Build an OpenSource Time Series Lib from Scratch in Rust

Build an Open-Source Time Series Lib from Scratch in Rust
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 7h 50m | 3.43 Gb
Instructor: Ravinthiran Partheepan

• You will learn to create an open-source time-series processing library from scratch.
  
• You will explore how time-series data and the mathematics behind each time-series model are used in forecasting and machine learning models.
  
• You will learn how to apply ARIMA, Exponential Smoothing, and machine learning techniques to predict future values
  
• You will learn how Rust handles data structures, file I/O, and performance optimization.
  

• A basic understanding of statistics, including concepts like mean, variance, and others, is recommended to take this course.
  
• No programming knowledge of Rust is required. The basics will be taught, and you will learn them while coding each module.
  

• What time-series data is and where it is used (stocks, weather, sensors, etc.)
  
• Rust basics and key features for data processing.
  
• How to build time-series functions like moving averages, trend detection, ARIMA, SARIMA model from scratch, and much more.
  
• Mathematical concepts and equations behind Time series models.
  
• How to optimize performance using Rust's speed and memory safety?
  
• How to open-source your cargo library and share it with others?
  

• Hands-on learning: build a time series model from scratch step by step without any external library dependencies.
  
• Beginner-friendly modules: No prior Rust experience needed
  
• Career growth: Add Rust and time-series expertise to your skillset
  
• By the end of this course, you will have your own Rust-based time-series library ready to use and share!
  

• Anyone who wants to contribute to open-source projects.
  
• Engineers looking to create high-performance data tools.
  
• Developers who want to learn Rust by building a real project.
  
• Anyone interested in time-series analysis and machine learning.
  
• Data scientists or analysts who want to explore Rust for time-series processing.