Julia for Data Science

Learn to use the machine learning algorithms in Julia to make better decisions and smarter actions in real time without human intervention Get to grips with the specialized packages in Julia and leverage its performance capabilities to create efficient programs Create your own modules and contribute to the Julia package system Learning Get to grips with the basic data structures in Julia and learn about different development environments Organize your code by writing Lisp-style macros and using modules Manage, analyze, and work in depth with statistical data sets using the powerful DataFrames package Perform statistical computations on data from different sources and visualize those using plotting packages Apply different algorithms from decision trees and other packages to extract meaningful information from the iris dataset Gain some valuable insights into interfacing Julia with an R application About Julia is an easy, fast, open source language that if written well performs nearly as well as low-level languages such as C and FORTRAN. Its design is a dance between specialization and abstraction, providing high machine performance without the sacrifice of human convenience. Julia is a fresh approach to technical computing, combining expertise from diverse fields of computational and computer science. This video course walks you through all the steps involved in applying the Julia ecosystem to your own data science projects. We start with the basics and show you how to design and implement some of the general purpose features of Julia. Is fast development and fast execution possible at the same time? Julia provides the best of both worlds with its wide range of types, and our course covers this in depth. You will have organized and readable code by the end of the course by learning how to write Lisp style macros and modules. The course demonstrates the power of the DataFrames package to manage, organize, and analyze data. It enables you to work with data from various sources, perform statistical calculations on them, and visualize their relationships in different kinds of plots through live demonstrations. Julia for Data Science takes you from zero to hero, leaving you with the know-how required to apply Style and approach This course provides in-depth content balanced with functional tutorials that put theory into practice. The focus of this course is to give you both a technical understanding and the practical experience that will allow you to use Julia for data science projects. Getting Comfortable with the Basic Structures in Julia The Course Overview Installing a Julia Working Environment Working with Variables and Basic Types Controlling the Flow Using Functions Using Tuples, Sets, and Dictionaries Working with Matrices for Data Storage and Calculations Diving Deeper into Julia Using Types and Parameterized Methods Optimizing Your Code by Using and Writing Macros Organizing Your Code in Modules Working with the Package Ecosystem Working with Data in Julia Reading and Writing Data Files and Julia Data Using DataArrays and DataFrames The Power of DataFrames Interacting with Relational Databases Like SQL Server Interacting with NoSQL Databases Like MongoDB Statistics with Julia Exploring and Understanding a Dataset Statistically An Overview of the Plotting Techniques in Julia Visualizing Data with Scatterplots, Histograms, and Box Plots Distributions and Hypothesis Testing Interfacing with R Machine Learning Techniques with Julia Basic Machine Learning Techniques Classification Using Decision Trees and Rules Training and Testing a Decision Tree Model Applying a Generalized Linear Model with GLM Working with Support Vector Machines