Julia Course Overview
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. Julia has a wealth of built-in and external tools for distributed and parallel computing. It facilitates the construction of user-defined data structures, and makes it easy to do meta programming, therefore it also define your own DSLs and it allows interacting with several other programming languages such as C, Python and R. Julia provides a multiple-dispatch programming paradigm, which in many ways helps you organize your code and makes you a better programmer and software engineer.
Julia Training Curriculum
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
Strings: Hello World
Introduction to Julia REPL and batch execution via “Hello World”, Julia String Types
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
Arrays
Vectors, Matrices, Multi-dimensional arrays, Heterogeneous arrays (cell arrays), Comprehensions
Other Elementary Types
Tuples, Ranges, Dictionaries, Symbols
Building Your Own Types
Abstract types, Composite types, Parametric composite types
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
Constructors
Inner constructors, Outer constructors
Control Flow
Compound expressions and scoping, Conditional evaluation, Loops, Exception Handling, Tasks
Code Organization
Modules, Packages
Metaprogramming
Symbols, Expressions, Quoting, Internal representation, Parsing, Evaluation, Interpolation
Reading and Writing Data
Filesystem, Data I/O, Lower Level Data I/O, Dataframes
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
Plotting
Plotting packages: Gadfly, Winston, Gaston, PyPlot, Plotly, Vega, Introduction to Gadfly, Interact and Gadfly
Parallel Computing
Introduction to Julia’s message passing implementation, Remote calling and fetching, Parallel map (pmap), Parallel for, Scheduling via tasks, Distributed arrays
|
| Course Duration |
30 hrs |
| Live Projects |
2 |
| Next Batch |
28 Nov, 2019
01 Dec, 2019
05 Dec, 2019 |
