https://github.com/3mmarand/bio00058m-data-science-2020

University of York, Department of Biology, M-level module: Data Science option of BIO00058M Data Analysis

https://github.com/3mmarand/bio00058m-data-science-2020

Repository

University of York, Department of Biology, M-level module: Data Science option of BIO00058M Data Analysis

https://github.com/3mmaRand/BIO00058M-Data-science-2020/blob/master/

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5442490.svg)](https://doi.org/10.5281/zenodo.5442490)

![](pics/58M.png)  

# Overview

## Introduction

Data science is an interdisciplinary field about processes to extract and report on  insights from data and includes importing, tidying, visualising, analysing and communicating. Data Science is applied to complex problems in any domain of biology and beyond, with data generated from, for example, images, all types of 'omic' technologies, monitoring devices, publications and social media. Critically evaluating such analyses and advancing methodology is dependent on research being open and reproducible. This option will allow you to develop skills required to openly and reproducibly acquire, analyse and communicate data in your own field. You will learn how to organise and document analyses efficiently and use RMarkdown for "literate programming" to create fully reproducible documents. You will also learn some widely used machine learning methods. The assessment has flexibility and you can either work with a provided dataset or one of your own choosing.

## Requirements

Experience of R equivalent to BIO00017C / BIO00008C and  BIO00058I

## Topics 

Chosen topics are: foundational, follow stages 1 and 2 well, are widely applicable (in this module and beyond) and transferable conceptually:

1. Using RStudio projects and an emphasis on good practice in code and project documentation and organisation.
2. More advanced data tidying.
3. An emphasis on reproducibility and reproducible reporting using R Markdown.
4. Some machine learning concepts and methods that are very commonly applied independent of the data domain.

-  Week 2: Preparation 1 - Update your R and RStudio, revise previously taught material.
-  Week 3: Preparation 2 - Introduction to the module. 
-  Week 4: Topic 1 - Project organisation. *
-  Week 5: Topic 2 - Tidying data and the tidyverse.*
-  Week 6: Topic 3 - Reproducibility and an introduction to R Markdown.*
-  Week 7: Topic 4 - Advanced R Markdown.*
-  Week 8: Topic 5 - An introduction to Machine Learning: Overview and Unsupervised methods.*
-  Week 9: Topic 6 - An introduction to Machine Learning: Supervised Methods.*
-  Week 10: Preparing the assessment - guidelines reminder, project infrastructure. *
-  Spring Weeks 1 - 5: Project work. Drop-ins TBC

*week includes a timetabled session

## Teaching and Contact time

Each topic will start with some independent study in the form of written material and short videos including worked examples. There will then be a workshop in which you will work on topic problems. 

These are indexed here: https://3mmarand.github.io/BIO00058M-Data-science-2020

## Materials

Data Science strand of BIO00058M by Emma Rand is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Please cite as:

Emma Rand. (2021). Data Science strand of BIO00058M (v1.0). Zenodo. https://doi.org/10.5281/zenodo.5442490

You can obtain all the workshop materials by using the green 'Clone or download' button above.

Owner

GitHub Events