Julia for Data Science
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VIDEO TUTORIAL Julia for Data Science

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Refine your data science skills with the heavy armory of tools provided by Julia

About This Julia for Data Science Video course

  • 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

Julia Data Science In Detail

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!

What will you learn in this course?

Course plan
Chapter 1
Getting Comfortable with the Basic Structures in Julia
Chapter 2
Diving Deeper into Julia
Chapter 3
Working with Data in Julia
Chapter 4
Statistics with Julia
Chapter 5
Machine Learning Techniques with Julia

Detailed course plan

Chapter 1 : Getting Comfortable with the Basic Structures in Julia
Lesson 1The Course Overview
Lesson 2Installing a Julia Working Environment
Lesson 3Working with Variables and Basic Types
Lesson 4Controlling the Flow
Lesson 5Using Functions
Lesson 6Using Tuples, Sets, and Dictionaries
Lesson 7Working with Matrices for Data Storage and Calculations
Chapter 2 : Diving Deeper into Julia
Lesson 1Using Types and Parameterized Methods
Lesson 2Optimizing Your Code by Using and Writing Macros
Lesson 3Organizing Your Code in Modules
Lesson 4Working with the Package Ecosystem
Chapter 3 : Working with Data in Julia
Lesson 1Reading and Writing Data Files and Julia Data
Lesson 2Using DataArrays and DataFrames
Lesson 3The Power of DataFrames
Lesson 4Interacting with Relational Databases Like SQL Server
Lesson 5Interacting with NoSQL Databases Like MongoDB
Chapter 4 : Statistics with Julia
Lesson 1Exploring and Understanding a Dataset Statistically
Lesson 2An Overview of the Plotting Techniques in Julia
Lesson 3Visualizing Data with Scatterplots, Histograms, and Box Plots
Lesson 4Distributions and Hypothesis Testing
Lesson 5Interfacing with R
Chapter 5 : Machine Learning Techniques with Julia
Lesson 1Basic Machine Learning Techniques
Lesson 2Classification Using Decision Trees and Rules
Lesson 3Training and Testing a Decision Tree Model
Lesson 4Applying a Generalized Linear Model with GLM
Lesson 5Working with Support Vector Machines

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