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Julia for Data Science
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
An Overview of the Plotting Techniques in Julia
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