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.
Julia, Data Science training table of contents (duration : 2h52m)
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Extracting and Handling Data
- The Course Overview 00:05:03
- Handling Data with CSV Files 00:06:29
- Handling Data with TSV Files 00:03:33
- Interacting with the Web 00:06:43
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Metaprogramming
- Representation of a Julia Program 00:06:38
- Symbols 00:03:07
- Quoting 00:03:32
- Interpolation 00:03:49
- The eval Function 00:03:25
- Macros 00:04:31
- Metaprogramming with DataFrames 00:07:57
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Statistics with Julia
- Basic Statistics Concepts free 00:05:15
- Descriptive Statistics 00:07:05
- Deviation Metrics 00:03:37
- Sampling 00:06:28
- Correlation Analysis 00:07:53
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Building Data Science Models
- Dimensionality Reduction 00:05:09
- Data Preprocessing 00:05:16
- Linear Regression 00:03:20
- Classification 00:03:20
- Performance Evaluation and Model Selection 00:04:47
- Cross Validation 00:03:29
- Distances 00:04:35
- Distributions 00:05:14
- Time Series Analysis 00:01:36
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Working with Visualizations
- Plotting Basic Arrays 00:06:22
- Plotting DataFrames 00:05:12
- Plotting Functions 00:05:32
- Exploratory Data Analytics Through Plots 00:05:13
- Line Plots 00:02:46
- Scatter Plots 00:03:33
- Histograms 00:03:45
- Aesthetic Customizations 00:03:49
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Parallel Computing
- Basic Concepts of Parallel Computing 00:05:46
- Data Movement 00:02:45
- Parallel Maps and Loop Operations 00:03:25
- Channels 00:02:09
- Certificate
Instructor : Packt
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With which software version is this tutorial compatible with?Julia
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What is the required level to follow this tutorial ?intermediate
