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Julia for Data Science
Getting Comfortable with the Basic Structures in Julia
The Course Overview (2:41)
Installing a Julia Working Environment (5:12)
Working with Variables and Basic Types (8:07)
Controlling the Flow (5:17)
Using Functions (8:35)
Using Tuples, Sets, and Dictionaries (5:53)
Working with Matrices for Data Storage and Calculations (8:25)
Diving Deeper into Julia
Using Types and Parameterized Methods (6:42)
Optimizing Your Code by Using and Writing Macros (7:11)
Organizing Your Code in Modules (6:25)
Working with the Package Ecosystem (6:18)
Working with Data in Julia
Reading and Writing Data Files and Julia Data (7:41)
Using DataArrays and DataFrames (7:41)
The Power of DataFrames (6:36)
Interacting with Relational Databases Like SQL Server (7:20)
Interacting with NoSQL Databases Like MongoDB (6:24)
Statistics with Julia
Exploring and Understanding a Dataset Statistically (6:38)
An Overview of the Plotting Techniques in Julia (3:02)
Visualizing Data with Scatterplots, Histograms, and Box Plots (4:24)
Distributions and Hypothesis Testing (5:34)
Interfacing with R (4:24)
Machine Learning Techniques with Julia
Basic Machine Learning Techniques (6:15)
Classification Using Decision Trees and Rules (7:00)
Training and Testing a Decision Tree Model (3:58)
Applying a Generalized Linear Model with GLM (6:17)
Working with Support Vector Machines (7:11)
Using Types and Parameterized Methods
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