Julia Training

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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

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