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!
Julia, Data Science training table of contents (duration : 2h41m)
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Getting Comfortable with the Basic Structures in Julia
- The Course Overview free 00:02:41
- Installing a Julia Working Environment 00:05:13
- Working with Variables and Basic Types 00:08:07
- Controlling the Flow 00:05:18
- Using Functions 00:08:36
- Using Tuples, Sets, and Dictionaries 00:05:54
- Working with Matrices for Data Storage and Calculations 00:08:25
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Diving Deeper into Julia
- Using Types and Parameterized Methods 00:06:43
- Optimizing Your Code by Using and Writing Macros 00:07:11
- Organizing Your Code in Modules 00:06:26
- Working with the Package Ecosystem 00:06:19
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Working with Data in Julia
- Reading and Writing Data Files and Julia Data free 00:07:41
- Using DataArrays and DataFrames 00:07:41
- The Power of DataFrames 00:06:36
- Interacting with Relational Databases Like SQL Server 00:07:21
- Interacting with NoSQL Databases Like MongoDB 00:06:24
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Statistics with Julia
- Exploring and Understanding a Dataset Statistically 00:06:38
- An Overview of the Plotting Techniques in Julia 00:03:02
- Visualizing Data with Scatterplots, Histograms, and Box Plots 00:04:24
- Distributions and Hypothesis Testing 00:05:35
- Interfacing with R 00:04:25
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Machine Learning Techniques with Julia
- Basic Machine Learning Techniques 00:06:15
- Classification Using Decision Trees and Rules 00:07:01
- Training and Testing a Decision Tree Model 00:03:58
- Applying a Generalized Linear Model with GLM 00:06:17
- Working with Support Vector Machines 00:07:11
- Sources Filesdownload
- Certificate
Instructor : Packt
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With which software version is this tutorial compatible with?Julia
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What is the required level to follow this tutorial ?intermediate
