Julia Online Training
0 STUDENTS ENROLLED
Introduction:
Julia Training briefs the basics of the Julia programming language with a strong focus on numerical accuracy, scientific computing and statistics. Julia programs are organized around the multiple dispatch; by defining functions and overloading them for different argument types, which can also be user-defined.
Course Content:
1.Introduction To Julia
- What niche is filled by Julia
- How can Julia help you with data analysis
- Getting started with Julia’s REPL
- Alternative environments for Julia development: Juno, IJulia and Sublime-IJulia
- The Julia ecosystem: documentation and package search
- Getting more help: Julia forums and Julia community
2.Strings: Hello World
- Introduction to Julia REPL and batch execution via “Hello World”
- Julia String Types
3.Scalar Types
- What is a variable? Why do we use a name and a type for it?
- Integers
- Floating point numbers
- Complex numbers
- Rational numbers
4.Arrays
- Vectors
- Matrices
- Multi-dimensional arrays
- Heterogeneous arrays (cell arrays)
- Comprehensions
5.Other Elementary Types
- Tuples
- Ranges
- Dictionaries
- Symbols
6.Building Your Own Types
- Abstract types
- Composite types
- Parametric composite types
7.Functions
- How to define a function in Julia
- Julia functions as methods operating on types
- Multiple dispatch
- How multiple dispatch differs from traditional object-oriented programming
- Parametric functions
- Functions changing their input
- Anonymous functions
- Optional function arguments
- Required function arguments
8.Constructors
- Inner constructors
- Outer constructors
9.Control Flow
- Compound expressions and scoping
- Conditional evaluation
- Loops
- Exception Handling
- Tasks
10.Code Organization
- Modules
- Packages
11.Metaprogramming
- Symbols
- Expressions
- Quoting
- Internal representation
- Parsing
- Evaluation
- Interpolation
12.Reading And Writing Data
- Filesystem
- Data I/O
- Lower Level Data I/O
- Dataframes
13.Distributions And Statistics
- Defining distributions
- Interface for evaluating and sampling from distributions
- Mean, variance and co variance
- Hypothesis testing
- Generalized linear models: a linear regression example
14.Plotting
- Plotting packages: Gadfly, Winston, Gaston, PyPlot, Plotly, Vega
- Introduction to Gadfly
- Interact and Gadfly
15.Parallel Computing
- Introduction to Julia’s message passing implementation
- Remote calling and fetching
- Parallel map (pmap)
- Parallel for
- Scheduling via tasks
- Distributed arrays
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