Overview of this book

Want to handle everything that Julia can throw at you and get the most of it every day? This practical guide to programming with Julia for performing numerical computation will make you more productive and able work with data more efficiently. The book starts with the main features of Julia to help you quickly refresh your knowledge of functions, modules, and arrays. We’ll also show you how to utilize the Julia language to identify, retrieve, and transform data sets so you can perform data analysis and data manipulation. Later on, you’ll see how to optimize data science programs with parallel computing and memory allocation. You’ll get familiar with the concepts of package development and networking to solve numerical problems using the Julia platform. This book includes recipes on identifying and classifying data science problems, data modelling, data analysis, data manipulation, meta-programming, multidimensional arrays, and parallel computing. By the end of the book, you will acquire the skills to work more effectively with your data.
Julia Cookbook
Credits
www.PacktPub.com
Preface
Free Chapter
Extracting and Handling Data
Metaprogramming
Statistics with Julia
Building Data Science Models
Working with Visualizations
Parallel Computing

Descriptive statistics

Descriptive statistics is the discipline of statistics, where information and features, which explain the essence of data, are extracted and analyzed. This part is very important, as it helps us estimate the shape and features of data for model and algorithm selection.

You have to have the `StatsBase` package ready. This can be done by running `using StatsBase` in the REPL.

How to do it...

1. The variance of a vector can be found using the `var()` function. This can be done by the following:

```var(x)
```

The output would look like the following:

2. For calculating the weighted variance of the vector x with respect to weight vector w, both of them can be simply added to the `variance()` function as arguments:

3. For calculating the standard deviation, the `std()` function can be used. This can be done by executing the following in the REPL:

```std(x)
```

The output would look like the following:

4. As with the calculation of the preceding variance, the weighted value of standard deviation...