Last updated 5/2017MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 173.41 MB | Duration: 2h 27m
Mould your programming skills by carrying out dynamic numerical computations with Julia What you'll learn Get familiar with the key concepts in Julia Follow a comprehensive approach to learn Julia programming Get an extensive coverage of Julia’s packages for statistical analysis Sharpen your skills to work more effectively with your data Requirements The software requirements assume you have any of the following OSes: Linux, Windows, or OS X There are no specific hardware requirements, except that you run and work all your code on a desktop, or a laptop preferably Description Julia is a high-performance dynamic programming language for numerical computing. This practical guide to programming with Julia will help you to work with data more efficiently. This course bs with the important features of Julia to help you quickly refresh your knowledge of functions, modules, and arrays. We’ll explore utilizing the Julia language to identify, retrieve, and transform datasets so you can perform efficient data analysis and data manipulation. You will then learn the concepts of metaprogramming and statistics in Julia. Moving on, you will learn to build data science models by using several algorithms such as dimensionality reduction, linear discriminant analysis, and so on. You’ll learn 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 includes sections on identifying and classifying data science problems, data modelling, data analysis, data manipulation, multidimensional arrays, and parallel computing. By the end of this course, you will acquire the skills to work more effectively with your data. What am I going to get from this course? Extract and manage your data efficiently with JuliaExplore the metaprogramming concepts in JuliaPerform statistical analysis with StatsBase.jl and Distributions.jlBuild your data science modelsFind out how to visualize your data with GadflyExplore big data concepts in Julia What’s special about this course? We've spent the last decade working to help developers stay relevant. The structure of this course is a result of deep and intensive research into what real-world developers need to know in order to be job-ready. We don't spend too long on theory, and focus on practical results so that you can see for yourself how things work in action. We have combined the best of the following Packt products Julia Cookbook by Jalem Raj RohitJulia Solutions by Jalem Raj Rohit Meet your expert instructors 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. Meet your managing editor This course has been planned and designed for you by me, Shiny Poojary. I'm here to help you be successful every step of the way, and get maximum value out of your course purchase. If you have any questions along the way, you can reach out to me and our author group via the instructor contact feature on Udemy. Overview Section 1: Getting Started Lecture 1 Introduction Lecture 2 Handling data with CSV files Lecture 3 Handling data with TSV files Lecture 4 Working with databases in Julia Lecture 5 Interacting with the Web Section 2: Metaprogramming Lecture 6 Representation of a Julia program Lecture 7 Symbols and expressions Lecture 8 Quoting Lecture 9 Interpolation Lecture 10 The Eval function Lecture 11 Macros Lecture 12 Metaprogramming with DataFrames Section 3: Statistics with Julia Lecture 13 Basic statistics concepts Lecture 14 Deviation metrics Lecture 15 Sampling Lecture 16 Correlation analysis Section 4: Building Data Science Models Lecture 17 Dimensionality reduction Lecture 18 Linear discriminant analysis Lecture 19 Data preprocessing Lecture 20 Linear regression Lecture 21 Classification Lecture 22 Performance evaluation and model selection Lecture 23 Cross validation Lecture 24 Distributions Lecture 25 series analysis Section 5: Working with Visualizations 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 Section 6: Parallel Computing Lecture 34 Basic concepts of parallel computing Lecture 35 Data movement Lecture 36 Parallel maps and loop operations Lecture 37 Channels This course is for Julia programmers who want to learn data science right from exploratory analytics to the visualization part.,Anyone who wants to work more effectively with data HomePage:
TO MAC USERS: If RAR password doesn't work, use this archive program:
RAR Expander 0.8.5 Beta 4 and extract password protected files without error.
TO WIN USERS: If RAR password doesn't work, use this archive program:
Latest Winrar and extract password protected files without error.