Julia- From Julia Zero to Hero- 2 in 1

Julia: From Julias Zero to Hero: 2 in 1, available at $39.99, has an average rating of 3.7, with 86 lectures, 2 quizzes, based on 55 reviews, and has 393 subscribers.

You will learn about 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. This course is ideal for individuals who are 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. It is particularly useful for 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.

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Summary

Title: Julia: From Julias Zero to Hero: 2 in 1

Price: $39.99

Average Rating: 3.7

Number of Lectures: 86

Number of Quizzes: 2

Number of Published Lectures: 86

Number of Published Quizzes: 2

Number of Curriculum Items: 88

Number of Published Curriculum Objects: 88

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

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.

Target Audiences

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.

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 Overview

This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Getting Started With Juliacovers 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 Solutionscovers 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.

Course Curriculum

Chapter 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

Chapter 2: Julia Solutions

Lecture 1: The Course Overview

Lecture 2: Handling Data with CSV Files

Lecture 3: Handling Data with TSV Files

Lecture 4: Interacting with the Web

Lecture 5: Representation of a Julia Program

Lecture 6: Symbols

Lecture 7: Quoting

Lecture 8: Interpolation

Lecture 9: The eval Function

Lecture 10: Macros

Lecture 11: Metaprogramming with DataFrames

Lecture 12: Basic Statistics Concepts

Lecture 13: Descriptive Statistics

Lecture 14: Deviation Metrics

Lecture 15: Sampling

Lecture 16: Correlation Analysis

Lecture 17: Dimensionality Reduction

Lecture 18: Data Preprocessing

Lecture 19: Linear Regression

Lecture 20: Classification

Lecture 21: Performance Evaluation and Model Selection

Lecture 22: Cross Validation

Lecture 23: Distances

Lecture 24: Distributions

Lecture 25: Time Series Analysis

Lecture 26: Plotting Basic Arrays

Lecture 27: Plotting DataFrames

Lecture 28: Plotting Functions

Lecture 29: Exploratory Data Analytics Through Plots

Lecture 30: Line Plots

Lecture 31: Scatter Plots

Lecture 32: Histograms

Lecture 33: Aesthetic Customizations

Lecture 34: Basic Concepts of Parallel Computing

Lecture 35: Data Movement

Lecture 36: Parallel Maps and Loop Operations

Lecture 37: Channels

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 4 votes

2 stars: 10 votes

3 stars: 14 votes

4 stars: 13 votes

5 stars: 14 votes

Frequently Asked Questions

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