Julia Solutions [Video]

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Julia Solutions [Video]

Jalem Raj Rohit

Comprehensive guide to learn data science for a Julia programmer, right from the exploratory analytics part to the visualization part

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Video Details

ISBN 139781787283299
Course Length2 hours and 52 minutes

Video Description

Want to handle everything that Julia can throw at you and get the most of it every day? This practical guide to programming with Julia for performing numerical computation will make you more productive and able to work with data more efficiently. The video 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 video course includes recipes 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.

Style and Approach

This video follows a recipe-based approach to help you grasp the concepts of Julia programming.

Table of Contents

Extracting and Handling Data
The Course Overview
Handling Data with CSV Files
Handling Data with TSV Files
Interacting with the Web
Metaprogramming
Representation of a Julia Program
Symbols
Quoting
Interpolation
The eval Function
Macros
Metaprogramming with DataFrames
Statistics with Julia
Basic Statistics Concepts
Descriptive Statistics
Deviation Metrics
Sampling
Correlation Analysis
Building Data Science Models
Dimensionality Reduction
Data Preprocessing
Linear Regression
Classification
Performance Evaluation and Model Selection
Cross Validation
Distances
Distributions
Time Series Analysis
Working with Visualizations
Plotting Basic Arrays
Plotting DataFrames
Plotting Functions
Exploratory Data Analytics Through Plots
Line Plots
Scatter Plots
Histograms
Aesthetic Customizations
Parallel Computing
Basic Concepts of Parallel Computing
Data Movement
Parallel Maps and Loop Operations
Channels

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
  • Find out how to visualize your data with Gadfly
  • Explore big data concepts in Julia

Authors

Table of Contents

Extracting and Handling Data
The Course Overview
Handling Data with CSV Files
Handling Data with TSV Files
Interacting with the Web
Metaprogramming
Representation of a Julia Program
Symbols
Quoting
Interpolation
The eval Function
Macros
Metaprogramming with DataFrames
Statistics with Julia
Basic Statistics Concepts
Descriptive Statistics
Deviation Metrics
Sampling
Correlation Analysis
Building Data Science Models
Dimensionality Reduction
Data Preprocessing
Linear Regression
Classification
Performance Evaluation and Model Selection
Cross Validation
Distances
Distributions
Time Series Analysis
Working with Visualizations
Plotting Basic Arrays
Plotting DataFrames
Plotting Functions
Exploratory Data Analytics Through Plots
Line Plots
Scatter Plots
Histograms
Aesthetic Customizations
Parallel Computing
Basic Concepts of Parallel Computing
Data Movement
Parallel Maps and Loop Operations
Channels

Video Details

ISBN 139781787283299
Course Length2 hours and 52 minutes
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