This tutorial teaches students everything they need to get started with Python programming for the fast-growing field of data analysis. Daniel Chen tightly links each new concept with easy-to-apply, relevant examples from modern data analysis.
Unlike other beginner's books, this guide helps today's newcomers learn both Python and its popular Pandas data science toolset in the context of tasks they'll really want to perform. Following the proven Software Carpentry approach to teaching programming, Chen introduces each concept with a simple motivating example, slowly offering deeper insights and expanding your ability to handle concrete tasks.
About the Author
Daniel Chen is a graduate student in the interdisciplinary PhD program in Genetics, Bioinformatics & Computational Biology (GBCB) at Virginia Tech. He is involved with Software Carpentry as an instructor and lesson maintainer. He completed his master’s degree in public health at Columbia University Mailman School of Public Health in Epidemiology, and currently works at the Social and Decision Analytics Laboratory under the Biocomplexity Institute of Virginia Tech where he is working with data to inform policy decision-making. He is the author of Pandas for Everyone and Pandas Data Analysis with Python Fundamentals LiveLessons.
Features
Establishes a solid foundation for all the Pandas basics needed to be effective
Covers dataframes, statistical calculations, data munging, modeling, machine learning, reproducible documents, and much more
Teaches step-by-step through easy, incremental examples, with plenty of opportunities to "code along"
Table Content
Part I: Introduction
Part II: Data Manipulation
Part III: Data Munging
Part IV: Data Modeling
Part V: Conclusion
Part VI: Appendixes
Appendix A: Installation
Appendix B: Command Line
Appendix C: Project Templates
Appendix D: Using Python
Appendix E: Working Directories
Appendix F: Environments
Appendix G: Install Packages
Appendix H: Importing Libraries
Appendix I: Lists
Appendix J: Tuples
Appendix K: Dictionaries
Appendix L: Slicing Values
Appendix M: Loops
Appendix N: Comprehensions
Appendix O: Functions
Appendix P: Ranges and Generators
Appendix Q: Multiple Assignment
Appendix R: numpy ndarray
Appendix S: Classes
Appendix T: Odo: The Shapeshifter
Loading...
