Current Status
Not Enrolled
Get Started

GPU-based Statistical Analysis from Scratch in Python​

Master parametric statistical analysis using Python and harness the computational power of both CPUs and GPUs from various vendors for the same base code!

Created by Yahya Khawam

Last Updated 02/2024 English

This course is an E-book

What You'll Learn

  • The course covers not only the implementation but also the underlying theory behind various statistical analyses. This theoretical understanding is critical for knowing when and how to apply different tests and analyses correctly and interpreting the results accurately.
  • In this course, you will learn how to create a pip-installable python package that contains all parametric statistical analysis algorithms you will learn from scratch. With this, you can let others use your own published projects on GitHub while contributing to open-source community.
  • The course teaches you how to write code that is cross-platform. Being able to write code that runs on various hardware (Apple Silicon, AMD, NVIDIA, Intel) without modification increases your code’s usability and your efficiency as a developer or analyst.
  •  Understanding how to utilize both CPU and GPU for data analysis ensures that you can perform analyses more quickly and efficiently. This is especially important when dealing with large datasets, where computational performance can be a bottleneck.

As the world becomes increasingly data-driven, the ability to analyze and interpret data is a valuable skill in almost every industry. Through this course, individuals will acquire the knowledge and tools necessary to make informed decisions based on data. In this cutting-edge course, you will learn how to conduct parametric statistical analysis using Python and harness the computational power of both CPUs and GPUs. The code in this course is designed to be cross-platform, which means it seamlessly works across various hardware including Apple Silicon, AMD, NVIDIA, and Intel, thanks to PyTorch’s robust functions. Whether you are a student, data analyst, researcher, or enthusiast, this course provides you with the tools to perform complex data analyses and visualizations at incredible speeds.

Learning to create statistical analyses from scratch in Python gives you the ability to customize your analyses and not be dependent on specific libraries or tools. This can be particularly important in research or specialized industries where standard tools might not be adequate.

As data continues to play an even larger role in society and the economy, having skills in data analysis and computation is likely to become even more important. Being prepared for this data-centric future is a wise investment. Proficiency in data analysis and understanding of computational acceleration is highly sought-after skills in the job market. Whether you are looking for a career in data science, research, finance, healthcare, or almost any other field, these skills can provide a competitive edge.

Who Is This Course For?

  • Data Analysts
  • Researchers
  • Students in Statistics or Data Science
  • Python Enthusiasts interested in Data Analysis
  • Anyone who wants to harness the power of GPU and CPU in Data Analysis

Course Requirements

  • Comfortable with Python programming.

Course Content

1.0 Data Visualization
2.0 Data Analysis
3.0 Data Distribution
4.0 Probability Theory
5.0 Discrete Random Variables
6.0 Part 1: CPU and GPU Acceleration for Essential Probability and Statistics Concepts
7.0 Introduction to Hypothesis Testing
8.0 Part 2: One Sample Parametric Statistical Testing From Scratch
9.0 Part 3: Two Sample Parametric Statistical Testing From Scratch
10.0 Publish The SciModelStats Package