Julia: From Julia'S Zero To Hero: 2 In 1

Over 40 recipes to solve complex problems with programming using Julia

What you'll learn

Extract and handle your data with Julia

Uncover the concepts of metaprogramming in Julia

Conduct statistical analysis with StatsBase .jl and Distributions .jl

Build your data science models

Explore big data concepts in Julia

Learn to to write high performance Julia code.

Requirements

This Learning Path is designed specifically for data scientists, data analysts or statisticians but is also suitable for any programmer.

Description

Are you looking forward to get well versed with Julia? Then this is the perfect course for you!Julia is a young language with limited documentation and although rapidly growing, a small user community. Most developers today will know the object oriented paradigm used in mainstream languages such as Python, Java and C++. This presents a challenge switching to Julia which is more functionally oriented.With this comprehensive 2-in-1 course takes a practical and incremental approach.  It teaches the fundamentals of Julia to developers with basic knowledge of programming. It is taught in a hands on approach, with simple programming examples the student can try themselves. Building on that, it will invite the user to a tour of the ecosystem of Julia through practical code examples.By end of this course you will more productive and acquire all the skills to work with data more efficiently. Also help you quickly refresh your knowledge of functions, modules, and arrays & shows you how to utilize the Julia language to identify, retrieve, and transform data sets so you can perform data analysis and data manipulation & also get familiar with the concepts of package development and networking to solve numerical problems using the Julia platform.Contents and OverviewThis training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Getting Started With Julia covers complete INSTALLATION AND SETUP along with basic of Julia. This course will not only introduce the language, but also explain how to think differently about problems with the Julia approach.  This course also focuses various aspects such as Functional Programming in Julia, Metaprogramming, Debugging and Testing & much more.The second course, Julia Solutions covers consist complete guide to programming with Julia for performing numerical computation will make you more productive and able to work with data more efficiently. The course starts with the main features of Julia to help you quickly refresh your knowledge of functions, modules, and arrays. We’ll also show you how to utilize the Julia language to identify, retrieve, and transform data sets so you can perform data analysis and data manipulation. Later on, you’ll see how to optimize data science programs with parallel computing and memory allocation. You’ll get familiar with the concepts of package development and networking to solve numerical problems using the Julia platform. This course also includes videos on identifying and classifying data science problems, data modelling, data analysis, data manipulation, meta-programming, multidimensional arrays, and parallel computing. By the end of the course, you will acquire the skills to work more effectively with your data.About the Authors:Erik Engheim is a professional mobile developer with experience in many different programming languages, often in combination. Erik Engheim has worked with C/C#, Java, C++, Objective-C, and Swift before moving into Julia. His experience with Julia involves automation, and high performance processing of code strings.Jalem Raj Rohit is an IIT Jodhpur graduate with a keen interest in machine learning, data science, data analysis, computational statistics, and natural language processing (NLP). Rohit currently works as a senior data scientist at Zomato, also having worked as the first data scientist at Kayako.He is part of the Julia project, where he develops data science models and contributes to the codebase. Additionally, Raj is also a Mozilla contributor and volunteer, and he has interned at Scimergent Analytics.

Overview

Section 1: Getting Started With Julia

Lecture 1 The Course Overview

Lecture 2 Downloading Julia

Lecture 3 Setting up an Editor

Lecture 4 Using the Julia REPL

Lecture 5 Numbers

Lecture 6 Strings

Lecture 7 Arrays

Lecture 8 Control Flow

Lecture 9 Functions

Lecture 10 Variables

Lecture 11 Dictionaries

Lecture 12 Practical Usage of Functions

Lecture 13 Inspecting Types

Lecture 14 Type Hierarchies and Multiple Dispatch

Lecture 15 Conversion and Promotion

Lecture 16 Defining Your Own Types

Lecture 17 Reading and Writing to Files

Lecture 18 Networking

Lecture 19 Dealing with Different File Formats

Lecture 20 Using Modules

Lecture 21 Networking

Lecture 22 Reading and Writing CSV Files

Lecture 23 Interfaces

Lecture 24 Maze Builder

Lecture 25 Graphics Editor

Lecture 26 Implementation Inheritance

Lecture 27 Higher Order Functions

Lecture 28 Function Composition

Lecture 29 Functional Approach

Lecture 30 Functional Interpreter Pattern

Lecture 31 Common Traits

Lecture 32 Collection Types

Lecture 33 Multidimensional Arrays

Lecture 34 Sets

Lecture 35 Introducing Type Unions

Lecture 36 Code Reuse Through Type Unions

Lecture 37 Why Parametric Types?

Lecture 38 Creating a Generic Collection

Lecture 39 Pitfalls

Lecture 40 Nullable

Lecture 41 Debugging Approaches

Lecture 42 Writing Debuggable Code

Lecture 43 Writing Tests

Lecture 44 Program Representation

Lecture 45 Macros

Lecture 46 Code Generation

Lecture 47 Compilation

Lecture 48 Abstract Versus Concrete Types

Lecture 49 Type Stability

Section 2: Julia Solutions

Lecture 50 The Course Overview

Lecture 51 Handling Data with CSV Files

Lecture 52 Handling Data with TSV Files

Lecture 53 Interacting with the Web

Lecture 54 Representation of a Julia Program

Lecture 55 Symbols

Lecture 56 Quoting

Lecture 57 Interpolation

Lecture 58 The eval Function

Lecture 59 Macros

Lecture 60 Metaprogramming with DataFrames

Lecture 61 Basic Statistics Concepts

Lecture 62 Descriptive Statistics

Lecture 63 Deviation Metrics

Lecture 64 Sampling

Lecture 65 Correlation Analysis

Lecture 66 Dimensionality Reduction

Lecture 67 Data Preprocessing

Lecture 68 Linear Regression

Lecture 69 Classification

Lecture 70 Performance Evaluation and Model Selection

Lecture 71 Cross Validation

Lecture 72 Distances

Lecture 73 Distributions

Lecture 74 Time Series Analysis

Lecture 75 Plotting Basic Arrays

Lecture 76 Plotting DataFrames

Lecture 77 Plotting Functions

Lecture 78 Exploratory Data Analytics Through Plots

Lecture 79 Line Plots

Lecture 80 Scatter Plots

Lecture 81 Histograms

Lecture 82 Aesthetic Customizations

Lecture 83 Basic Concepts of Parallel Computing

Lecture 84 Data Movement

Lecture 85 Parallel Maps and Loop Operations

Lecture 86 Channels

This Learning Path is designed specifically for data scientists, data analysts or statisticians but is also suitable for any programmer who is new to the field of data science, or anyone aspiring to get into the field of data science and choses Julia as the tool to do so.