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Learning Path: Julia: Explore Data Science with Julia

Video Description

Use the advanced features of Julia to work with complex data

In Detail

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 a a very good tool that helps us work with data more efficiently.

In this Learning Path, embark 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.

Prerequisites: The knowledge of the basic data science concepts is beneficial, although not necessary.

Resources: Code downloads and errata:

  • Julia for Data Science

  • Julia Solutions

  • PATH PRODUCTS

    This path navigates across the following products (in sequential order):

  • Julia for Data Science (2h 41m)

  • Julia Solutions (2h 52m)

  • Table of Contents

    1. Chapter 1 : Julia for Data Science
      1. The Course Overview 00:02:41
      2. Installing a Julia Working Environment 00:05:13
      3. Working with Variables and Basic Types 00:08:07
      4. Controlling the Flow 00:05:18
      5. Using Functions 00:08:36
      6. Using Tuples, Sets, and Dictionaries 00:05:54
      7. Working with Matrices for Data Storage and Calculations 00:08:25
      8. Using Types and Parameterized Methods 00:06:43
      9. Optimizing Your Code by Using and Writing Macros 00:07:11
      10. Organizing Your Code in Modules 00:06:26
      11. Working with the Package Ecosystem 00:06:19
      12. Reading and Writing Data Files and Julia Data 00:07:41
      13. Using DataArrays and DataFrames 00:07:41
      14. The Power of DataFrames 00:06:36
      15. Interacting with Relational Databases Like SQL Server 00:07:21
      16. Interacting with NoSQL Databases Like MongoDB 00:06:24
      17. Exploring and Understanding a Dataset Statistically 00:06:38
      18. An Overview of the Plotting Techniques in Julia 00:03:02
      19. Visualizing Data with Scatterplots, Histograms, and Box Plots 00:04:24
      20. Distributions and Hypothesis Testing 00:05:35
      21. Interfacing with R 00:04:25
      22. Basic Machine Learning Techniques 00:06:15
      23. Classification Using Decision Trees and Rules 00:07:01
      24. Training and Testing a Decision Tree Model 00:03:58
      25. Applying a Generalized Linear Model with GLM 00:06:17
      26. Working with Support Vector Machines 00:07:11
    2. Chapter 2 : Julia Solutions
      1. The Course Overview 00:05:03
      2. Handling Data with CSV Files 00:06:29
      3. Handling Data with TSV Files 00:03:33
      4. Interacting with the Web 00:06:43
      5. Representation of a Julia Program 00:06:38
      6. Symbols 00:03:07
      7. Quoting 00:03:32
      8. Interpolation 00:03:49
      9. The eval Function 00:03:25
      10. Macros 00:04:31
      11. Metaprogramming with DataFrames 00:07:57
      12. Basic Statistics Concepts 00:05:15
      13. Descriptive Statistics 00:07:04
      14. Deviation Metrics 00:03:37
      15. Sampling 00:06:28
      16. Correlation Analysis 00:07:53
      17. Dimensionality Reduction 00:05:09
      18. Data Preprocessing 00:05:16
      19. Linear Regression 00:03:20
      20. Classification 00:03:20
      21. Performance Evaluation and Model Selection 00:04:47
      22. Cross Validation 00:03:29
      23. Distances 00:04:35
      24. Distributions 00:05:14
      25. Time Series Analysis 00:01:36
      26. Plotting Basic Arrays 00:06:22
      27. Plotting DataFrames 00:05:12
      28. Plotting Functions 00:05:32
      29. Exploratory Data Analytics Through Plots 00:05:13
      30. Line Plots 00:02:46
      31. Scatter Plots 00:03:33
      32. Histograms 00:03:45
      33. Aesthetic Customizations 00:03:49
      34. Basic Concepts of Parallel Computing 00:05:46
      35. Data Movement 00:02:45
      36. Parallel Maps and Loop Operations 00:03:25
      37. Channels 00:02:09