Applied Statistical Modeling for Data Analysis in R
What you'll learn
- Analyze their own data by applying appropriate statistical techniques
- Interpret the results of their statistical analysis
- Identify which statistical techniques are best suited to their data and questions
- Have a strong foundation in fundamental statistical concepts
- Implement different statistical analysis in R and interpret the results
- Build intuitive data visualizations
- Carry out formalized hypothesis testing
- Implement linear modelling techniques such multiple regressions and GLMs
- Implement advanced regression analysis and multivariate analysis
Requirements
- Prior Familiarity With the Interface of R and R Studio
- Interest in Learning Statistical Modelling
- Interest in Applying Statistical Analysis to Real Life Data
- Interest in Gleaning Insights About Data (From Any Discipline)
- This Course Will be Demonstrated on a Windows OS. You Will Have to Adapt the Code Pertaining to the Changing Working Directories For your OS
Description
APPLIED STATISTICAL MODELING FOR DATA ANALYSIS IN R
COMPLETE GUIDE TO STATISTICAL DATA ANALYSIS & VISUALIZATION FOR PRACTICAL APPLICATIONS IN R
Confounded by Confidence Intervals? Pondering Over p-values? Hankering Over Hypothesis Testing?
Hello, My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).
I have several years of experience in analyzing real-life data from different sources using statistical modelling and producing publications for international peer-reviewed journals. If you find statistics books & manuals too vague, expensive & not practical, then you’re going to love this course!
I created this course to take you by hand teach you all the concepts, and take your statistical modelling from basic to an advanced level for practical data analysis.
With this course, I want to help you save time and learn what the arcane statistical concepts have to do with the actual analysis of data and the interpretation of the bespoke results. Frankly, this is the only course you need to complete in order to get a head start in practical statistical modelling for data analysis using R.
My course has 9.5 hours of lectures and provides a robust foundation to carry out PRACTICAL, real-life statistical data analysis tasks in R, one of the most popular and FREE data analysis frameworks.
GET ACCESS TO A COURSE THAT IS JAM-PACKED WITH TONS OF APPLICABLE INFORMATION! AND GET A FREE VIDEO COURSE IN MACHINE LEARNING AS WELL!
This course is your sure-fire way of acquiring the knowledge and statistical data analysis skills that I acquired from the rigorous training I received at 2 of the best universities in the world, the perusal of numerous books and publishing statistically rich papers in renowned international journals like PLOS One.
To be more specific, here’s what the course will do for you:
(a) It will take you (even if you have no prior statistical modelling/analysis background) from a basic level to performing some of the most common advanced statistical data analysis tasks in R.
(b) It will equip you to use R for performing the different statistical data analysis and visualization tasks for data modelling.
(c) It will Introduce some of the most important statistical concepts to you in a practical manner such that you can apply these concepts for practical data analysis and interpretation.
(d) You will learn some of the most important statistical modelling concepts from probability distributions to hypothesis testing to regression modelling and multivariate analysis.
(e) You will also be able to decide which statistical modelling techniques are best suited to answer your research questions and applicable to your data and interpret the results.
The course will mostly focus on helping you implement different statistical analysis techniques on your data and interpret the results.
After each video, you will learn a new concept or technique which you may apply to your own projects immediately!
TAKE ACTION NOW :) You’ll also have my continuous support when you take this course just to make sure you’re successful with it. If my GUARANTEE is not enough for you, you can ask for a refund within 30 days of your purchase in case you’re not completely satisfied with the course.
TAKE ACTION TODAY! I will personally support you and ensure your experience with this course is a success.
Who this course is for:
- People working in any numerate field which requires data analysis
- Students of Environmental Science, Ecology, Biology,Conservation and Other Natural Sciences
- People with some prior knowledge of the R interface- (a) installing packages (b) reading in csv files
- People carrying out observational or experimental studies
Instructor
- 4.4 Instructor Rating
- 28,245 Reviews
- 145,222 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).