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DTSTART;VALUE=DATE:20260407
DTEND;VALUE=DATE:20360409
DTSTAMP:20260415T065238
CREATED:20260413T113721Z
LAST-MODIFIED:20260413T120624Z
UID:10000610-1775520000-2091311999@prstats.org
SUMMARY:Python for Data Science and Statistical Computing (PYDSPR)
DESCRIPTION:
URL:https://prstats.org/course/python-for-data-science-and-statistical-computing-pydspr/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom
CATEGORIES:General Recorded Courses,Previously Recorded Courses
ATTACH;FMTTYPE=image/png:https://prstats.org/wp-content/uploads/2025/11/PYDS01.png
GEO:53.1423672;-7.6920536
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260409
DTEND;VALUE=DATE:20360410
DTSTAMP:20260415T065238
CREATED:20260409T161126Z
LAST-MODIFIED:20260410T080622Z
UID:10000606-1775692800-2091398399@prstats.org
SUMMARY:Mechanistic Species Distribution Modelling / Ecological Niche Modelling with NicheMapR (MSDMPR)
DESCRIPTION:
URL:https://prstats.org/course/mechanistic-species-distribution-modelling-ecological-niche-modelling-with-nichemapr-msdmpr/
CATEGORIES:Previously Recorded Courses,Spatial Ecology
ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2026/01/SDMS01-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260410
DTEND;VALUE=DATE:20360411
DTSTAMP:20260415T065238
CREATED:20260410T075640Z
LAST-MODIFIED:20260414T115745Z
UID:10000608-1775779200-2091484799@prstats.org
SUMMARY:Model Validation for Species Distribution and Ecological Niche Modelling (MVSDPR)
DESCRIPTION:
URL:https://prstats.org/course/model-validation-for-species-distribution-and-ecological-niche-modelling-mvsdpr/
CATEGORIES:Previously Recorded Courses,Spatial Ecology
ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2026/01/SDMS01-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260410
DTEND;VALUE=DATE:20360411
DTSTAMP:20260415T065238
CREATED:20260410T101455Z
LAST-MODIFIED:20260410T101646Z
UID:10000609-1775779200-2091484799@prstats.org
SUMMARY:Advanced Python for Ecologists and Evolutionary Biologists (APYBPR)
DESCRIPTION:
URL:https://prstats.org/course/advanced-python-for-ecologists-and-evolutionary-biologists-apybpr/
CATEGORIES:Molecular Ecology,Previously Recorded Courses
ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2025/04/AYPB01.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260413
DTEND;VALUE=DATE:20260418
DTSTAMP:20260415T065238
CREATED:20260126T121108Z
LAST-MODIFIED:20260127T163252Z
UID:10000582-1776038400-1776470399@prstats.org
SUMMARY:Machine Learning for Ecological Time Series (METR01)
DESCRIPTION:Python for Data Science and Statistical Computing (PYDSPR)\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nMonday\, September 22nd\, 2025\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCourse Program\nTIME ZONE – Spain (GMT+2) local time UTC+2 – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Details\n				The aim of the course is to introduce you to Bayesian inference using the integrated nested Laplace approximation (INLA) method and its associated R-INLA package for the analysis of spatial and spatio-temporal data. This course will cover the basics on the INLA methodology as well as practical modelling of different types of spatial and spatio-temporaldata. \nBy the end of the course participants should be able to: \n\nKnow the different types of spatial and spatio-temporal data available and how to work with them in R.\nKnow the different modelling approaches for spatial and spatio-temporal data.\nKnow how to visualize and produce maps of spatial and spatio-temporal data.\nBe able to fit models with the R-INLA package.\nKnow how to interpret the output from model fitting.\nBe confident with the use of INLA for data analysis.\nUnderstand the different models that can be fit with INLA to spatial and spatio-temporal data.\nKnow how to define the different parts of a model with INLA.\nHave the confidence to use INLA for their own projects.\n\n			\n				\n				\n				\n				\n				Intended Audiences\n				\nAcademics and post-graduate students working on projects related to spatial and spatio-temporal data analysis and modelling and who want to add the INLA methodology for Bayesian inference to their toolbox.\nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a command-line language such as R.\nThe course is designed for intermediate-to-advanced R users interested in data analysis and modelling. Ideally\, they should have some background on probability\, statistics and data analysis.\n\n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely\n			\n				\n				\n				\n				\n				Course Information\n				Time zone – Spain (GMT+2) local time \nAvailability – 20 places \nDuration – 5 days \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n  \nPLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n			\n				\n				\n				\n				\n				Teaching Format\n				\n\nThe course will be a mixture of theoretical and practical sessions. Each concept will be first described and explained\, and next there will be a time to exercise the topics using provided data sets. Participants are also very welcome to bring their own data. \n\n\n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				The course is designed for intermediate-to-advanced R users interested in Bayesian inference for data analysis and R beginners who have prior experience with Bayesian inference. Although an introduction to the INLA method will be given\, attendants are expected to be familiar with Bayesian inference. This includes how to define simple Bayesian models and have a basic understanding of some typical methods to compute or approximate the prior distributions (such as models with conjugate priors\, MCMC methods\, etc.). \n \n			\n				\n				\n				\n				\n				Assumed computer background\n				Attendees should already have experience with R and be familiar with data from different formats (csv\, tab\, etc.)\, create simple plots\, and manipulate data frames. Furthermore\, knowledge of how to fit generalized linear (mixed) models using typical R functions (such as glm and lme4) will be useful. No previous background on handling of spatial and spatio-temporal data will be assumed.\n			\n				\n				\n				\n				\n				Equipment and software requirements\n				\n\nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n\n\n  \n\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	\n		PYDSPR RECORDED\n	\n	PYDSPR RECORDED\n\n	\n		\n		\n				\n					£\n					300.00\n				\n						\n\n			\n			Unlimited	\n				\n			\n				Open the ticket description.\n				More			\n			\n				Close the ticket description.\n				Less			\n	\n	\n\n			\n			\n	Decrease ticket quantity for PYDSPR RECORDED\n	-\n		\n	\n		Quantity	\n	\n\n		\n	Increase ticket quantity for PYDSPR RECORDED\n	+\n		\n	\n				\n		\n\n		\n	\n		Quantity:	\n	0\n\n	\n	\n		Total:	\n	\n		\n				\n					£\n					0.00\n				\n				\n\n			\n	Get Tickets\n	\n\n	\n		\n	\n\n		\n	\n\n		\n	\n\n	\n\n\n\n\n\n	\n\n\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n			\n				\n				\n				\n				\n				If you are unsure about course suitability\, please get in touch by email to find out more \noliverhooker@prstatistics.com\n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Programme\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Monday 22nd\n				Day 1 – Classes from 14:00 to 21:00 \n\nSession 1 – Intro to INLA\nPractical 1 – Intro to INLA\nSession 2 – Model fitting with INLA\nPractical 2 – Model fitting with INLA\nSession 3 – GLMM’s with INLA\nPractical 3 – GLMM’s with INLA\nQ and A and end of day summary\n\n			\n				\n				\n				\n				\n				Tuesday 23rd\n				Day 2 – Classes from 14:00 to 21:00 \n\nSession 4 – Spatial Data\nPractical 4 – Spatial Data\nSession 5 – Spatio-Temporal Data\nPractical 5 – Spatio-Temporal Data\nSession 6 – Advanced Visualisation\nPractical 6 – Advanced Visualisation\nQ and A and end of day summary\n\n			\n				\n				\n				\n				\n				Wednesday 24th\n				Day 3 – Classes from 14:00 to 21:00 \n\nSession 7 – Spatial Models for Lattice Data\nPractical 7 – Spatial Models for Lattice Data\nSession 8 – Spatial Models for Continuous Data\nPractical 8 – Spatial Models for Continuous Data\nSession 9 – Spatial Models for Point Patterns\nPractical 9 – Spatial Models for Point Patterns\nQ and A and end of day summary\n\n			\n				\n				\n				\n				\n				Thursday 25th\n				Day 4 – Classes from 14:00 to 21:00 \n\nSession 10 – Spatio-Temporal Models for Lattice Data\nPractical 10 – Spatio-Temporal Models for Lattice Data\nSession 11 – Spatio-Temporal Models  for Continuous Data\nPractical 11 – Spatio-Temporal Models  for Continuous Data\nSession 12 – Spatio-Temporal Models  for Point Patterns\nPractical 12 – Spatio-Temporal Models  for Point Patterns\nQ and A and end of day summary\n\n			\n				\n				\n				\n				\n				Friday 26th\n				Day 5 – Classes from 14:00 to 21:00 \n\nCase studies\, own data and problem solving.\n\n			\n			\n				\n				\n				\n				\n				\n				\n					Dr Virgillio Gomez Rubio\n					\n					Virgilio has ample experience in Bayesian inference and statistical modeling as well as developing packages for the R programming language. His book Bayesian inference with INLA has been widely adopted for Bayesian modeling and it has been awarded the 2022 SEIO-BBVA Foundation Award in the category of Data Science and Big Data. You can find more information about him on here\n\n\nResearchgate\n\nGoogle Scholar\n\nORCID\n\nGitHub
URL:https://prstats.org/course/machine-learning-for-ecological-time-series-metr01/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.org/wp-content/uploads/2026/01/METR01.png
GEO:55.378051;-3.435973
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260414
DTEND;VALUE=DATE:20260416
DTSTAMP:20260415T065238
CREATED:20251121T182733Z
LAST-MODIFIED:20260213T113606Z
UID:10000564-1776124800-1776297599@prstats.org
SUMMARY:Deep Learning Using Python (DLUP01)
DESCRIPTION:Python for Data Science and Statistical Computing (PYDSPR)\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nTuesday\, 16th September\, 2025\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nThis is a ‘LIVE COURSE’ – the instructors will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCOURSE PROGRAM\n\nTIME ZONE – UK (GMT+1) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \n\n\nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				About This Course\n				Python is a dynamic\, readable language that is a popular platform for all types of  bioinformatics work\, from simple one-off scripts to large\, complex  software projects. This workshop aims to give novice programmers an introduction to using Python for research in evolutionary biology and genomics by using biological examples throughout. We will use example datasets and problems themed around sequence analysis\, taxonomy and ecology\, with plenty of time for participants to work on their own research data. \nThis workshop is aimed at complete beginners and assumes no prior programming experience. It gives an overview of the language with an emphasis on practical problem solving\, using examples and exercises drawn from various aspects of bioinformatics work. \nAfter completing the workshop\, students should be able to: \n\nApply the skills they have learned to tackling problems in their own research\nContinue their Python education in a self-directed way. All course materials (including copies of presentations\, practical exercises\, data files\, and example scripts prepared by the instructing team) will be provided electronically to participants.\n\n			\n				\n				\n				\n				\n				Intended Audiences\n				This workshop is aimed at all researchers and technical workers with a background in biology who want to learn programming. The syllabus has been planned with complete beginners in mind; people with previous programming experience are welcome to attend as a refresher but may find the pace a bit slow. If in doubt\, take a look at the detailed session content below or drop Martin Jones (martin@pythonforbiologists.com) an email. \nStudents should have enough biological background to appreciate the examples and exercise problems (i.e. they should know about DNA and protein sequences\, what translation is\, and what introns and exons are). No previous programming experience or computer skills (beyond the ability to use a text editor) are necessary\, but you’ll need to have a laptop with Python installed. \n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely \n			\n				\n				\n				\n				\n				Course Details\n				Time zone – UK (GMT+1) local time \nAvailability – 20 \nDuration – 4 days\, 8 hours per day \nContact hours – Approx. 28 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n			\n				\n				\n				\n				\n				Teaching Format\n				Lectures/discussions of Python code\, libraries and techniques delivered using interactive notebooks. Workshop/practical time for students to tackle carefully designed programming challenges that use the material from the discussion sessions. Usually followed up by discussion of solutions\, wrap up and summarisation. \n			\n				\n				\n				\n				\n				Assumed quantative knowledge\n				Little technical knowledge is assumed – we will be focussing more on applied problem-solving and less on statistics\, mathematics and interpretation. No maths is involved beyond basic addition/subtraction/powers etc. \n			\n				\n				\n				\n				\n				Assumed computer background\n				This course is suitable for complete beginners; all that is necessary is admin rights on a laptop in order to be able to install software. Pre course instructions will contain links to all software and data files that are necessary. \n			\n				\n				\n				\n				\n				Equipment and software requirements\n				\nA laptop computer with a working version of Python is required. Python is free and open-source software for PCs\, Macs\, and Linux computers.\n \nParticipants should be able to install additional software on their computers during the course (please ensure you have administration rights to your computer).\n\nAlthough not absolutely necessary\, a large monitor and a second screen could improve the learning experience. Participants are also encouraged to keep their webcams active to increase their interaction with the instructor and other students. \nDownload Python \n  \n\n\n  \n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	\n		PYDSPR RECORDED\n	\n	PYDSPR RECORDED\n\n	\n		\n		\n				\n					£\n					300.00\n				\n						\n\n			\n			Unlimited	\n				\n			\n				Open the ticket description.\n				More			\n			\n				Close the ticket description.\n				Less			\n	\n	\n\n			\n			\n	Decrease ticket quantity for PYDSPR RECORDED\n	-\n		\n	\n		Quantity	\n	\n\n		\n	Increase ticket quantity for PYDSPR RECORDED\n	+\n		\n	\n				\n		\n\n		\n	\n		Quantity:	\n	0\n\n	\n	\n		Total:	\n	\n		\n				\n					£\n					0.00\n				\n				\n\n			\n	Get Tickets\n	\n\n	\n		\n	\n\n		\n	\n\n		\n	\n\n	\n\n\n\n\n\n	\n\n\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\nPLEASE READ – CANCELLATION POLICY \n\n\nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited \n\n			\n				\n				\n				\n				\n				\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE PROGRAMME\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Tuesday 16th\n				Day 1  – Classes form 09:30 – 17:30 \nSession 1 : Introduction\, environment and text manipulation \n\nIn this session I introduce the students to Python and explain what we expect them to get out of it and how learning to program can benefit their research. I explain the format of the course and take care of any housekeeping details (like coffee breaks and catering arrangements). I outline the edit-run-fix cycle of software development and talk about how to avoid common text editing errors. In this session\, we also check that the computing infrastructure for the rest of the course is in place (e.g. making sure that everybody has an appropriate version of Python installed). Next\, students learn to write very simple programs that produce output to the terminal\, and in doing so become comfortable with editing and running Python code. This session also introduces many of the technical terms that we’ll rely on in future sessions. I run through some examples of tools for working with text and show how they work in the context of biological sequence manipulation. We also cover different types of errors and error messages\, and learn how to go about fixing them methodically. Core concepts introduced: terminals\, standard output\, variables and naming\, strings and characters\, special characters\, output formatting\, statements\, functions\, methods\, arguments\, comments.\n\nSession 2 : Files\, slices and user interfaces \n\nI introduce this session by talking about the importance of files in bioinformatics pipelines and workflows\, and we then explore the Python interfaces for reading from and writing to files. This involves introducing the idea of types and objects\, and a bit of discussion about how Python interacts with the operating system. We will also take a look at Python’s slice syntax\, which will play an important role later in the course once we introduce data structures. The practical session is spent combining the techniques from the first session with the file IO tools to create basic file processing scripts. Core concepts introduced: objects and classes\, paths and folders\, relationships between variables and values\, text and binary files\, newlines.\n\n			\n				\n				\n				\n				\n				Wednesday 17th\n				Day 2  – Classes form 09:30 – 17:30 \nSession 3 : Lists and loops \n\nIn this session we’ll start by thinking about the kinds of programs that we need to write for our research work. An important idea is that we want to write programs that can deal with arbitrary amounts of data. In order to do so\, we need two things: a way of *storing* large collections of values\, and a way of *processing* them. In Python\, **lists** and **loops** do these jobs respectively. We’ll go over the new syntax needed for each\, and see how together they allow us to write programs that are much closer to being useful in the real world. This new syntax will allow us to see how lists\, strings and files all share similar behaviour and how we can take advantage of that fact to write concise code. In the practical session we’ll tackle some problems that involve larger data files. Core concepts introduced: lists and arrays\, blocks and indentation\, variable scoping\, iteration and the iteration interface\, ranges.\n\nSession 4 : conditions and flow control \n\nWe will start this session by using the idea of decision making as a way to introduce conditional tests\, and outline the different building blocks of conditions before showing how conditions can be combined in an expressive way. We look at the different ways that we can use conditions to control program flow\, and how we can structure conditions to keep programs readable. These simple ideas combine with the material we have already covered to allow us to write programs that can follow rules and enforce logic. Correspondingly\, in the practical session we’ll be able to attempt some complex filtering challenges on a structured CSV file. Core concepts introduced: Truth and falsehood\, Boolean logic\, identity and equality\, evaluation of statements\, branching.\n\n			\n				\n				\n				\n				\n				Thursday 18th\n				Day 3 – Classes from 09:30 – 17:30 \nSession 5 : Organizing and structuring code \n\nWe’ll start off by discussing functions that we’d like to see in Python before considering how we can add to our computational toolbox by creating our own. We examine the nuts and bolts of writing functions before looking at best practice ways of making them usable. We also look at a couple of advanced features of Python – named arguments and defaults. This session ends with a first look at the concepts behind automated testing\, and the easiest way to get started with tests in Python. The practical session makes extensive use of automated testing\, with students writing functions to pass a series of unit tests. Core concepts introduced: argument passing\, encapsulation\, data flow through a program\, unit testing.\n\nSession 6 : The Python standard library and Regular expressions \n\nWe begin this sesion by browsing the documentation for the Python standard library and discussing how it fits in with the core parts of Python that we’ve already discussed\, along with other libraries of code that students may have already encountered. To explore how Python’s module system works in detail we will take a close look at one particular module: the one that deals with regular expressions. We’ll see how a range of common problems in bioinformatics can be described in terms of pattern matching\, and give an overview of Python’s regex tools. We look at the building blocks of regular expressions themselves\, and learn how they are a general solution to the problem of describing patterns in strings\, before practising writing some specific examples of regular expressions. Core concepts introduced: domain-specific languages\, modules and namespaces.\n\n			\n				\n				\n				\n				\n				Friday 19th\n				Day 4 – Classes form  09:30 – 17:30 \nSession 7 : Dictionaries \nAll of the data sets that we’ve considered so far in the course fit nicely into the list paradigm. In this session\, it’s time to introduce the second major data structure offered by Python: the dictionary. To do this\, we’ll look at a classic bioinformatics problem – kmer counting – and see how lists aren’t a good fit before learning the new syntax that we need to make dictionaries. Comparing the list and dictionary solutions will make it clear when we should use each approach. We’ll wrap up by discussing a few more examples of key-value data and see how the problem of storing them is a common one across bioinformatics and programming in general. In the practical session we will practice writing programs that create dictionaries\, and ones that use dictionaries\, including another classic bioinformatics problem: DNA to protein translation. Core concepts introduced: paired data types\, hashing\, key uniqueness\, argument unpacking and tuples. \nSession 8 : File management and housekeeping scripts \nThis session concerns a part of the Python standard library that is boring but useful – the modules concerned with file manipulation. We will cover the tools that Python gives us to automate the common repetitive housekeeping operations that are part of many bioinformatics projects\, but rarely make it into the final publication – things like renaming\, moving and deleting files\, creating folders\, etc. The notebook part of this session is quite brief\, giving us a generous amount of practical time to tackle an example of a bioinformatics data pre-processing problem: organizing a collection of DNA sequences by length. Although the problem can be stated very concisely\, we’ll quickly see that there are quite a few subtleties to it\, giving us a chance to think about program state of multiple runs\, processing multiple files\, and creating multiple output files. \n\n			\n			\n				\n				\n				\n				\n				\n				\n					Dr. Martin Jones\n					\n					Martin a freelance trainer specialising in teaching programming (mostly Python) and Linux skills to researchers in the field of biology. He trained as a biologist and completed his PhD in large-scale phylogenetics in 2007\, then held a number of academic positions at the University of Edinburgh ending in a two year stint as Lecturer in Bioinformatics. I launched Python for Biologists in 2015 and have been teaching and writing full-time ever since.
URL:https://prstats.org/course/deep-learning-using-python-dlup01/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.org/wp-content/uploads/2025/11/DLUP01.png
GEO:53.1423672;-7.6920536
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