Ultimate Parallel and Distributed Computing with Julia For Data Science: Excel in Data Analysis, Statistical Modeling and Machine Learning by . Programming ? Parallel Systems Path)

Unleash Julia’s power: Code Your Data Stories, Shape Machine Intelligence!
Are you ready to supercharge your data science skills with blazing-fast parallel computing with Julia and efficient distributed computing tools for real-world analytics? Ultimate Parallel and Distributed Computing with Julia For Data Science is your in-depth, hands-on guide for mastering scalable data solutions, workflow optimization, and advanced data visualization using Julia language.

Book Description
This book takes you through a step-by-step learning journey, starting with the essentials of Julia's syntax, variables, and functions. You'll unlock the power of efficient data handling by leveraging Julia arrays and DataFrames.jl for insightful analysis. Develop expertise in both basic and advanced statistical models, providing a robust toolkit for deriving meaningful data-driven insights. The journey continues with machine learning proficiency, where you'll implement algorithms confidently using MLJ.jl and MLBase.jl, paving the way for advanced data-driven solutions. Explore the realm of Bayesian inference skills through practical applications using Turing.jl, enhancing your ability to extract valuable insights. The book also introduces crucial Julia packages such as Plots.jl for visualizing data and results.

The handbook culminates in optimizing workflows with Julia's parallel and distributed computing capabilities, ensuring efficient and scalable data processing using Distributions.jl, Distributed.jl and SharedArrays.jl. This comprehensive guide equips you with the knowledge and practical insights needed to excel in the dynamic field of data science and machine learning.

What You'll Learn Inside:

• Step-by-step distributed computing Julia guide for scalable data science solutions
• Practical tutorials on parallel computing books with Julia arrays and high-performance workflow strategies
• How to build effective data analytics and visualization pipelines using Julia language and DataFrames.jl
• Comprehensive coverage of statistical modeling, bayesian inference and statistics, and machine learning in Julia for transformative business intelligence
• Real-world machine learning with Julia projects using MLJ.jl and MLBase.jl for scalable analytics
• BA hands-on approach to workflow optimization with sharedarrays Julia programming
• Complete strategies for business intelligence Julia and applied statistics with Julia for practical data science applications
• Optimization for data pipelines and scientific computing with Julia for advanced analytics

Who is This Book For?

• Data scientists and data analysts looking to scale analytics with parallel computing Julia
• Researchers and developers using distributed computing Julia for scientific computing and business intelligence
• Learners and professionals seeking hands-on experience in bayesian modeling Julia and workflow development
• Anyone interested in building robust real-world data pipelines with Julia for applied statistics and machine learning

Table of Contents
1. Julia In Data Science Arena
2. Getting Started with Julia
3. Features Assisting Scaling ML Projects
4. Data Structures in Julia
5. Working With Datasets In Julia
6. Basics of Statistics
7. Probability Data Distributions
8. Framing Data in Julia
9. Working on Data in DataFrames
10. Visualizing Data in Julia
11. Introducing Machine Learning in Julia
12. Data and Models
13. Bayesian Statistics and Modeling
14. Parallel Computation in Julia
15. Distributed Computation in Julia
Index