The Art and Science of Analyzing Software Data
Nonfiction, Computers, Database Management, Data Processing, Programming, Software Development, General Computing
| Author: | ISBN: | 9780124115439 | |
| Publisher: | Elsevier Science | Publication: | September 2, 2015 |
| Imprint: | Morgan Kaufmann | Language: | English |
| Author: | |
| ISBN: | 9780124115439 |
| Publisher: | Elsevier Science |
| Publication: | September 2, 2015 |
| Imprint: | Morgan Kaufmann |
| Language: | English |
The Art and Science of Analyzing Software Data provides valuable information on analysis techniques often used to derive insight from software data. This book shares best practices in the field generated by leading data scientists, collected from their experience training software engineering students and practitioners to master data science.
The book covers topics such as the analysis of security data, code reviews, app stores, log files, and user telemetry, among others. It covers a wide variety of techniques such as co-change analysis, text analysis, topic analysis, and concept analysis, as well as advanced topics such as release planning and generation of source code comments. It includes stories from the trenches from expert data scientists illustrating how to apply data analysis in industry and open source, present results to stakeholders, and drive decisions.
- Presents best practices, hints, and tips to analyze data and apply tools in data science projects
- Presents research methods and case studies that have emerged over the past few years to further understanding of software data
- Shares stories from the trenches of successful data science initiatives in industry
The Art and Science of Analyzing Software Data provides valuable information on analysis techniques often used to derive insight from software data. This book shares best practices in the field generated by leading data scientists, collected from their experience training software engineering students and practitioners to master data science.
The book covers topics such as the analysis of security data, code reviews, app stores, log files, and user telemetry, among others. It covers a wide variety of techniques such as co-change analysis, text analysis, topic analysis, and concept analysis, as well as advanced topics such as release planning and generation of source code comments. It includes stories from the trenches from expert data scientists illustrating how to apply data analysis in industry and open source, present results to stakeholders, and drive decisions.
- Presents best practices, hints, and tips to analyze data and apply tools in data science projects
- Presents research methods and case studies that have emerged over the past few years to further understanding of software data
- Shares stories from the trenches of successful data science initiatives in industry
