Describes the principles and methods of data analysis in an approach that can be understood by readers without specific knowledge of statistics or programming
This book teaches readers without specific knowledge of statistics or programming how to understand and use data analytics. The authors focus on explanation of intuition beyond the basic data analytics techniques. To do this, they employ easy to use tools to present and illustrate the examples. This book contains four parts. The first part motivates people for the necessity of analyzing data. The next part involves visualizing data and finding natural groups from data. Predicting the unknown is the subject of the next part, in which the authors discuss classification, regression, and advanced predictive methods. The last part discusses mining the web, and covers topics such as information retrieval, social network analysis, working with text, and recommender systems feedback. At the end of parts 2, 3, and 4 there is a project following the CRISP methodology that shows how to develop a project in the area of that part. The proposal is that the readers can develop their own project with their own dataset or with a dataset from a public repository. This book will be of interest to non-mathematicians, non-statisticians, and non-computer scientists interested in getting an introduction to data science.
- Explains the reasoning behind the given data mining techniques
- Uses freely available software packages to show readers how to perform data analysis
- Expands upon a unique illustrative example throughout all chapters
- Contains exercises at the end of each chapter, and larger projects at the end of each part
- Supplementary material includes presentation slides available to instructors
A General Introduction to Data Analytics is a text for upper level undergraduates or first year graduate students in areas that are using quantitative methods but outside mathematics and computer science.
Joao Moreira is a professor in the Department of Computer Engineering at the University of Porto, Porto, Portugal. He received his Ph.D. from University of Porto. Moreira is winner of the Best Paper Award at the 2014 International Conference on Advanced Data Mining and Applications, Guilin, China.Andre Carvalho is a professor in the Department of Computer Science at the University of Sao Paulo, Brazil. He received his Ph.D. from the University of Kent at Canterbury, United Kingdom. Carvalho is one of the founding and first chief editors of the International Journal of Computational Intelligence and Applications, Imperial College Press and World Scientific.
Tomas Horvath is an assistant professor at Pavol Jozef Safarik University in Kosice, Slovakia. He received his Ph.D. from the Institute of Computer Science in Pavol Jozef Safarik University.
Part I: Introductory Background
Chapter 1: What can we do with data?
Part II: Getting Insights from Data
Chapter 2: Descriptive statistics
Chapter 3: Descriptive Multivariate Analysis
Chapter 4: Data quality and pre-processing
Chapter 5: Clustering
Chapter 6: Frequent pattern mining
Chapter 7: Résumé and project on descriptive analytics
Part III: Predicting the Unknown
Chapter 8: Regression
Chapter 9: Classification
Chapter 10: Additional predictive methods
Chapter 11: Advanced predictive topics
Chapter 12: Résumé and Project on predictive analytics
Part IV: Popular Data Analytics Applications
Chapter 13: Applications for Text, Web and Social Media