Introduction to Julia Training Course
Course
In City Of London
Description
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Type
Course
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Location
City of london
This course is oriented towards data analysts as well as research scientists. Julia is a rapidly emerging programming language with a strong focus on numerical accuracy, scientific computing and statistics. It has gained most of its reputation due to its speed of execution in conjunction with its ease of programming. What is less emphasized, although it is true, is that
Julia has a wealth of built-in and external tools for distributed and parallel computing,
it facilitates the construction of user-defined data structures,
it makes it easy to do metaprogramming, therefore to also define your ownl DSLs,
it allows interacting with several other programming languages such as C, Python and R,
it provides a multiple-dispatch programming paradigm, which in many ways helps you organize your code and makes you a better programmer and software engineer.
Facilities
Location
Start date
Start date
Reviews
Subjects
- Evaluation
- Object oriented training
- Computing
- Testing
- Data analysis
- Statistics
- Writing
- Programming
- Oriented Programming
- Construction Training
Course programme
Introduction to Julia
- What niche is filled by Julia
- How can Julia help you with data analysis
- What you can expect to get out of this course
- 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
- Introduction to Julia REPL and batch execution via "Hello World"
- Julia String Types
- What is a variable? Why do we use a name and a type for it?
- Integers
- Floating point numbers
- Complex numbers
- Rational numbers
- Vectors
- Matrices
- Multi-dimensional arrays
- Heterogeneous arrays (cell arrays)
- Comprehensions
- Tuples
- Ranges
- Dictionaries
- Symbols
- Abstract types
- Composite types
- Parametric composite types
- 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
- Inner constructors
- Outer constructors
- Compound expressions and scoping
- Conditional evaluation
- Loops
- Exception Handling
- Tasks
- Modules
- Packages
- Symbols
- Expressions
- Quoting
- Internal representation
- Parsing
- Evaluation
- Interpolation
- Filesystem
- Data I/O
- Lower Level Data I/O
- Dataframes
- Defining distributions
- Interface for evaluating and sampling from distributions
- Mean, variance and covariance
- Hypothesis testing
- Generalized linear models: a linear regression example
- Plotting packages: Gadfly, Winston, Gaston, PyPlot, Plotly, Vega
- Introduction to Gadfly
- Interact and Gadfly
- Introduction to Julia's message passing implementation
- Remote calling and fetching
- Parallel map (pmap)
- Parallel for
- Scheduling via tasks
- Distributed arrays
Introduction to Julia Training Course