Statistics and R
Data Analysis for Life Sciences,
This course teaches the R programming language in the context of statistical data and statistical analysis in the life sciences.
We will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R code. We provide R programming examples in a way that will help make the connection between concepts and implementation. Problem sets requiring R programming will be used to test understanding and ability to implement basic data analyses. We will use visualization techniques to explore new data sets and determine the most appropriate approach. We will describe robust statistical techniques as alternatives when data do not fit assumptions required by the standard approaches. By using R scripts to analyze data, you will learn the basics of conducting reproducible research.
Given the diversity in educational background of our students we have divided the course materials 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. We start with simple calculations and descriptive statistics. 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 make up 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
PH525.7x: Advanced Bioconductor
This class was supported in part by NIH grant R25GM114818.
What you'll learn
- Random variables
- Distributions
- Inference: p-values and confidence intervals
- Exploratory Data Analysis
- Non-parametric statistics
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| Rating | 3.4★ based on 19 ratings |
|---|---|
| Length | 4 weeks |
| Effort | 4 weeks, 2–4 hours per week |
| Starts | On Demand (Start anytime) |
| Cost | $129 |
| From | Harvard University, HarvardX via edX |
| Instructors | Rafael Irizarry, Michael Love |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Data Science Science |
| Tags | Data Analysis & Statistics Biology & Life Sciences Science |
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What people are saying
asking rather than actually
The exercises usually built on these as well, although I felt that the wording for some of the questions were quite dubious - often a lot of the time I spent on this course was figuring out what the question was asking rather than actually working on getting a solution!
quite dubious - often
stats two years ago
To get through the first quarter of the course I had to do a lot of googling for how to work with R, which was fine and helped me learn R. I took intro to stats two years ago, now I'm facing econometrics so needed to learn R and brush up on stats.
videos are almost useless
computer science but none
I have a background in computer science but none in statistics.
due about 4 months
Course material is released every week, but all the quizzes were due about 4 months after the course actually started, which allows flexibility for students.
facing econometrics so needed
monte carlo simulations
Pro: If you watch the videos, read the material, and do the exercises, you will emerge with a working understanding of statistics foundations (normal distribution, Student's t-distribution, Monte Carlo simulations, etc.)
basic statistical information
This was basic statistical information, so someone with that background would be good to go.
comments should still
I believe the material in the first two-three courses remains the same, so my comments should still be valid here.)
many terms used
released every week
Careers
An overview of related careers and their average salaries in the US. Bars indicate income percentile.
Statistical Technician $39k
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Statistical Programmer Trainee $63k
Senior Statistical Data Analyst (part time) $69k
Assistant Supervisor Statistical Programmer Analyst $71k
Statistical Programmer/ Clinical SAS Programmer Contractor $82k
Statistical Accounting Analyst $89k
Staff Statistical Analyst $97k
Quality Engineer - Statistical Evaluation and Validation Manager $97k
Senior Statistical/Clinical Programmer $98k
Associate Principal Statistical Programmer $161k
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| Rating | 3.4★ based on 19 ratings |
|---|---|
| Length | 4 weeks |
| Effort | 4 weeks, 2–4 hours per week |
| Starts | On Demand (Start anytime) |
| Cost | $129 |
| From | Harvard University, HarvardX via edX |
| Instructors | Rafael Irizarry, Michael Love |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Data Science Science |
| Tags | Data Analysis & Statistics Biology & Life Sciences Science |
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