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:earth_americas: Readings and resource materials for data science

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README 

          
# Data Science Readings and Resources



[![Check Resources](https://github.com/erictleung/review/actions/workflows/test.yml/badge.svg)](https://github.com/erictleung/review/actions/workflows/test.yml)



Curated resource materials from around the internet for data science, with applications in bioinformatics and computational biology and other domains, that I've found useful.



**Table of Contents**



- [Learning to Learn](#learning-to-learn)

- [Statistics and Probability](#statistics-and-probability)

  - [General Resources](#general-resources)

  - [R Friendly Resources](#r-friendly-resources)

  - [Specific Topics](#specific-topics)

  - [Interactive Articles](#interactive-articles)

  - [Data-related](#data-related)

- [Datasets](#datasets)

- [General mathematics](#general-mathematics)

- [Linear Algebra](#linear-algebra)

- [Network Science](#network-science)

- [Algorithms and Data Structures](#algorithms-and-data-structures)

- [Programming](#programming)

- [Structural Query Language (SQL)](#structural-query-language-sql)

- [Statistical Methods and Machine Learning](#statistical-methods-and-machine-learning)

- [Computational Biology](#computational-biology)

- [Domain Knowledge](#domain-knowledge)

- [Data Visualization and Making Figures](#data-visualization-and-making-figures)

- [Should-Read Data Science Papers](#should-read-data-science-papers)

- [Software Engineering](#software-engineering)

- [Reproducible Science](#reproducible-science)

- [People Skills and Communication](#people-skills-and-communication)

- [Other Lists](#other-lists)

- [License](#license)



## Learning to learn



> Resources and tips on how to self-learn and learn with others



- [The Thinker's Guide to The Art of Socratic Questioning (PDF)](https://www.criticalthinking.org/TGS_files/SocraticQuestioning2006.pdf) - A checklist of questions to help facilitate directed discussions on topics.

- [Questions for a Socratic Dialogue (PDF)](https://courses.cs.vt.edu/cs2104/Spring14McQuain/Notes/SocraticQ.pdf) - Nine types of questions that can be used to facilitate understanding.



## Statistics and Probability



> Statistics is the study of the collection, analysis, interpretation,

> presentation, and organization of data.

>

> — [Wikipedia](https://en.wikipedia.org/wiki/Statistics)



### General Resources



- [Handbook of Biological Statistics](http://www.biostathandbook.com/index.html) and [R Supplement](http://rcompanion.org/rcompanion/index.html) - Online set of notes from "Biological Data Analysis" course from University of Delaware.

- [Engineering Statistics Handbook](https://web.archive.org/web/20240924073012fw_/https://www.itl.nist.gov/div898/handbook/toolaids/pff/index.htm) - Handbook to help scientists and engineering incorporate statistical methods.

- [Stat Trek](http://stattrek.com/) - Teach yourself statistics.

- [Online Statistics Education](http://onlinestatbook.com/2/index.html) - Developed by Rice University, University of Houston Clear Lake, and Tufts University.

- [BS704 Probability](http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/BS704_Probability/index.html) - Boston University course on probability.

- [StatQuest](https://www.youtube.com/playlist?list=PLblh5JKOoLUIcdlgu78MnlATeyx4cEVeR) - Series of videos on miscellaneous complex topics such as p-values, principle component analysis (PCA), and R-squared.

- [STAT 505 Applied Multivariate Statistical Analysis](https://onlinecourses.science.psu.edu/stat505/) - Penn State Eberly College of Science course.

- [StatSoft Electronic Statistics Textbook](https://web.archive.org/web/20200221222547/http://www.statsoft.com/Textbook)

- [UW Summer Institutes Archive Material](https://si.biostat.washington.edu/about/archives) - Various learning

  material in statistics, data analysis, machine learning, genetics, and

  clinical research.

- [Practical Data Science for Stats][peerjstats] - Collection of curated articles

  on practical data science.

- [Statistics for Biologists](https://www.nature.com/collections/qghhqm) - Nature collection of articles on statistical analysis.

- [Top Upvoted Questions on CrossValidated][topcrossvotes] - Great questions with

  great answers about topics in statistics and machine learning.

- [Ordination Methods for Ecologists][ordokstate] - Resource of ordination

  methodology

- [Probability Cheatsheet][probcheat] - Compiled by William Chen and Joe

  Blitzstein

- [Statistics Done Wrong][wrongstats] - Reviews popular statistical errors and

  slip-ups committed by scientists every day.

- [Statistics for Hackers][statshacks] - By Jake VanderPlas (PyCon 2016)

- [Modern Statistics for Modern Biologists][msmb]

- [An Introduction to Statistical Learning](https://www.statlearning.com/) - With editions in R and Python.



[peerjstats]: https://peerj.com/collections/50-practicaldatascistats/

[topcrossvotes]: https://stats.stackexchange.com/questions?sort=votes

[ordokstate]: http://ordination.okstate.edu/

[probcheat]: https://github.com/wzchen/probability_cheatsheet

[wrongstats]: https://www.statisticsdonewrong.com/

[statshacks]: https://speakerdeck.com/jakevdp/statistics-for-hackers

[moderndive]: https://moderndive.com/

[msmb]: https://web.stanford.edu/class/bios221/book/



### R Friendly Resources



- [Quick-R](http://www.statmethods.net/) - Quick reference to statistical methods using R.

- [Statistical Inference via Data Science: A ModernDive into R and the Tidyverse (Second Edition)](https://moderndive.com/v2/) - Statistical analyses using modern tools in R and tidyverse.

- [UCLA IDRE Statistics](http://www.ats.ucla.edu/stat/) - Examples of statistical analyses using R, SAS, SPSS, and Stata.

- [r-statistics.so](http://r-statistics.co/) - Educational resource for machine learning and statistical computing in R.

- [W2024 Applied Linear Regression Analysis](https://web.archive.org/web/20181222115225/http://www.stat.columbia.edu/~martin/W2024/W2024.html)



### Python Friendly Resources



- [Python for Data Analysis, 3E](https://wesmckinney.com/book/) by Wes McKinney

- [Automate the Boring Stuff with Python](https://automatetheboringstuff.com/) by Al Sweigart

- [Think Python, 2nd Edition](https://greenteapress.com/wp/think-python-2e/) by Allen B. Downey

- [Think Stats 2E](https://greenteapress.com/wp/think-stats-2e/) by Allen B. Downey



### Specific Topics



- [P-values, False Discovery Rate (FDR) and q-values](http://www.nonlinear.com/support/progenesis/comet/faq/v2.0/pq-values.aspx)

- [FAQ: How do I interpret odds ratio in logistic regression?](http://www.ats.ucla.edu/stat/mult_pkg/faq/general/odds_ratio.htm)

- [Standard error of the mean of a sample binomial distribution][stderrbinom]

- [Common Probability Distributions: The Data Scientist's Crib

  Sheet][cribsheet] - Data scientists have hundreds of probability

  distributions from which to choose. Where to start?

- [Choosing the correct statistical test in SAS, Stata, SPSS, and

  R][chooseTest] - Table giving general guidelines on choosing statistical

  tests.

- [Warning Signs in Experimental Design and Interpretation][warnexp] - Nine

  common warning signs in experimental design and nine common warning signs in

  interpretation of experiments by Peter Norvig.

- [Univariate Distribution Relationships](http://archive.today/2020.05.19-101733/http://www.math.wm.edu/~leemis/chart/UDR/UDR.html) - An interactive, flow chart diagram showing the relationships between variate univariate distributions.

- [First Internet Gallery of Statistics Jokes](https://about.illinoisstate.edu/gcramsey/first-internet-gallery-of-statistics-jokes/)

- [PLoS's Ten Simple Rules for Effective Statistical Practice][ploseffective]

- [Common Statistical Pitfalls in Basic Science Research][commonpitfalls]

- [Review of Probability Theory - Maleki and Do][cs229prob] (PDF)

- [Effect Size FAQs][effectsize]

- [Common Mistakes in Using Statistics - Spotting Them and Avoiding

  Them][commonstats]

- [Common Statistical Tests are Linear Models (Or: How to Teach Stats)][statslinmod]

- [The Permutation Test - A Visual Explanation of Statistical Testing][permtest]

- [Visualising Residuals][vizres] - Using R and ggplot2.

- [Forecasting: Principles and Practice, 3rd Ed](https://otexts.com/fpp3/) by Rob J Hyndman and George Athanasopoulos



[stderrbinom]: http://stats.stackexchange.com/a/221102/132399

[cribsheet]: http://blog.cloudera.com/blog/2015/12/common-probability-distributions-the-data-scientists-crib-sheet

[chooseTest]: http://stats.idre.ucla.edu/other/mult-pkg/whatstat/

[warnexp]: http://norvig.com/experiment-design.html

[ploseffective]: https://doi.org/10.1371/journal.pcbi.1004961

[commonpitfalls]: https://doi.org/10.1161/JAHA.116.004142

[cs229prob]: http://cs229.stanford.edu/section/cs229-prob.pdf

[effectsize]: https://effectsizefaq.com/

[commonstats]: https://web.ma.utexas.edu/users/mks/CommonMistakes2016/commonmistakeshome2016.html

[statslinmod]: https://lindeloev.github.io/tests-as-linear/

[permtest]: https://www.jwilber.me/permutationtest/

[vizres]: https://web.archive.org/web/20190521215531/https://drsimonj.svbtle.com/visualising-residuals



### Interactive Articles



- [Interpreting Cohen's d effect size][cohensd]

- [Interpreting Correlations][correlation]

- [Interactive Machine Learning, Deep Learning and Statistics websites][interactivemldlstats]



[cohensd]: http://rpsychologist.com/d3/cohend/

[correlation]: http://rpsychologist.com/d3/correlation/

[interactivemldlstats]: https://p.migdal.pl/interactive-machine-learning-list/



### Data-related



- [Tidyverse](https://www.tidyverse.org/) - Opinionated collection of R packages designed for

  data science

- [Tidymodels](https://www.tidymodels.org/) - Framework that is a collection of packages for

  modeling and machine learning using tidyverse principles.

- [Text Mining with R](https://www.tidytextmining.com/) - A tidy approach to performing text analysis

  in R.

- [Cross-industry standard process for data mining - Wikipedia](https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining) - Open standard for common processes used in data mining, which can be applied to data science analyses.

- [The Limits of Data By C. Thi Nguyen](https://issues.org/limits-of-data-nguyen/) - Emphasizes the importance of understanding the context of your data and that it inherently has biases.

- [Build a Career in Data Science](https://bestbook.cool/) by Emily Robinson and Jacquelie Nolis - A guide on landing your first data science job and being a valued senior employee, rather than on just the technical details of how regression works. The authors also have [an accompanying podcast](https://podcast.bestbook.cool/).

- [ExcelDemy](https://www.exceldemy.com/) - Excel courses, tutorials, and templates.

- [Excel Easy](https://www.excel-easy.com/) - Excel tutorials and tips on functions and more.



## Datasets



> Biased toward real-world datasets that can be used to practice data science skills.



- [Our World in Data](https://ourworldindata.org/) - Research and data to make progress against the world’s largest problems.

- [#tidytuesday](https://github.com/rfordatascience/tidytuesday) - Weekly real-world datasets for the Data Science Learning Community to practice their data skills and data visualizations for collective learning.

- [Federal Reserve Economic Data (FRED)](https://fred.stlouisfed.org/) - Download, graph, and track 825,000 US and international time series from 114 sources for economic data since 1991.

- [Databases, Tables & Calculators from the U.S. Bureau of Labor Statistics](https://www.bls.gov/data/)



## General mathematics



> Resources generally related to learning and understand mathematical foundations



- [A Gentle Introduction to the Art of Mathematics](https://giam.southernct.edu/GIAM/GIAM.pdf) - Gentle introduction to basic mathematical notation, set theory, writing mathematical proofs, and mathematical thinking.



## Linear Algebra



> Linear algebra is the branch of mathematics concerning vector spaces and

> linear mappings between such spaces.

>

> — [Wikipedia](https://en.wikipedia.org/wiki/Linear_algebra)



- [Essence of Linear Algebra][essence] - Excellent, short overview of linear

  algebra concepts that help develop intuition on the matter.

- [MIT OCW 18.06SC Linear Algebra][linalgmit] - Taught by Gilbert Strang.

- [Linear algebra explained in four pages][linbull] - Excerpt from the No

  Bullshit Guide to Linear Algebra by Ivan Savov.

- [S.O.S. Mathematics Matrix Algebra][sosmath]

- [PCA, Eigenvectors, and Eigenvalues (Cross Validated)][eigenstats]

- [The Matrix Reference Manual][matmanual] - Reference information about

  linear algebra and the properties of real and complex matrices.

- [Linear Algebra Review and Reference - Kolter and Do (PDF)][cs229lin]

- [Immersive Linear Algebra][immersivelinalg]



[essence]: https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab

[linalgmit]: http://bit.ly/2cvRwMe

[linbull]: https://minireference.com/static/tutorials/linear_algebra_in_4_pages.pdf

[sosmath]: http://www.sosmath.com/matrix/matrix.html

[eigenstats]: https://stats.stackexchange.com/a/140579/

[matmanual]: http://www.ee.ic.ac.uk/hp/staff/dmb/matrix/intro.html

[cs229lin]: http://cs229.stanford.edu/section/cs229-linalg.pdf

[immersivelinalg]: http://immersivemath.com/ila/index.html



## Network Science



> Network science is an academic field which studies complex networks such as

> telecommunication networks, computer networks, biological networks, cognitive

> and semantic networks, and social networks, considering distinct elements or

> actors represented by *nodes* (or *vertices*) and the connections between the

> elements or actors as *links* (or *edges*).

>

> — [Wikipedia](https://en.wikipedia.org/wiki/Network_science)



- [Network Science Book][netbook] - Online book with visualizations and

  interactive tools about network science by Albert-László Barabási.

- [Graph Theory by Sarada Herke][graphherke] - YouTube series on graph theory.

- [Network Science][netsci] - Aggregate of all things network science resarch,

  introductions, people, journals, conferences, datasets, etc.

- [Handbook of Graphs and Networks in People Analytics][ona] - The second

  volume in a series of technical textbooks for professionals working in

  analytics

- [Awesome Network Analysis][ana] - Curated awesome list of network analysis

  resources



[netbook]: http://barabasi.com/networksciencebook/

[graphherke]: https://www.youtube.com/user/DrSaradaHerke/playlists?shelf_id=5&view=50&sort=dd

[netsci]: http://www.network-science.org/

[ona]: https://ona-book.org/

[ana]: https://github.com/briatte/awesome-network-analysis



## Algorithms and Data Structures



> In mathematics and computer science, an algorithm is a self-contained

> step-by-step set of operations to be performed.

>

> — [Wikipedia](https://en.wikipedia.org/wiki/Algorithm)

>

> In computer science, a data structure is a particular way of organizing

> and storing data in a computer so that it can be accessed and modified

> efficiently.

>

> — [Wikipedia](https://en.wikipedia.org/wiki/Data_structure)



- [Bioinformatic Algorithms][bioalg] - Algorithm lectures by Phillip Compeau

  and Pavel Pevzner.

- [Algorithms for DNA Sequencing][benalg] - Ben Langmead's lectures algorithms

  used in DNA sequencing.

- [Rosalind][rosa] - Learn bioinformatics and programming through problem

  solving.

- [VisuAlgo][visualgo] - Visualizing data structures and algorithms through

  animation.

- [Discrete Mathematics: An Open Introduction](https://discrete.openmathbooks.org/dmoi4.html)



[bioalg]: http://bioinformaticsalgorithms.com/videos.htm

[benalg]: https://www.youtube.com/playlist?list=PL2mpR0RYFQsBiCWVJSvVAO3OJ2t7DzoHA

[rosa]: http://rosalind.info/

[visualgo]: https://visualgo.net/en



## Programming



> Computer programming (often shortened to programming) is a process that leads

> from an original formulation of a computing problem to executable computer

> programs.

>

> — [Wikipedia](https://en.wikipedia.org/wiki/Computer_programming)



- [DevDocs][devdocs] - API documentation browser.

- [Hyperpolyglot][polyglot] - Commonly used features in programming languages

  in side-by-side format.

- [Learn X in Y Minutes][xiny] - Quick start to many programming languages, data

  structures, and common tools.

- [How to Report Bugs Effectively][reportbugs]

- [Rosetta Code][rosetta] - Programming chrestomathy site.

- [Cookbook for R][rcookbook] - Provide solutions to common tasks and problems

  in analyzing data.

- [OverAPI.com][openapi] - Collecting All Cheat Sheets..

- [The Art of Comments][csscomment] - Essay on how to comment well.

- [devhints.io][devhints] - Modest collection of cheatsheets.

- [Teach Yourself Programming in Ten Years][norvigprog]

- [Code Complete Book Review][codecomplete] - Detailed review and notes of book.

- [The Pragmatic Programmer Quick Reference][pragmaticcode]

- [Bash Pitfalls][bashpits] - Common errors that Bash programmers make, along

  with [Bash FAQs][bashfaqs] and general [Bash Programming][bashprogramming].

- [Tech Dev Guide][techguide] - By Google.

- [How to C in 2016][c2016]

- [explainshell][explainshell] - See help text that matches each argument.

- [Teach Yourself Computer Science][teachyourselfcs]

- [Competitive Programming Books][competecode]

- [Comprehensive Python Cheatsheet][comppy]

- [Practical Business Python][bizpy]

- [Full Stack Python](https://www.fullstackpython.com/) - Build, deploy and operate Python apps.



[devdocs]: https://devdocs.io/

[polyglot]: http://hyperpolyglot.org/

[xiny]: https://learnxinyminutes.com/

[reportbugs]: http://www.chiark.greenend.org.uk/~sgtatham/bugs.html

[rosetta]: https://rosettacode.org/wiki/Rosetta_Code

[rcookbook]: http://www.cookbook-r.com/

[openapi]: http://overapi.com/

[csscomment]: https://css-tricks.com/the-art-of-comments/

[devhints]: https://devhints.io/

[norvigprog]: http://norvig.com/21-days.html

[codecomplete]: http://codecourse.sourceforge.net/materials/Code-Complete-A-Detailed-Book-Review.html

[pragmaticcode]: https://blog.codinghorror.com/a-pragmatic-quick-reference/

[bashpits]: http://mywiki.wooledge.org/BashPitfalls

[bashprogramming]: https://mywiki.wooledge.org/BashProgramming

[bashfaqs]: https://mywiki.wooledge.org/BashFAQ

[techguide]: https://techdevguide.withgoogle.com/

[c2016]: https://matt.sh/howto-c

[explainshell]: https://explainshell.com/

[teachyourselfcs]: https://teachyourselfcs.com/

[competecode]: https://cses.fi/book/index.html

[comppy]: https://github.com/gto76/python-cheatsheet/

[bizpy]: https://pbpython.com/



## Structural Query Language (SQL)



> SQL is a domain-specific language used to manage data, especially in a relational database management system (RDBMS).

> 

> — [Wikipedia](https://en.wikipedia.org/wiki/SQL)



- [Select Star SQL](https://selectstarsql.com/) - Interactive online book with a non-toy dataset to learn SQL.

- [SQLZoo](https://sqlzoo.net/wiki/SQL_Tutorial) - Tutorials learning SQL step-by-step by function.

- [SQL Tutorial](https://www.sqltutorial.org/) - Quick access tutorials on SQL.



## Statistical Methods and Machine Learning



> Machine learning is the subfield of computer science that "gives computers

> the ability to learn without being explicitly programmed".

>

> — [Wikipedia](https://en.wikipedia.org/wiki/Machine_learning)



- [Naive Bayes Part 1][nb1] and [Naive Bayes Part 2][nb2]

- [How to choose a predictive model after k-fold cross-validation?][cvFold]

- [Parametric versus nonparametric bootstrap resampling][parNonparBootstrap]

- [Feature engineering using R][featureR]

- [How to Use t-SNE Effectively][tsne] - Interactive visualization to explore

  how tSNE behaves in order to use it more effectively.

- [Accurately Measuring Model Prediction Error][modelerr]

- [Understanding the Bias-Variance Tradeoff][biasvariance]

- [Random Forests][rfs] - Creator Leo Breiman's site on random forests.

- [Google's Machine Learning Crash Course][googleml] - Learn TensorFlow APIs.

- [Learning Math for Machine Learning by Vincent Chen][learnmath]

- [Calculus Made Easy][easycalc] (PDF)



[nb1]: https://youtu.be/XcwH9JGfZOU

[nb2]: https://youtu.be/k2diLn5Nqbs

[cvFold]: http://stats.stackexchange.com/a/52277/132399

[parNonparBootstrap]: http://stats.stackexchange.com/a/54855

[featureR]: https://blogs.msdn.microsoft.com/microsoftrservertigerteam/2017/03/23/feature-engineering-using-r/

[tsne]: http://distill.pub/2016/misread-tsne/

[modelerr]: http://scott.fortmann-roe.com/docs/MeasuringError.html

[biasvariance]: http://scott.fortmann-roe.com/docs/BiasVariance.html

[rfs]: https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm

[googleml]: https://developers.google.com/machine-learning/crash-course/

[learnmath]: https://blog.ycombinator.com/learning-math-for-machine-learning/

[easycalc]: http://djm.cc/library/Calculus_Made_Easy_Thompson.pdf



## Computational Biology



> Computational biology involves the development and application of

> data-analytical and theoretical methods, mathematical modeling and

> computational simulation techniques to the study of biological, behavior, and

> social systems.

>

> — [Wikipedia](https://en.wikipedia.org/wiki/Computational_biology)



- [RPKM measure is inconsistent among samples][rpkm]

- [RPKM-TPM.r][rpkm-tpm.r] - R script to show RPKM vs TPM

- [StatQuest: RPKM, FPKM and TPM][statquest]

- [Why do we use the negative binomial distribution for analysing RNAseq

  data?][negbionom]

- [QCFail.com][qcfail] - Articles about common next-generation sequencing

  problems

- [Differences between DESeq/edgeR and CuffDiff in RNA-seq][deseq-edger-cuff]

- [HarvardX Biomedical Data Science Open Online Training][harvardxbd2k]

- [Question: Can someone please explain in simple terms how DESeq2

  works?][biostardeseq2]

- [RNA-seqlopedia][rnauo] - Overview of RNA-seq and choices for a successful

  experiment.

- [Theory Behind DESeq2][deseqtheory]



[rpkm]: http://blog.nextgenetics.net/?e=51

[rpkm-tpm.r]: https://gist.github.com/johnstantongeddes/6925426

[statquest]: https://youtu.be/TTUrtCY2k-w

[negbionom]: http://bridgeslab.sph.umich.edu/posts/why-do-we-use-the-negative-binomial-distribution-for-rnaseq

[qcfail]: https://sequencing.qcfail.com/

[deseq-edger-cuff]: http://seqanswers.com/forums/archive/index.php/t-10797.html

[harvardxbd2k]: http://rafalab.github.io/pages/harvardx.html

[biostardeseq2]: https://www.biostars.org/p/127756/#127941

[rnauo]: https://rnaseq.uoregon.edu/

[deseqtheory]: http://www.bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#theory-behind-deseq2



## Data Visualization and Making Figures



> Data visualization or data visualisation is viewed by many disciplines as a

> modern equivalent of visual communication. It involves the creation and study

> of the visual representation of data, meaning "information that has been

> abstracted in some schematic form, including attributes or variables for the

> units of information".

>

> — [Wikipedia](https://en.wikipedia.org/wiki/Data_visualization)



- [A Compendium of Clean Graphs in R][cleangraphs]

- [How to Create Publication-Quality Figures][qualityfigs]

- [Make Better Figures Faster Using Illustrator][bitesizeillustrator]

- [A Tour Through the Visualization Zoo][vizzoo]

- [Adobe Illustrator for Scientists][ytillustrate] (YouTube playlist)

- [WebGraphviz is Graphviz in the Browser][webgraphviz]

- [Same Stats, Different Graphs: Generating Datasets with Varied Appearance

  and Identical Statistics through Simulated Annealing][samestats]

- [from Data to Viz][dattoviz] - Leads you to most appropriate graph for your

  data.

- [Beautiful plotting in R: A ggplot2 cheatsheet][zevrossggplot2]

- [Effectively Using Matplotlib][effectivemat]

- [Fundamentals of Data Visualization](https://clauswilke.com/dataviz/) by Claus O. Wilke

- [Practical Typography][practicaltypo] by Matthew Butterick

- [ditaa](https://github.com/stathissideris/ditaa) - Small command-line utility to convert diagrams using ASCII

  art

- [Asciiflow](http://asciiflow.com/) - GUI to easily create ASCII plain text diagrams

- [Hand drawn feel to diagrams](https://sankhs.com/shakydraw/)

- [10+ Guidelines for Better Tables in R (2020)](https://themockup.blog/posts/2020-09-04-10-table-rules-in-r/) - Notes on making better tables with accompanying R code

- [The Design Philosophy of Great Tables (2024)](https://posit-dev.github.io/great-tables/blog/design-philosophy/) - Design philosophy behind the the [great-tables](https://github.com/posit-dev/great-tables) Python package to generate effect tables of data

- [FriendsDontLetFriends](https://github.com/cxli233/FriendsDontLetFriends) - Opinionated essay about good and bad practices in data visualization with examples.

- [Visual Analysis Best Practices: A Guidebook - Tableau](https://www.tableau.com/learn/whitepapers/tableau-visual-guidebook?signin=c6cf87638b3864d1c393ffafb79ae10c) - List of techniques to turn data visualizations from good to great



[cleangraphs]: http://shinyapps.org/apps/RGraphCompendium/index.php

[qualityfigs]: http://b.nanes.org/figures/

[bitesizeillustrator]: https://bitesizebio.com/8113/make-better-figures-faster-using-illustrator/

[vizzoo]: https://homes.cs.washington.edu/~jheer/files/zoo/

[ytillustrate]: https://www.youtube.com/playlist?list=PLhKpKEPEAauYIsyjnIN2YXztNo7BrZVxQ

[webgraphviz]: http://www.webgraphviz.com/

[samestats]: https://www.autodeskresearch.com/publications/samestats

[dattoviz]: https://www.data-to-viz.com/

[zevrossggplot2]: https://web.archive.org/web/20220501085848/http://zevross.com/blog/2014/08/04/beautiful-plotting-in-r-a-ggplot2-cheatsheet-3/

[effectivemat]: http://pbpython.com/effective-matplotlib.html

[practicaltypo]: https://practicaltypography.com



## Should-Read Data Science Papers



> Data science, computational biology, and bioinformatics papers to cover the breadth of their fields.



- [applied-ml](https://github.com/eugeneyan/applied-ml) by Eugene Yan - Curated papers, articles, and blogs on data science and machine learning in production.

- [List of important publications in data science - Wikipedia](https://en.wikipedia.org/wiki/List_of_important_publications_in_data_science)

- [How to read a research paper](http://www.theexclusive.org/2017/11/read-a-paper.html) - One question to ask when reading papers.

- [Zhang Lab Recommendations](http://zhanglab.ccmb.med.umich.edu/literature/)

- [The Leek group guide to genomics papers](https://github.com/jtleek/genomicspapers)

- ["Foundations of Computational and Systems Biology" Readings](https://ocw.mit.edu/courses/biology/7-91j-foundations-of-computational-and-systems-biology-spring-2014/readings/) - MIT OCW course readings.

- [Question: What Are The Classic Papers In Bioinformatics?](https://www.biostars.org/p/3204/)

- [Best Academic Papers About the Microbiome](http://www.richardsprague.com/note/2017/10/16/best-academic-papers-about-the-microbiome/)

- [Staying Current in Bioinformatics & Genomics: 2017 Edition](https://gettinggeneticsdone.blogspot.com/2017/02/staying-current-in-bioinformatics-genomics-2017.html) by Stephen Turner

- [RNA-Seq Analysis, Differential Gene Expression, and Functional Enrichment Analysis](http://diytranscriptomics.com/) (Recent removal of readings page, but course overall is valuable)



> General knowledge mapping and exploration tools



- [Inciteful](https://inciteful.xyz/) - Tools to help you accelerate your research



## Software Engineering



> Software engineering is the application of engineering to the development of

> software in a systematic method.

>

> — [Wikipedia](https://en.wikipedia.org/wiki/Software_engineering)



- ["The Guide to the Software Engineering Body of Knowledge"][swebook]

- [Software Engineering - Ian Sommerville][sommerville]

- [Unix as IDE Series][unixide]

- [Software Engineering Resources][spiresources] - Aggregation of over 1800

  software engineering resources on various topics.

- [Flowchart Symbols Explained][smartdraw]

- [Write the Docs][writethedocs] - A global community of people who care

  about documentation.

- [Amazon Web Services - A Practical Guide][awspractical]

- [Amazon Web Services in Plain English][awsenglish]

- [Command-line Tools can be 235x Faster than your Hadoop Cluster](https://adamdrake.com/command-line-tools-can-be-235x-faster-than-your-hadoop-cluster.html) - Simple but effective demonstration of using the right tool for the right amount of data.



[swebook]: https://www.computer.org/web/swebok

[sommerville]: http://iansommerville.com/software-engineering-book/

[unixide]: https://sanctum.geek.nz/arabesque/series/unix-as-ide/

[spiresources]: http://rspa.com/spi/

[smartdraw]: https://www.smartdraw.com/flowchart/flowchart-symbols.htm

[writethedocs]: http://www.writethedocs.org/

[awspractical]: https://github.com/open-guides/og-aws

[awsenglish]: https://www.expeditedssl.com/aws-in-plain-english



## Reproducible Science



> Reproducibility is the ability to get the same research results using the

> raw data and computer programs provided by the researchers.

>

> — [Wikipedia](https://en.wikipedia.org/wiki/Reproducibility)



- [A statistical definition for reproducibility and replicability](https://doi.org/10.1101/066803)

- [`scifigure`: Visualize Reproducibility and Replicability in a Comparison of Scientific Studies](https://cran.r-project.org/package=scifigure) (R package)

- [What should Researchers Expect When They Replicate Studies? A Statistical View of Replicability in Psychological Science](https://doi.org/10.1177/1745691616646366)

- [A Guide to Reproducible Code in Ecology and Evolution](https://archive.org/details/bes-guide-reproducible-code/mode/2up) (PDF)

- [Docker for Beginners](https://docker-curriculum.com/) - By Prakhar Srivastav.

- [Riffomonas: Reproducible Research](https://www.youtube.com/playlist?list=PLmNrK_nkqBpL0d2E26TqPkmTAfelYKbQX) - By Patrick D. Schloss.



## People Skills and Communication



> People skills are patterns of behavior and behavioral interactions.

> Among people, it is an umbrella term for skills under three related

> set of abilities: personal effectiveness, interaction skills, and

> intercession skills.

>

> — [Wikipedia](https://en.wikipedia.org/wiki/People_skills)



- [How to ask good questions][questionevans] - By Julia Evans.

- [How To Ask Questions The Smart Way][raymondquestions] - By Eric Raymond.

- [Teaching Tech Together][teachingtech] - By Greg Wilson.

- [(An Opionionated Talk) On Preparing Good Talks][jhalatalk] (PDF) - By Ranjit Jhala.

- [CommKit][commkit] - By MIT's Department of Biological Engineering Communication

  Fellows on successfull scientific communication.

- [General Principles of Mathematical Communication][maacomm] - By Mathematical

  Association of America.

- [Community Tool Box][commtoolbox] - By University of Kansas.

- [Speech-Words to Minutes][speechtime] - Estimate how many words are need for a given

  timed speech.

- [Novelist Cormac McCarthy's tips on how to write a great science paper][scicommmccarthy]

  The Pulitzer prizewinner shares his advice for pleasing readers, editors and yourself.

- [Science Writing: Guidelines and Guidelines][scicommzimmer] - Notes from Carl Zimmer

  on writing about science, medicine, and the environment.

- [Write the Paper First][writepaper] - Argues that "writing now is a favor to yourself"

  and the benefits of clear writing for organizing thoughts early.



[questionevans]: https://jvns.ca/blog/good-questions/

[raymondquestions]: http://www.catb.org/~esr/faqs/smart-questions.html

[teachingtech]: http://teachtogether.tech/

[jhalatalk]: https://ranjitjhala.github.io/static/PLMW-talk-opinionated.pdf

[commkit]: http://mitcommlab.mit.edu/be/use-the-commkit/

[maacomm]: http://mathcomm.org/general-principles-of-communicating-math/

[commtoolbox]: https://ctb.ku.edu/en

[speechtime]: http://www.speechinminutes.com/

[scicommmccarthy]: https://www.nature.com/articles/d41586-019-02918-5

[scicommzimmer]: https://carlzimmer.com/science-writing-guidelines-and-guidance/

[writepaper]: https://www.cs.jhu.edu/~jason/advice/write-the-paper-first.html



## Other Lists



> Useful lists on their own that may intersect other topics above.



- [Awesome Design Tools][adt]

- [Fred's ImageMagick Scripts][imagemagickscripts]



[adt]: https://github.com/LisaDziuba/Awesome-Design-Tools

[imagemagickscripts]: http://www.fmwconcepts.com/imagemagick/index.php



## License



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