After covering the importance of Julia to the data science community and several essential data science principles, we start with the basics including how to install Julia and its powerful libraries. Many examples are provided as we illustrate how to leverage each Julia command, dataset, and function.
Specialized script packages are introduced and described. Hands-on problems representative of those commonly encountered throughout the data science pipeline are provided, and we guide you in the use of Julia in solving them using published datasets. Many of these scenarios make use of existing packages and built-in functions, as we cover:
- An overview of the data science pipeline along with an example illustrating the key points, implemented in Julia
- Options for Julia IDEs
- Programming structures and functions
- Engineering tasks, such as importing, cleaning, formatting and storing data, as well as performing data preprocessing
- Data visualization and some simple yet powerful statistics for data exploration purposes
- Dimensionality reduction and feature evaluation
- Machine learning methods, ranging from unsupervised (different types of clustering) to supervised ones (decision trees, random forests, basic neural networks, regression trees, and Extreme Learning Machines)
- Graph analysis including pinpointing the connections among the various entities and how they can be mined for useful insights.
Each chapter concludes with a series of questions and exercises to reinforce what you learned. The last chapter of the book will guide you in creating a data science application from scratch using Julia.
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Table of Contents
Introduction 1 CHAPTER 1: Introducing Julia 3 CHAPTER 2: Setting Up the Data Science Lab 13 CHAPTER 3: Learning the Ropes of Julia 49 CHAPTER 4: Going Beyond the Basics in Julia 81 CHAPTER 5: Julia Goes All Data Science-y 101 CHAPTER 6: Julia the Data Engineer 125 CHAPTER 7: Exploring Datasets 153 CHAPTER 8: Manipulating the Fabric of the Data Space 187 CHAPTER 9: Sampling Data and Evaluating Results 205 CHAPTER 10: Unsupervised Machine Learning 227 CHAPTER 11: Supervised Machine Learning 247 CHAPTER 12: Graph Analysis 281 CHAPTER 13: Reaching the Next Level 305 APPENDIX A: Downloading and Installing Julia and IJulia 315 APPENDIX B: Useful Websites Related to Julia 317 APPENDIX C: Packages Used in This Book 319 APPENDIX D: Bridging Julia with Other Platforms 321 APPENDIX E: Parallelization in Julia 325