Core Spatial Data Analysis: Introductory GIS with R and QGIS
What you'll learn
- You will have a greater clarity of basic spatial data concepts and data types
- Carry out practical spatial data analysis tasks in freely available software
- Learn about the kind of questions that are answered through spatial analysis and where to obtain free spatial data.
- Analyze spatial data using both R and QGIS
- Process raster and vector data in both R and QGIS
- Show off your skills & gain experience by working on a real life conservation related spatial data analysis project
- Start analyzing spatial data for your own projects using two powerful freeware tools
- You'll have a copy of all the data and R scripts used in the course will be provided to students for their reference and to use in their own analysis.
- You'll also have plenty of handy hints and tips will be provided alongside the code to prevent glitches
Requirements
- An interest in working with spatial data.
- It would be useful if students are able to (a) change working directories, (b) install packages, (c) load libraries and (d) read in CSVs in R.
- The course will be demonstrated using a Windows PC. Mac and Linux users will have to adapt the instructions to their operating systems.
- Optional install: QGIS (While this course will introduce using QGIS alongside R for doing spatial data analysis, it is entirely possible to master all the concepts of the course in R alone).
Description
MASTER SPATIAL DATA ANALYSIS IN R & QGIS: HANDS ON TRAINING WITH A REAL SPATIAL DATA PROJECT!
Do you find GIS & Spatial Data books & manuals too vague, expensive & not practical and looking for a course that takes you by hand, teaches you all the concepts, and get you started on a real life project?
Or perhaps you want to save time and learn how to automate some of the most common GIS tasks?
I'm very excited you found my spatial data analysis course. My course provides a foundation to carry out PRACTICAL, real-life spatial data analysis tasks in popular and FREE software frameworks.
My name is MINERVA SINGH and i am an Oxford University MPhil (Geography and Environment) graduate. I am currently pursuing a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life spatial data from different sources and producing publications for international peer reviewed journals.
In this course, actual spatial data from the Tam Dao National Park in Vietnam will be used to give a practical hands-on experience of working with real life spatial data and understanding what kind of questions spatial data can help us answer. The underlying motivation for the course is to ensure you can put spatial data analysis into practice today. Start analyzing spatial data for your own projects, whatever your skill level and IMPRESS your potential employers with an actual example of your spatial data analysis abilities.
This is a core course in spatial data analysis, i.e. we will focus on learning the most important and widely encountered spatial data analysis tasks in both R and QGIS
It is a practical, hands-on course, i.e. we will spend a tiny amount of time dealing with some of the theoretical concepts related to spatial data analysis. However, majority of the course will focus on working with the spatial data from the Tam Dao National Park, Vietnam. After each video you will learn a new concept or technique which you may apply to your own projects.
TAKE ACTION TODAY! I will personally support you and ensure your experience with this course is a success.
Who this course is for:
- Academics
- Researchers
- Conservation managers
- Anybody who works/will work with spatial data
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Instructor
- 4.4 Instructor Rating
- 28,245 Reviews
- 145,215 Students
- 61 Courses
I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics.
I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC).