Covers multivariate analysis and computational techniques for data analytics using Python
Provides a step-by-step practical approach to learning using 100 tutorials and 50 worked-out exercises
Is useful for programmers, statisticians, and practicing data analytics application professionals
Dr. Srikrishnan Sundararajan Ph.D. in Computer Applications, is a retired senior professor of business analytics, Loyola institute of business administration, Chennai, India. He has held various tenured and visiting professorships in business analytics, and computer science for over 10 years, which includes institutions such as Kerala University of Digital Sciences, Innovation and Technology; LM Thapar School of Management; Agni College of Technology; and SCMS-Cochin. He has 25 years of experience as a consultant in the information technology industry in India and the USA, in information systems development and technology support. As an IT consultant, he has guided multi-cultural teams working from the USA, UK as well as India. He has worked with Tata Consultancy Services, Covansys Inc. USA, UST Global, and HCL Technologies Ltd., where he has contributed to software application development and the centre of excellence for technology.
This book offers a comprehensive first-level introduction to data analytics. The book covers multivariate analysis, AI / ML, and other computational techniques for solving data analytics problems using Python. The topics covered include (a) a working introduction to programming with Python for data analytics, (b) an overview of statistical techniques – probability and statistics, hypothesis testing, correlation and regression, factor analysis, classification (logistic regression, linear discriminant analysis, decision tree, support vector machines, and other methods), various clustering techniques, and survival analysis, (c) introduction to general computational techniques such as market basket analysis, and social network analysis, and (d) machine learning and deep learning.
Many academic textbooks are available for teaching statistical applications using R, SAS, and SPSS. However, there is a dearth of textbooks that provide a comprehensive introduction to the emerging and powerful Python ecosystem, which is pervasive in data science and machine learning applications.
The book offers a judicious mix of theory and practice, reinforced by over 100 tutorials coded in the Python programming language. The book provides worked-out examples that conceptualize real-world problems using data curated from public domain datasets. It is designed to benefit any data science aspirant, who has a basic (higher secondary school level) understanding of programming and statistics. The book may be used by analytics students for courses on statistics, multivariate analysis, machine learning, deep learning, data mining, and business analytics. It can be also used as a reference book by data analytics professionals.
Chapter 1: Introduction.- Chapter 2: Python for Data Analytics – A Quick Tour.- Chapter 3: Probability.- Chapter 4: Statistical Concepts.- Chapter 5: Correlation and Regression.- Chapter 6: Classification.- Chapter 7: Factor Analysis.- Chapter 8: Cluster Analysis.- Chapter 9: Survival Analysis.- Chapter 10: Computational Techniques.- Chapter 11: Machine Learning.
This book offers a comprehensive first-level introduction to data analytics. The book covers multivariate analysis, AI / ML, and other computational techniques for solving data analytics problems using Python. The topics covered include (a) a working introduction to programming with Python for data analytics, (b) an overview of statistical techniques – probability and statistics, hypothesis testing, correlation and regression, factor analysis, classification (logistic regression, linear discriminant analysis, decision tree, support vector machines, and other methods), various clustering techniques, and survival analysis, (c) introduction to general computational techniques such as market basket analysis, and social network analysis, and (d) machine learning and deep learning.
Many academic textbooks are available for teaching statistical applications using R, SAS, and SPSS. However, there is a dearth of textbooks that provide a comprehensive introduction to the emerging and powerful Python ecosystem, which is pervasive in data science and machine learning applications.
The book offers a judicious mix of theory and practice, reinforced by over 100 tutorials coded in the Python programming language. The book provides worked-out examples that conceptualize real-world problems using data curated from public domain datasets. It is designed to benefit any data science aspirant, who has a basic (higher secondary school level) understanding of programming and statistics. The book may be used by analytics students for courses on statistics, multivariate analysis, machine learning, deep learning, data mining, and business analytics. It can be also used as a reference book by data analytics professionals.
This book offers a comprehensive first-level introduction to data analytics. The book covers multivariate analysis, AI / ML, and other computational techniques for solving data analytics problems using Python. The topics covered include (a) a working introduction to programming with Python for data analytics, (b) an overview of statistical techniques - probability and statistics, hypothesis testing, correlation and regression, factor analysis, classification (logistic regression, linear discriminant analysis, decision tree, support vector machines, and other methods), various clustering techniques, and survival analysis, (c) introduction to general computational techniques such as market basket analysis, and social network analysis, and (d) machine learning and deep learning. Many academic textbooks are available for teaching statistical applications using R, SAS, and SPSS. However, there is a dearth of textbooks that provide a comprehensiveintroduction to the emerging and powerful Python ecosystem, which is pervasive in data science and machine learning applications.
The book offers a judicious mix of theory and practice, reinforced by over 100 tutorials coded in the Python programming language. The book provides worked-out examples that conceptualize real-world problems using data curated from public domain datasets. It is designed to benefit any data science aspirant, who has a basic (higher secondary school level) understanding of programming and statistics. The book may be used by analytics students for courses on statistics, multivariate analysis, machine learning, deep learning, data mining, and business analytics. It can be also used as a reference book by data analytics professionals.
Chapter 1: Introduction.- Chapter 2: Python for Data Analytics - A Quick Tour.- Chapter 3: Probability.- Chapter 4: Statistical Concepts.- Chapter 5: Correlation and Regression.- Chapter 6: Classification.- Chapter 7: Factor Analysis.- Chapter 8: Cluster Analysis.- Chapter 9: Survival Analysis.- Chapter 10: Computational Techniques.- Chapter 11: Machine Learning.
Über den Autor
Dr. Srikrishnan Sundararajan Ph.D. in Computer Applications, is a retired senior professor of business analytics, Loyola institute of business administration, Chennai, India. He has held various tenured and visiting professorships in business analytics, and computer science for over 10 years, which includes institutions such as Kerala University of Digital Sciences, Innovation and Technology; LM Thapar School of Management; Agni College of Technology; and SCMS-Cochin. He has 25 years of experience as a consultant in the information technology industry in India and the USA, in information systems development and technology support. As an IT consultant, he has guided multi-cultural teams working from the USA, UK as well as India. He has worked with Tata Consultancy Services, Covansys Inc. USA, UST Global, and HCL Technologies Ltd., where he has contributed to software application development and the centre of excellence for technology.
Inhaltsverzeichnis
Chapter 1: Introduction.- Chapter 2: Python for Data Analytics - A Quick Tour.- Chapter 3: Probability.- Chapter 4: Statistical Concepts.- Chapter 5: Correlation and Regression.- Chapter 6: Classification.- Chapter 7: Factor Analysis.- Chapter 8: Cluster Analysis.- Chapter 9: Survival Analysis.- Chapter 10: Computational Techniques.- Chapter 11: Machine Learning.
Klappentext
This book offers a comprehensive first-level introduction to data analytics. The book covers multivariate analysis, AI / ML, and other computational techniques for solving data analytics problems using Python. The topics covered include (a) a working introduction to programming with Python for data analytics, (b) an overview of statistical techniques ¿ probability and statistics, hypothesis testing, correlation and regression, factor analysis, classification (logistic regression, linear discriminant analysis, decision tree, support vector machines, and other methods), various clustering techniques, and survival analysis, (c) introduction to general computational techniques such as market basket analysis, and social network analysis, and (d) machine learning and deep learning.
Many academic textbooks are available for teaching statistical applications using R, SAS, and SPSS. However, there is a dearth of textbooks that provide a comprehensive introduction to the emerging and powerful Python ecosystem, which is pervasive in data science and machine learning applications.
The book offers a judicious mix of theory and practice, reinforced by over 100 tutorials coded in the Python programming language. The book provides worked-out examples that conceptualize real-world problems using data curated from public domain datasets. It is designed to benefit any data science aspirant, who has a basic (higher secondary school level) understanding of programming and statistics. The book may be used by analytics students for courses on statistics, multivariate analysis, machine learning, deep learning, data mining, and business analytics. It can be also used as a reference book by data analytics professionals.
Covers multivariate analysis and computational techniques for data analytics using Python
Provides a step-by-step practical approach to learning using 100 tutorials and 50 worked-out exercises
Is useful for programmers, statisticians, and practicing data analytics application professionals
