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Development Data Science Julia Programming Language

Learning Path: Julia: Explore Data Science with Julia

Use the advanced features of Julia to work with complex data
Rating: 4.3 out of 54.3 (69 ratings)
658 students
Created by Packt Publishing
Last updated 4/2017
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Get to grips with the basic data structures in Julia and learn about different development environments
  • Organize your code by writing Lisp-style macros and using modules
  • Manage, analyze, and work in depth with statistical datasets using the powerful DataFrames package
  • Perform statistical computations on data from different sources and visualize those using plotting packages
  • Apply different algorithms from decision trees and other packages to extract meaningful information from the Iris dataset
  • Gain some valuable insights into interfacing Julia with an R application
  • Uncover the concepts of metaprogramming in Julia
  • Conduct statistical analysis with StatsBase.jl and Distributions.jl

Course content

2 sections • 63 lectures • 5h 32m total length

  • Preview02:35
  • Installing a Julia Working Environment
    05:12
  • Working with Variables and Basic Types
    08:07
  • Controlling the Flow
    05:17
  • Using Functions
    08:35
  • Using Tuples, Sets, and Dictionaries
    05:53
  • Working with Matrices for Data Storage and Calculations
    08:25
  • Preview06:42
  • Optimizing Your Code by Using and Writing Macros
    07:11
  • Organizing Your Code in Modules
    06:25
  • Working with the Package Ecosystem
    06:18
  • Preview07:41
  • Using DataArrays and DataFrames
    07:41
  • The Power of DataFrames
    06:36
  • Interacting with Relational Databases Like SQL Server
    07:20
  • Interacting with NoSQL Databases Like MongoDB
    06:23
  • Preview06:38
  • An Overview of the Plotting Techniques in Julia
    03:02
  • Visualizing Data with Scatterplots, Histograms, and Box Plots
    04:24
  • Distributions and Hypothesis Testing
    05:34
  • Interfacing with R
    04:24
  • Preview06:15
  • Classification Using Decision Trees and Rules
    07:00
  • Training and Testing a Decision Tree Model
    03:58
  • Applying a Generalized Linear Model with GLM
    06:17
  • Working with Support Vector Machines
    07:11

  • Preview05:02
  • Handling Data with CSV Files
    06:28
  • Handling Data with TSV Files
    03:33
  • Interacting with the Web
    06:42
  • Preview06:38
  • Symbols
    03:07
  • Quoting
    03:32
  • Interpolation
    03:48
  • The eval Function
    03:24
  • Macros
    04:31
  • Metaprogramming with DataFrames
    07:56
  • Preview05:15
  • Descriptive Statistics
    07:04
  • Deviation Metrics
    03:36
  • Sampling
    06:27
  • Correlation Analysis
    07:52
  • Preview05:09
  • Data Preprocessing
    05:16
  • Linear Regression
    03:20
  • Classification
    03:19
  • Performance Evaluation and Model Selection
    04:47
  • Cross Validation
    03:28
  • Distances
    04:35
  • Distributions
    05:14
  • Time Series Analysis
    01:35
  • Preview06:21
  • Plotting DataFrames
    05:12
  • Plotting Functions
    05:31
  • Exploratory Data Analytics Through Plots
    05:13
  • Line Plots
    02:46
  • Scatter Plots
    03:33
  • Histograms
    03:45
  • Aesthetic Customizations
    03:49
  • Basic Concepts of Parallel Computing
    05:46
  • Data Movement
    02:45
  • Parallel Maps and Loop Operations
    03:25
  • Channels
    02:04

Requirements

  • Although knowing the basic concepts of data science will give you a head-start, it is not a mandatory requirement. With no previous knowledge in data science as well, you will find the pace of the Learning Path quite comfortable and easy to follow.

Description

Almost all companies these days are investing thousands of dollars in data analysis to get their data analyzed. Well, in fact studies say that there are around 73% of organizations have invested in Big Data. Why do you think that is the case? What can you reap of the data, ideally just 1s and 0s? Moreover, how does this data help an organization’s future?

Most of you might have guessed it right; the market trends, the consumer habits can all be precisely predicted, if we are able to analyze our data efficiently. This Learning Path will tell you how you can achieve all this by using Julia.

Packt’s Video Learning Paths are an amalgamation of multiple video courses that are logically tied together to provide you with a larger learning curve.

With the amount of data that is generated in the world these days, we are faced with the challenge of analyzing this data. Julia, which enjoys the benefits of a sophisticated compiler, parallel execution, and an all-encompassing mathematical function library, acts as a very good tool that helps us work with data more efficiently.

In this Learning Path, embark on your journey from the basics of Julia, right from installing it on your system and setting up the environment. You will then be introduced to the basic machine learning techniques, data science models, and concepts of parallel computing.

After completing this Learning Path, you will have acquired all the skills that will help you work with data effectively. 

About the Authors

Ivo Balbaert is currently a web programming and databases lecturer at CVO Antwerpen, a community college in Belgium. He received a PhD in applied physics in 1986 from the University of Antwerp. He worked for 20 years in the software industry as a developer and consultant in several companies, and, for 10 years, as a project manager at the University Hospital of Antwerp. In 2000, he switched over to partly teach and partly develop software (KHM Mechelen, CVO Antwerp).

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 has interned at Scimergent Analytics.

Who this course is for:

  • This Learning Path is for anyone 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.

Instructor

Packt Publishing
Tech Knowledge in Motion
Packt Publishing
  • 3.9 Instructor Rating
  • 59,127 Reviews
  • 354,606 Students
  • 1,418 Courses

Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

With an extensive library of content - more than 4000 books and video courses -Packt's mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages, to cutting edge data analytics, and DevOps, Packt takes software professionals in every field to what's important to them now.

From skills that will help you to develop and future proof your career to immediate solutions to every day tech challenges, Packt is a go-to resource to make you a better, smarter developer.

Packt Udemy courses continue this tradition, bringing you comprehensive yet concise video courses straight from the experts.



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