Julia Solutions
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VIDEO TUTORIAL Julia Solutions

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

About This Julia Video course

  • Follow a practical approach to learn Julia programming the easy way
  • Get an extensive coverage of Julia’s packages for statistical analysis
  • This video-based approach will help you get familiar with the key concepts in Julia

Julia video tutorial In Detail

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 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 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.

What will you learn in this course?

Course plan
Chapter 1
Extracting and Handling Data
Chapter 2
Chapter 3
Statistics with Julia
Chapter 4
Building Data Science Models
Chapter 5
Working with Visualizations
Chapter 6
Parallel Computing

Detailed course plan

Chapter 1 : Extracting and Handling Data
Lesson 1The Course Overview
Lesson 2Handling Data with CSV Files
Lesson 3Handling Data with TSV Files
Lesson 4Interacting with the Web
Chapter 2 : Metaprogramming
Lesson 1Representation of a Julia Program
Lesson 2Symbols
Lesson 3Quoting
Lesson 4Interpolation
Lesson 5The eval Function
Lesson 6Macros
Lesson 7Metaprogramming with DataFrames
Chapter 3 : Statistics with Julia
Lesson 1Basic Statistics Concepts
Lesson 2Descriptive Statistics
Lesson 3Deviation Metrics
Lesson 4Sampling
Lesson 5Correlation Analysis
Chapter 4 : Building Data Science Models
Lesson 1Dimensionality Reduction
Lesson 2Data Preprocessing
Lesson 3Linear Regression
Lesson 4Classification
Lesson 5Performance Evaluation and Model Selection
Lesson 6Cross Validation
Lesson 7Distances
Lesson 8Distributions
Lesson 9Time Series Analysis
Chapter 5 : Working with Visualizations
Lesson 1Plotting Basic Arrays
Lesson 2Plotting DataFrames
Lesson 3Plotting Functions
Lesson 4Exploratory Data Analytics Through Plots
Lesson 5Line Plots
Lesson 6Scatter Plots
Lesson 7Histograms
Lesson 8Aesthetic Customizations
Chapter 6 : Parallel Computing
Lesson 1Basic Concepts of Parallel Computing
Lesson 2Data Movement
Lesson 3Parallel Maps and Loop Operations
Lesson 4Channels

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