Exploration of Tools for Data Science
Qasem S. Abu Al-Haija · 2022
Data science is a multidisciplinary area that concerns managing, manipulating, analyzing, extracting, and interpreting knowledge from a tremendous amount of data (known as a big data field). Data science is a vast body of expertise that uses methods, approaches, algorithms, tools, and concepts belonging to other interrelated areas, such as information science, probability and statistics, mathematics, big data analytics, and computer science and engineering. As such, mechanisms employed in data science include machine/deep learning techniques, data engineering, data visualization techniques, pattern recognition, data mining, probability modeling, signal processing, and computer vision. Data science is being actively adopted by several vital divisions that take advantage of data science, such as organizations, governments, and academia. This chapter will provide an overview of the top 10 tools and applications that must interest any data scientist. Chapter objectives include (but are not limited to): realize the use of Python in developing solutions of data science tasks, recognize the use of R Language can be used as an open-source data science provider, travel around the SQL to provide structured models for data science projects, navigate through data analytics and statistics using Excel, using D3.js scripting tools for data visualization. Also, short emphasis and practical examples/case studies are provided on data visualization, data analytics, regression, forecasting, and outlier detection.