Last updated 9/2018MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 4.84 GB | Duration: 12h 37m
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 perfog 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 Srgent Analytics. Overview Section 1: Getting Started With Julia Lecture 1 The Course Overview Lecture 2 ing 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 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. 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