Abstract
This chapter will guide you through the modeling, uses, and trends in data analysis and data science. The authors focus on the importance of pictorial data in replacement of numeric data. In most situations, graphical representation of data can present the information more distinctly, informative, and in less space than the same information requires in sentence form. This chapter provides a brief knowledge about representing data to more understandable form such that any person whether layman or not can understand it without any difficulty. This chapter also deals with the software Tableau which we use to convert the table data into graphical data. This Chapter contains 11 heat maps related to the world economies and their detailed study on several different topics. It will also give light on the basics of Python Language and its various algorithm studies to compare all the world economies based on their development.
TopData Analysis: An Introduction
The data analysis takes place when event is repeated over a period to many numbers of times. It can be detected statistically or graphically. The data can be of many forms and can be collected from various sources such as survey on internet, previous records of associated data to be analysed. The data which have been collected are known as raw data. The requirement of data analysis is that to predict the possibility for future of an event. For example- Let a day be selected for predicting the possibility of rain, to predict the possibility by analysing the whole record of rain for that day and by making algorithm, the probability of the rain for that day will be resulted. This is how the prediction occurs. The data analysis is using widely by the different websites and also in different fields. The data analysis is used in different fields such as education, weather, business intelligence, construction, digital marketing, risk analysis, software analysis etc. The data analysis is also done by cleaning and modelling of the data and helps in supporting to the resulted data. The data refining is one of the main procedures for data analysis. The raw data should be appropriate for data analysis (Said & Torra, 2019).
History
Herman Hollerith invented Tabulating Machine in 1890. This machine was based on punch cards. The data scientist came in need when data was gathering in large amount and handling the data became difficult. The data handling was also a part of data analysis. The data analysis has given many benefits such as to manage and store the data and to use that data for the prediction of an event. It was important to use that data that was stored in machine because it helped to understand and improve the business processes and economic growth for a country and saved the time and money. Every company got the benefit. Before analysis the data it is necessary that for whom and for which purpose the analysis is going to be conducting. That became the main step of data analysis which helps the people to make decisions. Historical analytics is also a term which defines the analysis of an activity and the data from the past to recognize the patterns, correlations and other statistical relationships. Business Evolution was the main reason to build a field of data analysis (Miyazaki, M. 2015).
Key Terms in this Chapter
Language R: Language R is a programming language developed in 1993 for the graphics and statistical computing. It is useful for Statisticians and those who deal with number chunks.
Data Analysis: Data Analysis is a process of gathering and extracting information from the data already present in different ways and order to study the pattern occurs.
Tableau Software: Tableau software is a virtual platform for conversion of numbers into graphs and charts form. It simplifies the process of conversion and visualization.
Data Visualization: Data Visualization is a way of representing the data collected in the form of figures and diagrams like tables, charts, graphs in order to make the data for common man more easily understandable.
Data Science: Data Science is the branch of science that uses technologies to predict the upcoming nature of different things such as a market or weather conditions. It shows a wide usage in today’s world.