Introduction to Linear Models and Matrix Algebra

Introduction to Linear Models and Matrix Algebra: You’ll learn to use R programming to apply linear models to analyze data in life sciences.

Last updated on July 22, 2021 1:22 pm
Category: Data Analytics

Introduction

In this course on Introduction to Linear Models and Matrix Algebra, you’ll learn to use R programming to apply linear models to analyze data in life sciences.

Matrix Algebra underlies many of the current tools for experimental design and the analysis of high-dimensional data. In this introductory online course in data analysis, we will use matrix algebra to represent the linear models that commonly used to model differences between experimental units. We perform statistical inference on these differences. Throughout the course we will use the R programming language to perform matrix operations.

Given the diversity in the educational background of our students, we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. You will need to know some basic stats for this course. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.

These courses makeup two Professional Certificates and are self-paced:

Data Analysis for Life Sciences:

• PH525.1x: Statistics and R for the Life Sciences
• PH525.2x: Introduction to Linear Models and Matrix Algebra
• PH525.3x: Statistical Inference and Modeling for High-throughput Experiments
• PH525.4x: High-Dimensional Data Analysis

Genomics Data Analysis:

• PH525.5x: Introduction to Bioconductor
• PH525.6x: Case Studies in Functional Genomics
• This class was supported in part by NIH grant R25GM114818.

What you will learn?

• Matrix algebra notation
• Matrix algebra operations
• Application of matrix algebra to data analysis
• Linear models
• Brief introduction to the QR decomposition

Prerequisites

• Basic math
• Basic stats and R programming OR PH525.1x

Syllabus

Introduction and Resources

• Introduction
• Course Materials and R Resources
• Pre-Course Survey

Week 1

• Introduction
• Introduction to Matrix Algebra

Week 2

• Data Analysis with Matrix Algebra
• Inference

Week 3

• Linear Models

Week 4

• Interactions and Contrasts
• Calculation of Linear Models
• Data Analysis for Life Sciences Series

If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.

FAQ

Introduction

In this course on Introduction to Linear Models and Matrix Algebra, you’ll learn to use R programming to apply linear models to analyze data in life sciences.

Matrix Algebra underlies many of the current tools for experimental design and the analysis of high-dimensional data. In this introductory online course in data analysis, we will use matrix algebra to represent the linear models that commonly used to model differences between experimental units. We perform statistical inference on these differences. Throughout the course we will use the R programming language to perform matrix operations.

Given the diversity in the educational background of our students, we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. You will need to know some basic stats for this course. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.

These courses makeup two Professional Certificates and are self-paced:

Data Analysis for Life Sciences:

• PH525.1x: Statistics and R for the Life Sciences
• PH525.2x: Introduction to Linear Models and Matrix Algebra
• PH525.3x: Statistical Inference and Modeling for High-throughput Experiments
• PH525.4x: High-Dimensional Data Analysis

Genomics Data Analysis:

• PH525.5x: Introduction to Bioconductor
• PH525.6x: Case Studies in Functional Genomics
• This class was supported in part by NIH grant R25GM114818.

What you will learn?

• Matrix algebra notation
• Matrix algebra operations
• Application of matrix algebra to data analysis
• Linear models
• Brief introduction to the QR decomposition

Prerequisites

• Basic math
• Basic stats and R programming OR PH525.1x

Syllabus

Introduction and Resources

• Introduction
• Course Materials and R Resources
• Pre-Course Survey

Week 1

• Introduction
• Introduction to Matrix Algebra

Week 2

• Data Analysis with Matrix Algebra
• Inference

Week 3

• Linear Models

Week 4

• Interactions and Contrasts
• Calculation of Linear Models
• Data Analysis for Life Sciences Series

If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.

FAQ

Specification:

• EDX
• Harvard University
• Online Course
• Self-paced
• Intermediate
• 1-4 Weeks
• Free Course (Affordable Certificate)
• English
• Bioinformatics Computer programming Data Analysis Maths

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Introduction to Linear Models and Matrix Algebra

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