Julia: Performing Statistical Computations

Mould your programming skills by carrying out dynamic numerical computations with Julia

What you'll learn

Get familiar with the key concepts in Julia

Follow a comprehensive approach to learn Julia programming

Get an extensive coverage of Julia’s packages for statistical analysis

Sharpen your skills to work more effectively with your data

Requirements

The software requirements assume you have any of the following OSes: Linux, Windows, or OS X

There are no specific hardware requirements, except that you run and work all your code on a desktop, or a laptop preferably

Description

Julia is a high-performance dynamic programming language for numerical computing. This practical guide to programming with Julia will help you to work with data more efficiently.
This course begins with the important features of Julia to help you quickly refresh your knowledge of functions, modules, and arrays. We’ll explore utilizing the Julia language to identify, retrieve, and transform datasets so you can perform efficient data analysis and data manipulation.
You will then learn the concepts of metaprogramming and statistics in Julia.
Moving on, you will learn to build data science models by using several algorithms such as dimensionality reduction, linear discriminant analysis, and so on.
You’ll learn 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 course includes sections on identifying and classifying data science problems, data modelling, data analysis, data manipulation, multidimensional arrays, and parallel computing.
By the end of this course, you will acquire the skills to work more effectively with your data.

What am I going to get from this course?
Extract and manage your data efficiently with JuliaExplore the metaprogramming concepts in JuliaPerform statistical analysis with StatsBase.jl and Distributions.jlBuild your data science modelsFind out how to visualize your data with GadflyExplore big data concepts in Julia
What’s special about this course?

We've spent the last decade working to help developers stay relevant. The structure of this course is a result of deep and intensive research into what real-world developers need to know in order to be job-ready. We don't spend too long on theory, and focus on practical results so that you can see for yourself how things work in action.
We have combined the best of the following Packt products:
Julia Cookbook by Jalem Raj RohitJulia Solutions by Jalem Raj Rohit

Meet your expert instructors:

Jalem Raj Rohit is an IIT Jodhpur graduate with a keen interest in machine learning, data science, data analysis, computational statistics, and natural language processing (NLP). Rohit currently works as a senior data scientist at Zomato, also having worked as the first data scientist at Kayako. He is part of the Julia project, where he develops data science models and contributes to the codebase. 

Meet your managing editor:
This course has been planned and designed for you by me, Shiny Poojary. I'm here to help you be successful every step of the way, and get maximum value out of your course purchase. If you have any questions along the way, you can reach out to me and our author group via the instructor contact feature on Udemy.


Overview

Section 1: Getting Started

Lecture 1 Introduction

Lecture 2 Handling data with CSV files

Lecture 3 Handling data with TSV files

Lecture 4 Working with databases in Julia

Lecture 5 Interacting with the Web

Section 2: Metaprogramming

Lecture 6 Representation of a Julia program

Lecture 7 Symbols and expressions

Lecture 8 Quoting

Lecture 9 Interpolation

Lecture 10 The Eval function

Lecture 11 Macros

Lecture 12 Metaprogramming with DataFrames

Section 3: Statistics with Julia

Lecture 13 Basic statistics concepts

Lecture 14 Deviation metrics

Lecture 15 Sampling

Lecture 16 Correlation analysis

Section 4: Building Data Science Models

Lecture 17 Dimensionality reduction

Lecture 18 Linear discriminant analysis

Lecture 19 Data preprocessing

Lecture 20 Linear regression

Lecture 21 Classification

Lecture 22 Performance evaluation and model selection

Lecture 23 Cross validation

Lecture 24 Distributions

Lecture 25 Time series analysis

Section 5: Working with Visualizations

Lecture 26 Plotting basic arrays

Lecture 27 Plotting dataframes

Lecture 28 Plotting functions

Lecture 29 Exploratory data analytics through plots

Lecture 30 Line plots

Lecture 31 Scatter plots

Lecture 32 Histograms

Lecture 33 Aesthetic customizations

Section 6: Parallel Computing

Lecture 34 Basic concepts of parallel computing

Lecture 35 Data movement

Lecture 36 Parallel maps and loop operations

Lecture 37 Channels

This course is for Julia programmers who want to learn data science right from exploratory analytics to the visualization part.,Anyone who wants to work more effectively with data