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
Learn to build an open-source time-series library in Rust from scratch.
What you'll learn
- 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.
Requirements
- 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.
Description
Want to build something useful while learning Rust? In this course, you will create an open-source time-series library from scratch without any external library dependencies using Rust!
What You Will Learn:
- 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?
Why Take This Course?
- 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!
Who this course is for:
- 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.
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