BEGIN:VCALENDAR VERSION:2.0 PRODID:-//PR Statistics - ECPv6.10.0//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:PR Statistics X-ORIGINAL-URL:https://prstats.preprodw.com X-WR-CALDESC:Events for PR Statistics REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:Europe/London BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20210328T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20211031T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20220327T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20221030T010000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;VALUE=DATE:20220504 DTEND;VALUE=DATE:20220506 DTSTAMP:20260419T052230 CREATED:20220218T162532Z LAST-MODIFIED:20220316T135034Z UID:10000348-1651622400-1651795199@prstats.preprodw.com SUMMARY:ONLINE COURSE – Introduction To Scientific\, Numerical\, And Data Analysis Programming In Python (PYSC03) This course will be delivered live DESCRIPTION:ONLINE COURSE – Reproducible and collaborative data analysis with R (RACR03) This course will be delivered live\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nWednesday\, May 4th\, 2022\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. \nTime Zone\nTIME ZONE – GMT – 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 About This Course\n Python is one of the most widely used and highly valued programming languages in the world\, and is especially widely used in data science\, machine learning\, and in other scientific computing applications. In order to use Python confidently and competently for these applications\, it is necessary to have a solid foundation in the fundamentals of scientific\, numerical\, and data analysis programming Python. This two day course provides a general introduction to numerical programming in Python\, particularly using numpy\, data processing in Python using Pandas\, data analysis in Python using statsmodels and rpy2. We will also cover the major data visualization and graphics tools in Python\, particularly matplotlib\, seaborn\, and ggplot. Finally\, we will cover some other major scientific Python tools\, such as for symbolic mathematics and parallel programming and code acceleration. Note that in this course\, we will not be teaching Python fundamentals and general purpose programming\, but this knowledge will be assumed\, and is also provided in a preceding two-day course. \n \n \n \n \n \n Intended Audiences\n This course is aimed at anyone who is interested in learning the fundamentals of Python generally and especially for ultimately using Python for data science and scientific applications. Although these applications are not covered directly here\, but are covered in a subsequent course\, the fundamentals taught here are vital for master data science and scientific applications of Python.\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Details\n Availability – TBC \nDuration – 2 days \nContact hours – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n This course will be hands-on and workshop based. Throughout each day\, there will be some brief introductory remarks for each new topic\, introducing and explaining key concepts. \nThe course will take place online using Zoom. On each day\, the live video broadcasts will occur between (UK local time) at:• 10am-12pm• 1pm-3pm• 4pm-6pm \nAll sessions will be video recorded and made available to all attendees as soon as possible\, hopefully soon after each 2hr session. Attendees in different time zones will be able to join in to some of these live broadcasts\, even if all of them are not convenient times. By joining any live sessions that are possible\, this will allow attendees to benefit from asking questions and having discussions\, rather than just watching prerecorded sessions. Although not strictly required\, using a large monitor or preferably even a second monitor will make the learning experience better. All the sessions will be video recorded\, and made available immediately on a private video hosting website. Any materials\, such as slides\, data sets\, etc.\, will be shared via GitHub. \n \n \n \n \n \n Assumed quantitative knowledge\n We will assume familiarity with some general statistical and mathematical concepts such as matrix algebra\, calculus\,probability distributions. However\, expertise with these concepts are not necessary. Anyone who has taken anyundergraduate (Bachelor’s) level course in mathematics\, or even advanced high school level\, can be assumed to havesufficient familiarity with these concepts. \n \n \n \n \n \n Assumed computer background\n We assume familiarity with using Python and knowledge of general purpose programming in Python. This topics are covered comprehensively in a preceding two-day course\, which will provide all the prerequisites for this course. \n \n \n \n \n \n Equipment and software requirements\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 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 Tickets are no longer available\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 \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\nWednesday 4th – Classes from 10:00 to 18:00 \nTopic 1: Numerical programming with numpy. Although not part of Python’s official standard library\, the numpy package is the part of the de facto standard library for any scientific and numerical programming. Here we will introduce numpy\, especially numpy arrays and their built in functions (i.e. “methods”). Here\, we will also consider how to speed up numpy code using the Numba just-in-time compiler. \nTopic 2: Data processing with pandas. The pandas library provides means to represent and manipulate data frames. Like numpy\, pandas can be see as part of the de facto standard library for data oriented uses of Python. Here\, we will focus on data wrangling including selecting rows and columns by name and other criteria\, applying functions to the selected data\, aggregating the data. For this\, we will use Pandas directly\, and also helper packages like siuba. \nThursday 5th – Classes from 10:00 to 18:00 \nTopic 3: Data Visualization. Python provides many options for data visualization. The matplotlib library is a low level plotting library that allows for considerable control of the plot\, albeit at the price of a considerable amount ofm low level code. Based on matplotlib\, and providing a much higher level interface to the plot\, is the seaborn library. This allows us to produce complex data visualizations with a minimal amount of code. Similar to seaborn is ggplot\, which is a direct port of the widely used R based visualization library. \nTopic 4: Statistical data analysis. In this section\, we will describe how to perform widely used statistical analysis in Python. Here we will start with the statsmodels\, which provides linear and generalized linear models as well as many other widely used statistical models. We will also cover rpy2\, which is and interface from Python to R. This allows us to access all of the the power of R from within Python. \nTopic 5: Symbolic mathematics. Symbolic mathematics systems\, also known as computer algebra systems\, allow us to algebraically manipulate and solve symbolic mathematical expression. In Python\, the principal symbolic mathematics library is sympy. This allows us simplify mathematical expressions\, compute derivatives\, integrals\, and limits\, solve equations\, algebraically manipulate matrices\, and more. \nTopic 6: Parallel processing. In this section\, we will cover how to parallelize code to take advantage of multiple processors. While there are many ways to accomplish this in Python\, here we will focus on the multiprocessing \n \n \n \n \n \n Course Instructor\n \n\n\n\nDr. Mark Andrews\n\nWorks AtSenior Lecturer\, Psychology Department\, Nottingham Trent University\, England \n\nTeaches\nFree 1 day intro to r and r studio (FIRR)\nIntroduction To Statistics Using R And Rstudio (IRRS03)\nIntroduction to generalised linear models using r and rstudio (IGLM)\nIntroduction to mixed models using r and rstudio (IMMR)\nNonlinear regression using generalized additive models (GAMR)\nIntroduction to hidden markov and state space models (HMSS)\nIntroduction to machine learning and deep learning using r (IMDL)\nModel selection and model simplification (MSMS)\nData visualization using gg plot 2 (r and rstudio) (DVGG)\nData wrangling using r and rstudio (DWRS)\nReproducible data science using rmarkdown\, git\, r packages\, docker\, make & drake\, and other tools (RDRP)\nIntroduction/fundamentals of bayesian data analysis statistics using R (FBDA)\nBayesian data analysis (BADA)\nBayesian approaches to regression and mixed effects models using r and brms (BARM)\nIntroduction to stan for bayesian data analysis (ISBD)\nIntroduction to unix (UNIX01)\nIntroduction to python (PYIN03)\nIntroduction to scientific\, numerical\, and data analysis programming in python (PYSC03)\nMachine learning and deep learning using python (PYML03)\nPython for data science\, machine learning\, and scientific computing (PDMS02)\n\n  \nPersonal website \n\n\nResearchGate \nGoogle Scholar \nMark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham\, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition\, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis\, particularly as applied to data from the social and behavioural sciences. Since 2015\, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences. URL:https://prstats.preprodw.com/course/introduction-to-scientific-numerical-and-data-analysis-programming-in-python-pysc03/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/PYSC03.png GEO:53.1423672;-7.6920536 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20220427 DTEND;VALUE=DATE:20220429 DTSTAMP:20260419T052230 CREATED:20220224T223604Z LAST-MODIFIED:20220329T153816Z UID:10000397-1651017600-1651190399@prstats.preprodw.com SUMMARY:ONLINE COURSE – Introduction To Python And Programming In Python (PYIN03) This course will be delivered live DESCRIPTION:ONLINE COURSE – Reproducible and collaborative data analysis with R (RACR03) This course will be delivered live\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nWednesday\, April 20th\, 2022\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. \nTime Zone\nTIME ZONE – GMT – 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 About This Course\n \nPython is one of the most widely used and highly valued programming languages in the world\, and is especially widely used in data science\, machine learning\, and in other scientific computing applications. In order to use Python confidently and competently for these applications\, it is necessary to have a solid foundation in the fundamentals of general purpose Python. This two day course provides a general introduction to the Python environment\, the Python language\, and general purpose programming in Python. We cover how to install and set up a Python computing environment\, describing how to set virtual environments\, how to use Python package installers\, and overview some Python integrated development environments (IDE) and Python Jupyter notebooks. We then provide a comprehensive introduction to programming in Python\, covering all the following major topics: data types and data container types\, conditionals\, iterations\, functional programming\, object oriented programming\, modules\, packages\, and imports. Note that in this course\, we will not be covering numerical and scientific programming in Python directly. That is provided in a subsequent two-day course\, for which the topics covered in this course are a necessary prerequisite. \n\n \n \n \n \n \n Intended Audiences\n \nThis course is aimed at anyone who is interested in learning the fundamentals of Python generally and especially for ultimately using Python for data science and scientific applications. Although these applications are not covered directly here\, but are covered in a subsequent course\, the fundamentals taught here are vital for master data science and scientific applications of Python. \n\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Details\n Availability – TBC \nDuration – 2 days \nContact hours – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English\n \n \n \n \n \n Teaching Format\n This course will be hands-on and workshop based. Throughout each day\, there will be some brief introductory remarks for each new topic\, introducing and explaining key concepts. \nThe course will take place online using Zoom. On each day\, the live video broadcasts will occur between (UK local time) at:\n• 10am-12pm\n• 1pm-3pm\n• 4pm-6pm \nAll sessions will be video recorded and made available to all attendees as soon as possible\, hopefully soon after each 2hr session. Attendees in different time zones will be able to join in to some of these live broadcasts\, even if all of them are not convenient times. By joining any live sessions that are possible\, this will allow attendees to benefit from asking questions and having discussions\, rather than just watching prerecorded sessions. Although not strictly required\, using a large monitor or preferably even a second monitor will make the learning experience better. All the sessions will be video recorded\, and made available immediately on a private video hosting website. Any materials\, such as slides\, data sets\, etc.\, will be shared via GitHub.\n \n \n \n \n \n Assumed quantitative knowledge\n No particular knowledge of mathematics or statistics is required.\n \n \n \n \n \n Assumed computer background\n \nNo prior experience with Python or any other programming language is required. Of course\, any familiarity with any other programming will be helpful\, but is not required. \n\n \n \n \n \n \n Equipment and software requirements\n \nAttendees of the course must use a computer with Python (version 3) installed. This can in fact be done entirely online for free using Google’s Colaboratory without needing to install any software on your own laptop or desktop. If you are new to Python\, this approach is highly recommended. You will be able to immediately starting learning Python without any installation or configuration of software. This entire course can be done using this approach. \nIf you prefer to install and use Python on your machine\, instructions on how to install and configure all the software needed for this course are provided here. We will also provide time during the workshops to ensure that all software is installed and configured properly. \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 Tickets are no longer available\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 \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\nWednesday 27th \nClasses from 10:00 to 18:00 \nTopic 1: Installing and setting up Python. There are many ways to write and execute code in Python. Which to use depends on personal preference and the type of programming that is being done. Here\, we will explore some of the commonly used Integrated Development Environments (IDE) for Python\, which include Spyder and PyCharm. Here\, we will also introduce Jupyter notebooks\, which are widely used for scientific applications of Python\, and are an excellent tool for doing reproducible interactive work. Also as part of this topic\, we will describe how to use virtual environments and package installers such as pip and conda. \nTopic 2: Data Structures. We will begin our coverage of programming with Python by introducing its different data structures.and operations on data structures This will begin with the elementary data types such as integers\, floats\, Booleans\, and strings\, and the common operations that can be applied to these data types. We will then proceed to the so-called collection data structures\, which primarily include lists\, dictionaries\, tuples\, and sets. \nTopic 3: Programming I. Having introduced Python’s data types\, we will now turn to how to program in Python. We will begin with iteration\, such as the for and while Here\, we also cover some of Python’s functional programming features\, specifically list\, dictionary\, and set comprehensions. \nThursday 28th \nClasses from 10:00 to 18:00 \nTopic 4: Programming II. Having covered iterations\, we now turn to other major programming features in Python\, specifically\, conditionals\, functions\, and exceptions. \nTopic 5: Object Oriented Programming. Python is an object oriented language and object oriented programming in Python is extensively used in anything beyond the very simplest types of programs. Moreover\, compared to other languages\, object oriented programming in Python is relatively easy to learn. Here\, we provide a comprehensive introduction to object oriented programming in Python. \nTopic 6: Modules\, packages\, and imports. Python is extended by hundreds of thousands of additional packages. Here\, we will cover how to install and import these packages\, but more importantly\, we will show how to write our own modules and packages\, which is remarkably easy in Python relative to some programming languages. \n \n \n \n \n \n Course Instructor\n \n\n\n\nDr. Mark Andrews\n\nWorks At\nSenior Lecturer\, Psychology Department\, Nottingham Trent University\, England \n\nTeaches\nFree 1 day intro to r and r studio (FIRR)\nIntroduction To Statistics Using R And Rstudio (IRRS03)\nIntroduction to generalised linear models using r and rstudio (IGLM)\nIntroduction to mixed models using r and rstudio (IMMR)\nNonlinear regression using generalized additive models (GAMR)\nIntroduction to hidden markov and state space models (HMSS)\nIntroduction to machine learning and deep learning using r (IMDL)\nModel selection and model simplification (MSMS)\nData visualization using gg plot 2 (r and rstudio) (DVGG)\nData wrangling using r and rstudio (DWRS)\nReproducible data science using rmarkdown\, git\, r packages\, docker\, make & drake\, and other tools (RDRP)\nIntroduction/fundamentals of bayesian data analysis statistics using R (FBDA)\nBayesian data analysis (BADA)\nBayesian approaches to regression and mixed effects models using r and brms (BARM)\nIntroduction to stan for bayesian data analysis (ISBD)\nIntroduction to unix (UNIX01)\nIntroduction to python (PYIN03)\nIntroduction to scientific\, numerical\, and data analysis programming in python (PYSC03)\nMachine learning and deep learning using python (PYML03)\nPython for data science\, machine learning\, and scientific computing (PDMS02)\n\n  \nPersonal website\n\nResearchGate \nGoogle Scholar \nMark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham\, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition\, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis\, particularly as applied to data from the social and behavioural sciences. Since 2015\, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences. URL:https://prstats.preprodw.com/course/introduction-to-python-and-programming-in-python-pyin03/ 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.preprodw.com/wp-content/uploads/2022/02/PYIN03R.png GEO:53.1423672;-7.6920536 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20220214 DTEND;VALUE=DATE:20220219 DTSTAMP:20260419T052230 CREATED:20190808T160414Z LAST-MODIFIED:20221019T153619Z UID:10000300-1644796800-1645228799@prstats.preprodw.com SUMMARY:ONLINE COURSE - GIS And Remote Sensing Analyses With R (GARM01) This course will be delivered live DESCRIPTION:ONLINE COURSE – Reproducible and collaborative data analysis with R (RACR03) This course will be delivered live\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, February 14th\, 2022\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. \nTime Zone\nTIME ZONE – Western European Standard Time – 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 About This Course\n The course will cover the basics to perform spatial analyses using R as a Geographical Information System (GIS) platform and Remote Sensing as main data source. The course will provide a brief theoretical background of GIS tools and Remote Sensing data and techniques. By the end of this 4-day practical course\, attendees will have the capacity to search satellite imagery\, to manipulate Remote Sensing data\, to create new variables\, as well as to choose the best spatial tools and techniques to perform spatial analyses and interpret their results. \nThe course will be mainly practical\, with some theoretical lectures. All modelling processes and calculations will be performed with R\, the free software environment for statistical computing and graphics (http://www.r-project.org/). Attendees will learn to use the Rpackage RSToolbox for Remote Sensing image processing and analysis such as calculating spectral indices\, principal component transformation\, or unsupervised and supervised classification. \n \n \n \n \n \n Intended Audiences\n This course is orientated to PhD and MSc students\, as well as other students and researchers working on biogeography\, spatial ecology\, or related disciplines. \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Availability – 25 places \nDuration – 4 days \nContact hours – Approx. 28 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n Introductory lectures on the concepts and applications of GIS and Remote Sensing.Practical lectures on most used spatial tools. Presentations and round-table discussions about the analysis requirements of attendees (option for them to bring their own data). Data sets for computer practical modules will be provided by the instructor\, but participants are welcome to bring their own data. \n \n \n \n \n \n Assumed quantitative knowledge\n Basic knowledge in Geographical Information Systems\, Remote Sensing\, and spatial analyses. \n \n \n \n \n \n Assumed computer background\n Familiarity with R. Ability to import/export data\, manipulate data frames\, fit basic statistical models & generate simple exploratory and diagnostic plots. \n \n \n \n \n \n Equipment and software requirements\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 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 Tickets are no longer available\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 oliverhooker@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 21st\n Classes from 09:00 to 17:00Theory – Introduction to GIS.Practical – Introduction to GIS with R: Import and plot data.Theory – Coordinate systems.Practical – Projecting vectorial & raster files. \n \n \n \n \n \n Tuesday 22nd\n Classes from 09:30 – 17:00Theory – Vector database operations.Practical – Attribute and spatial queries: join/merge\, filter/subset\, select by attribute\, select bylocation\, summarize\, add/calculate new attributes (columns)\, plot attributes.Theory – Vector analyses.P: Vector analyses – buffer\, merge\, dissolve\, intersect\, union\, select\, calculate areas. \n \n \n \n \n \n Wednesday 23rd\n Classes from 09:30 – 17:00Theory – Raster GIS.Practical – Raster analyses: rasterize\, crop\, mask\, merge\, distance surface\, zonal statistics.Theory – Introduction to Remote Sensing. RS as main data source: RS sensors & variables.RS software.Practical – Getting and plotting RS data. Downloading\, reading\, and plotting RS data in R.Manipulating satellite data. \n \n \n \n \n \n Thursday 24th\n Classes from 09:30 – 17:00Theory – Working with RS variables. Image classification\, Vegetation indexes\, data fusion.Practical – Calculating RS variables with RStoolbox: Vegetation indexes and classificationmethods.Theory: Remote Sensing applications to biologyPractical: Statistical analyses with RS data. \n \n \n \n \n \n \n \n \n Dr. Neftali Sillero\n \n Neftalí Sillero works in the analysis and identification of biodiversity spatial patterns\, from species to populations and individuals. For this\, he uses four powerful tools to better understand how space influence biodiversity: Geographical Information Systems\, Remote Sensing\, Ecological Niche Modelling\, and Spatial Statistics. His main areas of research are: application of new technologies on species’ distributions atlases\, ecological modelling of species’ ranges\, identification of biogeographical regions and species’ chorotypes\, mapping and modelling road-kill hotspots\, and spatial analyses of home ranges. \nHe has more than 10 years’ experience working in ecological niche models. He has authored >70 peer reviewed publications and he is since 2007 Chairman of the Mapping Committee of the Societas Herpetologica Europaea\, where he is the PI of the NA2RE project (www.na2re.ismai.pt)\, the New Atlas of Amphibians and Reptiles of Europe \nPersonal websiteWork WebpageResearchGateGoogleScholar \n \n \n \n \n \n \n \n \n \n \n \n \n Teaches\n \nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n \n \n \n \n \n Teaches\n \nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM) URL:https://prstats.preprodw.com/course/gis-and-remote-sensing-analyses-with-r-garm01/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/GARM01R.png GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20211213 DTEND;VALUE=DATE:20211218 DTSTAMP:20260419T052230 CREATED:20220425T145328Z LAST-MODIFIED:20220804T114533Z UID:10000408-1639353600-1639785599@prstats.preprodw.com SUMMARY:ONLINE COURSE - Remote Sensing With Aircraft And Drone LiDAR Sensors (RSLD01) This course will be delivered live DESCRIPTION:ONLINE COURSE – Reproducible and collaborative data analysis with R (RACR03) This course will be delivered live\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, December 12th\, 2021\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. \nTime Zone\nTIME ZONE – GMT+1 – 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 About This Course\n \nUnmanned Airborne Vehicles (UAVs) equipped with consumer-grade imaging/ranging and direct geo-referencing systems have been proven as a potential Remote Sensing platform that could satisfy the needs of a wide range of civilian applications. The continuous developments in direct georeferencing and Remote Sensing (i.e.\, passive and active imaging sensors in the visible and infrared range – RGB cameras and LiDAR) is providing the professional geospatial community with ever-growing opportunities to provide accurate 3D information used in environmental research to collect information about the Earth\, such as vegetation and tree species. \n\n\nThis 4-day course aims to provide participants with an integrated \n\n\nend-to-end perspective going from measurement techniques to end- \n\n\nuser applications\, covering issues related to LiDAR sensors coupled on aircraft and UAVs\, computing exercises on the processing of 3D point clouds to produce geospatial products. \n\n \n \n \n \n \n Intended Audiences\n \nAny researchers (PhD and MSc students\, post-docs\, primary investigators) and environmental professionals who are specialised in a variety of Earth Science disciplines and wish to expand and improve their knowledge and skills. \n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Availability – 30 places \nDuration – 4 days \nContact hours – Approx. 24 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n \nThe course will be divided into theoretical lectures to introduce and explain key concepts and theories\, and practices with computing exercises on the processing of LiDAR data and point clouds. Afternoon practicals will be based on the topics covered in the morning lectures. \n\n \n \n \n \n \n Assumed quantitative knowledge\n \nFamiliarity with Geographic Information Systems and geospatial data (i.e.\, raster and vector data) could be useful\, but not mandatory. A basic understanding of physics radiation and proprieties of electromagnetic spectrum could be also useful\, but not required. \n\n \n \n \n \n \n Assumed computer background\n \nNo prior experience with LiDAR processing software\, point cloud data or any programming language is required. \n\n \n \n \n \n \n Equipment and software requirements\n \nAttendees of the course must use a computer with any Operating System installed (GNU/Linux\, MS Windows or MacOS). The course will use Open-Source software (FOSS) and some proprietary software which will be downloaded\, installed and configured during the lectures. \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 Tickets are no longer available\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 \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 COURSE PROGRAMME\n\nMonday 6th February – Classes from 10:00 to 17:00 \n\n\nModule 1: Fundamentals of Light Detection and Ranging (LiDAR) technique. Theoretical principles of a LiDAR systems. Electronic and sensor components. Main differences between spatial\, aerial and terrestrial platforms. The physics of laser signals: Introduction to discrete and full-waveform LiDAR and signal return analysis. Resolutions and precisions achieved. Advantages and disadvantages of the technique. Practice: Introduction to LiDAR data\, platforms and services. Overview of the available processing software and programming languages/libraries. \n\n\nTuesday 7th February – Classes from 10:00 to 17:00 \n\n\nModule 2: Interpretation of LiDAR data. Introduction to metrics/products such as Digital Elevation Models\, Digital Terrain Models and Canopy Height Models. Tree delineation approaches and algorithms (ex. Watershed Algorithm). Discrete versus full-waveform LiDAR data. Echo Decomposition for peak point extraction. Voxelisation of full-waveform LiDAR data. Introduction to binary files: Discrete and full-waveform LiDAR LAS files formats. Practice: Tridimensional point cloud processing and analysis. Filtering\, measuring and classification of LiDAR point clouds. \n\n\nMonday 13th February – Classes from 10:00 to 17:00 \n\n\nModule 3: Managing and exploring a LAS dataset. Visualization advanced techniques\, metadata analysis and content reports\, LiDAR points classification into ground points and non-ground points\, buildings and high vegetation classification. Coordinate Reference System transforms. LIDAR points triangulation into a TIN in order to create a Digital Elevation Model. Elevation contours extraction from a LiDAR point cloud and boundary polygon extraction. RGB colour sampled from an orthomosaic. \n\n\nTuesday 14th February – Classes from 10:00 to 17:00 \n\n\nModule 4: Different applications for LiDAR data: biodiversity monitoring\, forest health monitoring\, urban planning\, wood trade\, archaeology and heritage monitoring and automated driving. Other types of LiDAR systems: Space-based liDAR for measuring ice sheet mass balance\, cloud and aerosol heights. Bathymetric LiDAR for the study of underwater depth of ocean floors. Practice: Post-processing of LiDAR products\, Digital Terrain Model and elevation profile analysis. Measurements of distances\, areas and volumes. Integration with external geospatial data in a Geographic Information System (GIS). \n\n \n \n \n \n \n Course Instructor\n \nDr. Nelson Pires\n\n– Works at: University of Porto\, Portugal \n\n\n– Delivers: \n\n\nRemote Sensing with satellite multi-spectral sensors (RSMS01) \n\n\nRemote Sensing with drone RGB and Near Infrared cameras (RSWD01) \n\n\nRemote Sensing with aircraft and drone LiDAR sensors (RSLD) \n\n\nNelson holds a degree in Physics and Surveying Engineering\, a MSc and PhD degrees in Surveying Engineering from University of Porto. With more than 10 years of experience in teaching at higher education institutions and doing research work in several geospatial subjects. Past and recent research includes subjects in atmospheric corrections with high-precision Global Navigation Satellite Systems analysis\, aerial and close-range photogrammetric studies with drones for coastal monitoring and map production\, multi-spectral and SAR-imaging Remote Sensing for ocean wind-generated waves and ocean dynamics. \n\n\nORCID: https://orcid.org/0000-0002-6629-8060 URL:https://prstats.preprodw.com/course/online-course-remote-sensing-with-aircraft-and-drone-lidar-sensors-rsld01/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/VGNR04R.png GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20210920 DTEND;VALUE=DATE:20210921 DTSTAMP:20260419T052230 CREATED:20220219T015845Z LAST-MODIFIED:20220804T113932Z UID:10000314-1632096000-1632182399@prstats.preprodw.com SUMMARY:ONLINE COURSE - Remote Sensing With Satellite Multi-Spectral Sensors (RSMS01) This course will be delivered live DESCRIPTION:ONLINE COURSE – Reproducible and collaborative data analysis with R (RACR03) This course will be delivered live\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, September 20th\, 2021\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. \nTime Zone\nTIME ZONE – GMT+1 – 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 \nSatellite Remote Sensing has become a common tool to investigate the different fields of Earth and environmental sciences. The progress of the performance capabilities of the optoelectronic and radar devices mounted on-board remote sensing platforms have further improved the capability of instruments to acquire information about the Earth and its resources for global\, regional and local assessments. Disciplines such as agriculture\, hydrology\, and ecosystem studies have all developed a strong Remote Sensing component\, facilitating our understanding of the environment and its processes over a broad range of spatial and temporal scales. \n\n\nThis 4-day course aims to provide participants with an integrated end-to-end perspective going from measurement techniques to end-user applications\, covering issues related to Remote Sensing\, Earth System Modelling and Data Assimilation as well as hands-on computing exercises on the processing of Earth Observation data. \n\n \n \n \n \n \n Intended Audiences\n \nAny researchers (PhD and MSc students\, post-docs\, primary investigators) and environmental professionals who are specialised in a variety of Earth Science disciplines and wish to expand and improve their knowledge and skills. \n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Information\n Availability – 30 places \nDuration – 4 days \nContact hours – Approx. 24 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n \nThe course will be divided into theoretical lectures to introduce and explain key concepts and theories\, and practices with computing exercises on the processing of Earth Observation data. Afternoon practicals will be based on the topics covered in the morning lectures. \n\n \n \n \n \n \n Assumed quantitative knowledge\n \nFamiliarity with Geographic Information Systems and geospatial data (i.e.\, raster and vector data) could be useful\, but not mandatory. A basic understanding of physics radiation and proprieties of electromagnetic spectrum could be also useful\, but not required. \n\n \n \n \n \n \n Assumed computer background\n \nNo prior experience with Remote Sensing software and data or any programming language is required. Familiarity with any digital image processing technique will be helpful\, but is not required. \n\n \n \n \n \n \n Equipment and software requirements\n \nAttendees of the course must use a computer with any Operating System installed (GNU/Linux\, MS Windows or MacOS). The course will use only Open-Source software (FOSS) which will be downloaded\, installed and configured during the lectures. \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 Tickets are no longer available\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 \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 COURSE PROGRAMME\n\nMonday 26th September – Classes from 10:00 to 17:00 \n\n\nModule 1: Fundamentals of Remote Sensing. Concepts of satellite orbits\, spacial resolutions\, temporal resolutions\, spectral and radiometric resolutions. Different types of sensors and processing levels of Earth Observation satellites. The physics of atmosphere and spectral signatures. Conceptual understanding of Remote Sensing\, where the participants will be able to identify its advantages and disadvantages. Introduction to data platforms\, software tools\, web portals\, and environmental monitoring applications. Practice: Introduction to Remote Sensing software. \n\n\nTuesday 27th September – Classes from 10:00 to 17:00 \n\n\nModule 2: Earth Observation Programmes. The National Aeronautics and Space Administration (NASA) LANDSAT Program and the European Space Agency (ESA) Copernicus/SENTINEL Program. History and Objectives. Satellite missions chronology. Different spatial\, temporal\, spectral and radiometric resolutions. LANDSAT Multispectral Scanner (MSS) and SENTINEL-2 Multispectral Instrument (MSI) sensor designs. Uses of Earth Observation satellite imagery for natural resources management\, climate change\, environmental disasters and ecology. Practice: Introduction to satellite image processing.  \n\n\nThursday 29th September – Classes from 10:00 to 17:00 \n\n\nModule 3: Remote Sensing for Vegetation Monitoring and Agricultural Applications. Satellite observations to assess a wide variety of geophysical and biophysical parameters\, including precipitation\, temperature\, evapotranspiration\, soil moisture\, and vegetation health. Band combination e index classification for vegetation monitoring. Remote Sensing data for agriculture monitoring\, specifically drought and crop monitoring. Practice: Supervised and unsupervised classification methods. \n\n\nFriday 30th September – Classes from 10:00 to 17:00 \n\n\nModule 4: Satellite Applications for Biodiversity Conservation. Specific applications and hands-on demonstrations of how to use Remote Sensing data to derive conservation policies and management decisions. Remote Sensing for Conservation and Biodiversity: Animal Movement\, Dynamic Habitat Index for Biodiversity\, Vegetation Carbon Stock Corridors and techniques for Land Change Detection. Land Management and Ecosystem Based Tools: Coral Reef Watch and MODIS NDVI Anomalies and Time Series. Practice: Image fusion and Pansharpening techniques. \n\n \n \n \n \n \n Course Instructor\n \n \n \n \n \n \nDr. Nelson Pires\n\n– Works at: University of Porto\, Portugal \n\n\n– Delivers: \n\n\nRemote Sensing with satellite multi-spectral sensors (RSMS01) \n\n\nRemote Sensing with drone RGB and Near Infrared cameras (RSWD01) \n\n\nRemote Sensing with aircraft and drone LiDAR sensors (RSLD) \n\n\nNelson holds a degree in Physics and Surveying Engineering\, a MSc and PhD degrees in Surveying Engineering from University of Porto. With more than 10 years of experience in teaching at higher education institutions and doing research work in several geospatial subjects. Past and recent research includes subjects in atmospheric corrections with high-precision Global Navigation Satellite Systems analysis\, aerial and close-range photogrammetric studies with drones for coastal monitoring and map production\, multi-spectral and SAR-imaging Remote Sensing for ocean wind-generated waves and ocean dynamics. \n\n\nORCID: https://orcid.org/0000-0002-6629-8060 \n\n  URL:https://prstats.preprodw.com/course/remote-sensing-with-satellite-multi-spectral-sensors-rsms01/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/03/RSMS01.png GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20210809 DTEND;VALUE=DATE:20210814 DTSTAMP:20260419T052230 CREATED:20220302T143216Z LAST-MODIFIED:20220804T111614Z UID:10000403-1628467200-1628899199@prstats.preprodw.com SUMMARY:ONLINE COURSE - Landscape genetic data analysis using R (LNDG04) This course will be delivered live DESCRIPTION:ONLINE COURSE – Reproducible and collaborative data analysis with R (RACR03) This course will be delivered live\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, 9th August\, 2021\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. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \nTIME ZONE\nTIME ZONE – Eastern Standard Time – Please 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 About This Course\n The term ‘landscape genetics’ has been applied studies that integrate ecological context and intervening landscape into population genetic analyses of contemporary processes such as gene flow and migration. This course will cover the basics of both quantitative landscape ecology and population genetics\, focusing on how we develop and evaluate spatial/genetic analyses using the R platform. \n \n \n \n \n \n Intended Audiences\n This course is suitable for graduate students\, postdoctoral researchers\, and primary investigators interested in learning how to integrate landscape ecological and population genetic tools using the R software. \n \n \n \n \n \n Course Details\n Availability – 24 places \nDuration – 5 days \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n \n \n \n \n \n Venue\n Delivered Remotely \n \n \n \n \n \n Teaching Format\n There will be morning lectures based on the modules outlined in the course timetable. In the afternoon there will be practicals based on the topics covered that morning. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \n \n \n \n \n \n Assumed quantative knowledge\n A basic understanding of statistical concepts. Specifically\, generalised linear regression models\, statistical significance\, hypothesis testing. \n \n \n \n \n \n Assumed computer background\n Familiarity with R. Ability to import/export data\, manipulate data frames\, fit basic statistical models & generate simple exploratory and diagnostic plots. \n \n \n \n \n \n Equipment and software requirements\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 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 Tickets are no longer available\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 \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 Monday 15th\n Classes from 9:30 to 17:30 \nModule 1: Spatial & Ecological Data.Installation & configuring R & RStudioAcquiring spatial data\, projections\, and visualizationVector and raster data \n  \n \n \n \n \n \n Tuesday 16th\n Classes from 9:30 to 17:30 \nModule 2: Genetic markers and basic analysesGenetic markers and samplingGenetic distance\, diversity\, and structureOrdination techniques based upon genetic markers \n  \n \n \n \n \n \n Wednesday 17th\n Classes from 9:30 to 17:30 \nModule 3: Integrating spatial and genetic dataBarrier detection & population divisionResistance ModelingMantel and distance regressionsRemote sensing – LiDAR and Hyperspectral data \n  \n \n \n \n \n \n Thursday 18th\n Classes from 9:30 to 17:30 \nModule 4: Integrating spatial and genetic dataSpatial autocorrelationNetwork ApproachesPCMN & Redundancy \n  \n \n \n \n \n \n Friday 19th\n Classes from 9:30 to 17:30 \nModule 5: Adaptive Genetic VarianceOutliers & gradientsQuantitative genetics\, why we should care.Chromosome walking \n  \n \n \n \n \n \n \n Course Instructor\n \nProf. Rodney Dyer\nComing Soon URL:https://prstats.preprodw.com/course/landscape-genetic-data-analysis-using-r-lndg04/ LOCATION:Delivered remotely (USA)\, Eastern Daylight Time\, MD United States\, United States CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/03/LNDG05.png GEO:39.0457549;-76.6412712 END:VEVENT END:VCALENDAR