Book Image

Julia for Data Science

By : Anshul Joshi
2 (1)
Book Image

Julia for Data Science

2 (1)
By: Anshul Joshi

Overview of this book

Julia is a fast and high performing language that's perfectly suited to data science with a mature package ecosystem and is now feature complete. It is a good tool for a data science practitioner. There was a famous post at Harvard Business Review that Data Scientist is the sexiest job of the 21st century. (https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century). This book will help you get familiarised with Julia's rich ecosystem, which is continuously evolving, allowing you to stay on top of your game. This book contains the essentials of data science and gives a high-level overview of advanced statistics and techniques. You will dive in and will work on generating insights by performing inferential statistics, and will reveal hidden patterns and trends using data mining. This has the practical coverage of statistics and machine learning. You will develop knowledge to build statistical models and machine learning systems in Julia with attractive visualizations. You will then delve into the world of Deep learning in Julia and will understand the framework, Mocha.jl with which you can create artificial neural networks and implement deep learning. This book addresses the challenges of real-world data science problems, including data cleaning, data preparation, inferential statistics, statistical modeling, building high-performance machine learning systems and creating effective visualizations using Julia.
Table of Contents (17 chapters)
Julia for Data Science
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface

What is forecasting?


Let's take the example of an organization that needs to find out the demand for its inventory in the near future, to maximize the return on investment.

For instance, numerous stock frameworks apply for indeterminate demand. The stock parameters in these frameworks require evaluations of the demand and forecast error distributions.

The two phases of these frameworks, forecasting and stock control, are frequently analyzed autonomously. It is essential to comprehend the cooperation between demand estimating and stock control since this impacts the execution of the stock framework.

Forecasting requirements include:

  • Each decision gets to be operational sooner or later, so it ought to be based on figures of future conditions.

  • Figures are required all through an organization and they should absolutely not be created by a disconnected gathering of forecasters.

  • Forecasting is never "wrapped up". Forecasts are required constantly, and as time proceeds onward, the effect of the forecasts...