Oreilly – Julia for Data Analysis, Video Edition 2024-10

Published on: 2024-11-19 14:24:08

Categories: 28

Description

Julia for Data Analysis course, Video Edition. This comprehensive and hands-on course will help you learn core data analysis skills using the powerful Julia programming language. With engaging hands-on projects, you’ll learn how to analyze time series data, build predictive models, rank popularity, and more. About the technology: Julia is a great programming language for data analysis. It’s easy to learn, fast, and suitable for anything from simple calculations to full data processing pipelines. Whether you’re looking for a better way to analyze your day-to-day business data or you’re just starting your data science journey, learning Julia will be an invaluable skill for you.

What you will learn

Read and write data in different formats
Work with tabular data, including subcategorization, grouping, and data transformation
Data visualization
Building prediction models
Create a data processing pipeline
Creating web services to share data analysis results
Writing readable and efficient Julia programs

Who is this course suitable for?

This course is suitable for anyone looking to learn a powerful tool for data analysis, including:
Data scientists
Data analysts
Data engineers
Students and researchers in different fields

Julia for Data Analysis course specifications, Video Edition

Publisher: Oreilly
Lecturer: Bogumil Kaminski
Education level: Intermediate
Training duration: 12 hours 30 minutes

Course headings

Chapter 1. Introduction
Chapter 1. Key features of Julia from a data scientist’s perspective
Chapter 1. Usage scenarios of tools presented in the book
Chapter 1. Julia’s drawbacks
Chapter 1. What data analysis skills will you learn?
Chapter 1. How can Julia be used for data analysis?
Chapter 1. Summary
Part 1. Essential Julia skills
Chapter 2. Getting started with Julia
Chapter 2. Defining variables
Chapter 2. Using the most important control-flow constructs
Chapter 2. Defining functions
Chapter 2. Understanding variable scoping rules
Chapter 2. Summary
Chapter 3. Julia’s support for scaling projects
Chapter 3. Using multiple dispatch in Julia
Chapter 3. Working with packages and modules
Chapter 3. Using macros
Chapter 3. Summary
Chapter 4. Working with collections in Julia
Chapter 4. Mapping key-value pairs with dictionaries
Chapter 4. Structuring your data by using named tuples
Chapter 4. Summary
Chapter 5. Advanced topics on handling collections
Chapter 5. Defining methods with parametric types
Chapter 5. Integrating with Python
Chapter 5. Summary
Chapter 6. Working with strings
Chapter 6. Splitting strings
Chapter 6. Using regular expressions to work with strings
Chapter 6. Extracting a subset from a string with indexing
Chapter 6. Analyzing genre frequency in movies.dat
Chapter 6. Introducing symbols
Chapter 6. Using fixed-width string types to improve performance
Chapter 6. Compressing vectors of strings with PooledArrays.jl
Chapter 6. Choosing appropriate storage for collections of strings
Chapter 6. Summary
Chapter 7. Handling time-series data and missing values
Chapter 7. Working with missing data in Julia
Chapter 7. Getting time-series data from the NBP Web API
Chapter 7. Analyzing data fetched from the NBP Web API
Chapter 7. Summary
Part 2. Toolbox for data analysis
Chapter 8. First steps with data frames
Chapter 8. Loading the data to a data frame
Chapter 8. Getting a column out of a data frame
Chapter 8. Reading and writing data frames using different formats
Chapter 8. Summary
Chapter 9. Getting data from a data frame
Chapter 9. Analyzing the relationship between puzzle difficulty and popularity
Chapter 9. Summary
Chapter 10. Creating data frame objects
Chapter 10. Creating data frames incrementally
Chapter 10. Summary
Chapter 11. Converting and grouping data frames
Chapter 11. Grouping data frame objects
Chapter 11. Summary
Chapter 12. Mutating and transforming data frames
Chapter 12. Computing additional node features
Chapter 12. Using the split-apply-combine approach to predict the developer’s type
Chapter 12. Reviewing data frame mutation operations
Chapter 12. Summary
Chapter 13. Advanced transformations of data frames
Chapter 13. Investigating the violation column
Chapter 13. Preparing data for making predictions
Chapter 13. Building a predictive model of arrest probability
Chapter 13. Reviewing functionalities provided by DataFrames.jl
Chapter 13. Summary
Chapter 14. Creating web services for sharing data analysis results
Chapter 14. Implementing the option pricing simulator
Chapter 14. Creating a web service serving the Asian option valuation
Chapter 14. Using the Asian option pricing web service
Chapter 14. Summary
Appendix A. First steps with Julia
Appendix A. Getting help in and about Julia
Appendix A. Managing packages in Julia
Appendix A. Reviewing standard ways to work with Julia
Appendix B. Solutions to exercises
Appendix C. Julia packages for data science
Appendix C. Scaling computing with Julia
Appendix C. Working with databases and data storage formats
Appendix C. Using data science methods
Appendix C. Summary