization tools, researchers and analysts can effectively present and analyse data, leading to better decision-making and insights.

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“…It mentions Tableau as a tool for creating data visualizations and dashboards [15]. Finally, an article briefly mentions Tableau as one of the tools used for data visualization, alongside Python libraries and other tools [16]. In conclusion, the literature reveals that Tableau is widely used in the data analytics industry as a tool for data visualization.…”

Section: Tableaumentioning

confidence: 99%

Assessing the Performance of Python Data Visualization Libraries: A Review

Lavanya,

Gaurav,

Sindhuja

et al. 2023

IJCERT

11

0

View full text Add to dashboard Cite

Data visualization tools have revolutionized processing, analysing, and communicating data. With the increasing amount of data available, it has become increasingly important to present data in an easily understandable and visually appealing way. As such, data visualization tools have become essential to data analysis and decisionmaking processes in various fields, including business, healthcare, social sciences, and engineering. This review paper aims to provide an overview of the various data visualization tools available and their features, strengths, and weaknesses. We begin by introducing the concept of data visualization and its importance in the data analysis process. We then provide a brief history of data visualization, highlighting its evolution over time from static charts to interactive and dynamic visualizations. We then discuss the available data visualization tools, including bar charts, line graphs, scatter plots, heat maps, tree maps, and network diagrams. For each type of visualization, we provide examples of when and how they can be used to present and analyse data effectively. Next, we examine the features and functionalities of popular data visualization tools, such as Tableau, Power BI, Google Data Studio, D3.js, and Python libraries like Matplotlib, Seaborn, and Plotly. We discuss the strengths and weaknesses of each tool and provide examples of realworld applications. In addition, we highlight the importance of choosing the right visualization tool based on the data type, audience, and purpose. We also discuss best practices for creating effective data visualizations, such as choosing the right colour scheme, designing for accessibility, and avoiding common pitfalls. Finally, we discuss future trends and developments in data visualization, such as using augmented and virtual reality for data visualization and incorporating machine learning and artificial intelligence in data visualization tools. In conclusion, data visualization tools have become an essential part of the data analysis process. This review paper overviews the available data visualization tools and their features, strengths, and weaknesses. By understanding the strengths and limitations of different visualization tools, researchers and analysts can effectively present and analyse data, leading to better decision-making and insights.

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Development of heart attack prediction model based on ensemble learning

Hasan

¹

,

Saleh

²

2021

EEJET

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With the advent of the data age, the continuous improvement and widespread application of medical information systems have led to an exponential growth of biomedical data, such as medical imaging, electronic medical records, biometric tags, and clinical records that have potential and essential research value. However, medical research based on statistical methods is limited by the class and size of the research community, so it cannot effectively perform data mining for large-scale medical information. At the same time, supervised machine learning techniques can effectively solve this problem. Heart attack is one of the most common diseases and one of the leading causes of death, so finding a system that can accurately and reliably predict early diagnosis is an essential and influential step in treating such diseases. Researchers have used various data mining and machine learning techniques to analyze medical data, helping professionals predict heart disease. This paper presents various features related to heart disease, and the model is based on ensemble learning. The proposed system involves preprocessing data, selecting attributes, and then using logistic regression algorithms as meta-classifiers to build the ensemble learning model. Furthermore, using machine learning algorithms (Support Vector Machines, Decision Tree, Random Forest, Extreme Gradient Boosting) for prediction on the Framingham Heart Study dataset and compared with the proposed methodology. The results show that the feasibility and effectiveness of the proposed prediction method based on group learning provide accuracy for medical recommendations and better accuracy than the single traditional machine learning algorithm.

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Hands-On Data Science for Biologists Using Python

Cited by 3 publications

References 0 publications

A Comprehensive Review of Data Visualization Tools: Features, Strengths, and Weaknesses

A Comprehensive Review of Data Visualization Tools: Features, Strengths, and Weaknesses

Assessing the Performance of Python Data Visualization Libraries: A Review

Development of heart attack prediction model based on ensemble learning

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