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Saturday, 4 January 2020

[Intermediate] Spatial Data Analysis with R, QGIS & More

PRACTICAL TRAINING WITH REAL SPATIAL DATA FROM DIFFERENT SOURCES.

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 DEVELOP MAD GIS SKILLS AND PERFORM SPATIAL DATA ANALYSIS USING FREE KICKASS TOOLS SUCH AS QGIS, R, GRASS AND GOOGLE EARTH.

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This course is designed to take users who use R and QGIS for basic spatial data/GIS analysis to perform more advanced GIS tasks (including automated workflows and geo-referencing) using a variety of different data. In addition to making you proficient in R and QGIS for spatial data analysis, you will be introduced to another powerful free GIS software.. GRASS.

This course takes a completely practical approach to spatial data analysis and mapping- Each lecture will teach you a practical application/processing technique which you can apply easily.

The course is taught by Minerva Singh, A PhD graduate from Cambridge University, UK, who has several years of research experience in Quantitative Ecology and an MPhil in Geography and Environment from Oxford University. Minerva has published papers in international peer reviewed journals and given talks at international conferences. 

The underlying motivation for the course is to ensure you can put spatial data analysis into practice today and develop sound GIS analysis skills. You’ll be able to start analyzing spatial data for your own projects, and IMPRESS YOUR FUTURE EMPLOYERS with examples of your PRACTICAL spatial data analysis abilities. This course is different from other training resources. Each lecture seeks to enhance your GIS skills in a demonstrable and tangible manner and provide you with practically implementable GIS solutions.

This is an intermediate course in spatial data analysis, i.e. we will build on on basic spatial data analysis tasks (such as those covered in the beginner version course: Core Spatial Data Analysis: Introductory GIS with R and QGIS) and teach users how to practically implement more complex GIS tasks such as interpolation, mapping spatial data, geo-referencing and detailed vector processing. Additionally you will be introduced to preliminary geo-statistics and mapping/visualizing spatial data.

This course covers complex GIS techniques, and by completing this course, you will be implementing these PRACTICALLY in freely-available software, thus making you MORE ATTRACTIVE TO EMPLOYERS. 

It is a practical, hands-on course, i.e. we will spend a tiny amount of time dealing with some of the theoretical concepts pertaining to the different spatial data analysis techniques demonstrated in the course. However, majority of the course will focus on working with real spatial data from different sources. After each video you will learn how to practically implement a new concept or technique in the different softwares used for the course.

During the course of my research I have discovered that R is a powerful tool for collating and analyzing spatial data acquired from different sources.  Proficiency in spatial data analysis in R and QGIS has helped me publish more peer reviewed papers faster. Feel free to check out my profile on ResearchGate.

FREE BONUS: You will have access to all the data used in the course, along with the R code files. You will also have access to future lectures, resources and R code files. Enroll in the course today & take advantage of this special bonus!

I don’t have to remind you that we have a RISK-FREE GUARANTEE in the case of you not being satisfied with the course. Take action now!





Who this course is for:
People who have a basic understanding of spatial data analysis and want to learn more
Students interested in building up on skills acquired through my previous course Core Spatial Data Analysis: Introductory GIS with R and QGIS
Academics
Conservation managers
GIS Technicians.


95% Discount #coupon code for Spatial #Data #Analysis with R, QGIS & More #udemy #course
https://www.udemy.com/intermediate-spatial-data-analysis-with-r-qgis-more/?couponCode=10PROMO

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Hands-on text mining and natural language processing (NLP) training for data science applications in R

Over the past decade there has been an explosion in social media sites and now sites like Facebook and Twitter are used for everything from sharing information to distributing news. Social media both captures and sets trends. Mining unstructured text data and social media is the latest frontier of machine learning and data science. 
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 data science related techniques and producing publications for international peer reviewed journals. Unlike other courses out there, which focus on theory and outdated methods, this course will teach you practical techniques to harness the power of both text data and social media to build powerful predictive models. We will cover web-scraping, text mining and natural language processing along with mining social media sites like Twitter and Facebook for text data. Additionally you will learn to apply both exploratory data analysis and machine learning techniques to gain actionable insights from text and social media data .
My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you’ll easily use packages like caret, dplyr to work with real data in R. You will also learn to use the common social media mining and natural language processing packages to extract insights from text data.   I will even introduce you to some very important practical case studies - such as identifying important words in a text and predicting movie sentiments based on textual  reviews. You will also extract tweets pertaining to trending topics and analyze their underlying sentiments and identify topics with Latent Dirichlet allocation. With this Powerful  course, you’ll know it all:  extracting text data from websites, extracting data from social media sites and carrying out analysis of these using visualization, stats, machine learning, and deep learning!  
Start analyzing data for your own projects, whatever your skill level and Impress your potential employers with actual examples of your data science projects.
  • Data Structures and Reading in R, including CSV, Excel, JSON, HTML data.
  • Web-Scraping using R
  • Extracting text data from Twitter and Facebook using APIs
  • Extract and clean data from the FourSquare app
  • Exploratory data analysis of textual data
  • Common Natural Language Processing techniques such as sentiment analysis and topic modelling
  • Implement machine learning techniques such as clustering, regression and classification on textual data
  • Network analysis
Plus you will apply your newly gained skills and complete a practical text analysis assignment
We will spend some time dealing with some of the theoretical concepts. However, majority of the course will focus on implementing different techniques on real data and interpret the results.
After each video you will learn a new concept or technique which you may apply to your own projects.
All the data and code used in the course has been made available free of charge and you can use it as you like. You will also have access to additional lectures that are added in the future for FREE.


Who this course is for:
  • People who wish to learn practical text mining and natural language processing
  • People with prior experience of using RStudio
  • People with some prior experience of implementing machine learning techniques in R
  • People who were previously enrolled for my Data Science:Data Mining and Natural Language Processing course
  • People who wish to derive insights from textual and social media data.

94% off !!! #udemy #course 
Text #Mining and Natural #Language Processing in R
Hands-On Text Mining and Natural Language Processing (NLP) Training for Data Science Applications in R
#couponcode
https://www.udemy.com/text-mining-and-natural-language-processing-in-r/?couponCode=TEXTMINING_DISC1

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Your Complete Guide to Statistical Data Analysis and Visualization For Practical Applications in R

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 modeling 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 and teach you all the concepts, and take your statistical modeling 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 one course you need to complete  in order to get a head start in practical statistical modeling 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.

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, perusal of numerous books and publishing statistically rich papers in renowned international journal 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.
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.

Applied #Statistical Modeling for #Data #Analysis in R
95% off #discount #course now on #udemy 
https://www.udemy.com/applied-statistical-modeling-for-data-analysis-in-r/?couponCode=AUGDISC10

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Project Based Python Programming For Kids & Beginners

Teach yourself (and your kids) to code fun games, graphics and GUI in Python, the powerful programming language used at tech companies and in academia. 
Unlike the other courses and books out there, this course provides a rare opportunity to learn the graphics and UX (User Experience) sides of Python – even as a beginner! Unlike the many other Python courses on Udemy, this course introduces you to this computer language by drawing shapes, coding a simple game, and designing GUIs (Graphic User Interfaces), including a functional GUI for a temperature converter app.
  • Learn the basics of Python game programming
  • Craft elegant and useful Python GUIs
  • Create simple and practical applications in Python
  • Explore the world of Python graphic design
I designed this programming course to be easily understood by absolute beginners and young people. We start with basic Python programming concepts. Reinforce the same by developing games, graphics and GUIs. And finally we will develop a practical temperature converter app using Python.
The Python coding language integrates well with other platforms – and runs on virtually all modern devices. If you’re new to coding, you can easily learn the basics in this fast and powerful coding environment. If you have experience with other computer languages, you’ll find Python simple and straightforward. This OSI-approved open-source language allows free use and distribution – even commercial distribution.
Absolutely! On average, U.S. Python developers earn $109,000 per year. This powerful and widely-used language could be your or your child's ticket to a better life. With the rigorous grounding you get from this course, you’ll have the knowledge and confidence to step into higher-level Python courses.
Once you gain a basic knowledge of Python through this course, you can explore a diverse range of programming specialties:
  • Build Desktop/Laptop GUIs
  • Design Exciting and Immersive Games
  • Develop Websites and Apps
  • Analyze Scientific and Statistical Data
  • Create Educational Software
  • Access and Organize Databases
  • Manage Networks
This course gives you a solid set of skills in one of today’s top programming languages. Today’s biggest companies (and smartest startups) use Python, including Google, Facebook, Instagram, Amazon, IBM, and NASA. Python is increasingly being used for scientific computations and data analysis. 

Detailed instructions have been provided with regards to Python installation and getting started with Microsoft Visual Code, a powerful programming IDLE that will be a valuable tool for your programming journey. Hands-on coding instructions have been provided in the lecture videos to enable you to follow along. Additionally, working code examples have been provided for you to try and modify. Each video will teach you a new practical programming concept that you can apply in real time and quizzes will reinforce your learning. 
The instructor is an Oxbridge trained researcher and always available to troubleshoot. You'll also receive an industry recognized Certificate of Completion upon finishing the course.
No Risk: Preview videos from the different sections for FREE, and enjoy a 30-day money-back guarantee when you enroll - zero risk, unlimited payoff! 



Who this course is for:
  • Anyone who wants to learn to code
  • People wanting to program in Python
  • People interested in gaining hands-on Python skills and actually wanting to work through real life programming projects
  • People interested in building games and GUIs
  • Anyone looking to start with Python GUI development
  • Programming beginners and children who want to create practical applications.

Project Based #Python #Programming For #Kids & #Beginners  90% OFF ! #coupon for UDEMY COURSE
https://www.udemy.com/project-based-python-programming-for-kids-beginners/?couponCode=PYTHONLEARN10A

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ArcGIS Desktop For Spatial Analysis: Go From Basic To Pro

  • I found most spatial data books & manuals vague
  • There are no courses that actually teach me how it’s actually done
  • Available resources are expensive & I can’t afford them
  • I want to get a job in the field of GIS and geospatial analysis
  • I work in the field of ecology or quantitative social sciences or hydrology or civil engineering or geography
Over the past few months, I have published multiple courses on Udemy around this topic which will be tremendously helpful to you & most important of all AFFORDABLE!
Today, I’ve created yet another powerful resource for you!
In this course, over 50+ hands-on and practical lecture, I will help you master the most common and important geo-processing tasks that can be performed with ArcGIS Desktop, one of THE MOST important GIS software tools available.
I will also show you the kind of questions answered through Spatial Analysis & data used.
First of all, we’ll start some basic GIS tasks like “Zooming”.
Then, we’ll move into more complex processing tasks like “Geo-Statistics”.
We’ll also deal with some theoretical concepts related to Spatial Data Analysis, and then we’ll focus on implementing some of the most common GIS techniques (all the way showing you how to execute these tasks in ArcGIS Desktop).
The stuff you’ll learn from this course will be extremely useful in terms of you being able to implement it on future Spatial Data projects you’ll be working on (in a variety of disciplines from ecology to engineering).
LEARN FROM AN ACTUAL EXPERT IN THIS AREA:
My name is MINERVA SINGH.
I am an Oxford University MPhil (Geography and Environment) graduate.
I recently finished my PhD at Cambridge University (Tropical Ecology and Conservation).
I have SEVERAL YEARS OF EXPERIENCE in analyzing REAL LIFE DATA from different sources in ArcGIS Desktop.
I’ve also published my work in many international peer reviewed journals.
HERE IS HOW MY COURSE IS UNIQUE..
My course is a HANDS ON TRAINING with REAL data.
It’s a step by step course covering both the THEORY & APPLICATION of Spatial Data Analysis.
I teach Practical Stuff that you can learn quickly and start implementing NOW.
This is one of the most comprehensive courses on this topic.
I advise you to take advantage of it & enroll in the course TODAY!
Please make sure you have access to ArcGIS before enrolling
Who this course is for:
  • Academics and Researchers
  • Conservation managers, field ecologists and social scientists
  • People looking to get started in the field of GIS Analysis
  • Students of Geography, Environmental Sciences, Geology, Hydrology, Engineering, Earth Sciences and Ecology
  • People looking to use ArcGIS Desktop in academic or professional settings.

#ArcGIS Desktop For Spatial Analysis: Go From Basic To Pro #udemy #course 95% off !!! #couponcode 
https://www.udemy.com/arcgis-desktop-for-spatial-analysis-go-from-basic-to-pro/?couponCode=ARCGIS10A


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Clustering & Classification With Machine Learning In R

This course your complete guide to both supervised & unsupervised learning using R...
That means, this course covers all the main aspects of practical data science and if you take this course, you can do away with taking other courses or buying books on R based data science.
 In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised & supervised learning in R, you can give your company a competitive edge and boost your career to the next level.
My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University.
I have +5 years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals.
Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic...
This course will give you a robust grounding in the main aspects of machine learning- clustering & classification. 
Unlike other R instructors, I dig deep into the machine learning features of R and gives you a one-of-a-kind grounding in  Data Science!
You will go all the way from carrying out data reading & cleaning  to machine learning to finally implementing powerful machine learning algorithms and evaluating their performance using R.
• A full introduction to the R Framework for data science 
• Data Structures and Reading in R, including CSV, Excel and HTML data
• How to Pre-Process and “Clean” data by removing NAs/No data,visualization 
• Machine Learning, Supervised Learning, Unsupervised Learning in R
• Model building and selection...& MUCH MORE!
By the end of the course, you’ll have the keys to the entire R Machine Learning Kingdom!
NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE REQUIRED:
You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.
My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life.
After taking this course, you’ll easily use data science packages like caret to work with real data in R...
You’ll even understand concepts like unsupervised learning, dimension reduction and supervised learning. Again, we'll work with real data and you will have access to all the code and data used in the course. 
Who this course is for:
  • Students Interested In Getting Started With Data Science Applications In The R & R Studio Environment
  • Students Wishing To Learn The Implementation Of Unsupervised Learning On Real Data
  • Students Wishing To Learn The Implementation Of Supervised Learning (Classification) On Real Data Using R.

92% discount #coupon #udemy #course for
#Clustering & #Classification With #Machine #Learning In R
#couponcode
https://www.udemy.com/clustering-classification-with-machine-learning-in-r/

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Tensorflow and Keras For Neural Networks and Deep Learning

It is a full 7-Hour Python Tensorflow & Keras Neural Network & Deep Learning Boot Camp that will help you learn basic machine learning, neural networks and deep learning  using two of the most important Deep Learning frameworks- Tensorflow and Keras.                         
This course is your complete guide to practical machine & deep learning using the Tensorflow & Keras framework in Python..
This means, this course covers the important aspects of Keras and Tensorflow (Google's powerful Deep Learning framework) and if you take this course, you can do away with taking other courses or buying books on Python Tensorflow and Keras based data science.  
In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and advent of Tensorflow and Keras is revolutionizing Deep Learning...
By gaining proficiency in Keras and and Tensorflow, you can give your company a competitive edge and boost your career to the next level.
But first things first. 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 data science related techniques and producing publications for international peer reviewed journals.
 Over the course of my research I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning..
This gives students an incomplete knowledge of the subject. My course, on the other hand, will give you a robust grounding in all aspects of data science within the Tensorflow framework.
Unlike other Python courses, we dig deep into the statistical modeling features of Tensorflow & Keras and give you a one-of-a-kind grounding in these frameworks!
DISCOVER 8 COMPLETE SECTIONS ADDRESSING EVERY ASPECT OF PYTHON BASED TENSORFLOW DATA SCIENCE:
• A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda
• Getting started with Jupyter notebooks for implementing data science techniques in Python
• A comprehensive presentation about Tensorflow & Keras installation and a brief introduction to the other Python data science packages
• Brief introduction to the working of Pandas and Numpy
• The basics of the Tensorflow syntax and graphing environment
• The basics of the Keras syntax
• Machine Learning, Supervised Learning, Unsupervised Learning in the Tensorflow & Keras frameworks
• You’ll even discover how to create artificial neural networks and deep learning structures with Tensorflow & Keras
You’ll start by absorbing the most valuable Python Tensorflow and Keras basics and techniques.
I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts.
My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python based data science in real -life.
After taking this course, you’ll easily use packages like Numpy, Pandas, and Matplotlib to work with real data in Python along with gaining fluency in Tensorflow and Keras. I will even introduce you to deep learning models such as Convolution Neural network (CNN) !!
The underlying motivation for the course is to ensure you can apply Python based data science on real data into practice today, start analyzing  data for your own projects whatever your skill level, and impress your potential employers with actual examples of your data science abilities.
This course will take students without a prior Python and/or statistics background background from a basic level to performing some of the most common advanced data science techniques using the powerful Python based Jupyter notebooks
It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different  techniques on real data and interpret the results..
After each video you will learn a new concept or technique which you may apply to your own projects!
Who this course is for:
  • People Interested In Learning Python Based Tensorflow and Keras For Data Science Applications
  • People With Prior Exposure To Python Programming &/Or Data Science Concepts
  • People Interested In Implementing Neural Networks & Deep Learning Models With Tensorflow
  • People Interested In Implementing Neural Networks & Deep Learning Models With Keras

92% discount #coupon #udemy #course for
#Tensorflow and Keras For Neural Networks and #Deep #Learning
#couponcode
https://www.udemy.com/tensorflow-and-keras-for-neural-networks-and-deep-learning/

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Complete Deep Learning In R With Keras & Others

This course covers the main aspects of neural networks and deep learning. If you take this course, you can do away with taking other courses or buying books on R based data science.
 In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in neural networks and deep learning in R, you can give your company a competitive edge and boost your career to the next level!

My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University.
I have +5 years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals.
Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic .
This course will give you a robust grounding in the main aspects of practical neural networks and deep learning. 
Unlike other R instructors, I dig deep into the data science features of R and give you a one-of-a-kind grounding in data science...
You will go all the way from carrying out data reading & cleaning  to to finally implementing powerful neural networks and deep learning algorithms and evaluating their performance using R.
  • You will be introduced to powerful R-based deep learning packages such as h2o and MXNET.
  • You will be introduced to deep neural networks (DNN), convolution neural networks (CNN) and unsupervised methods.
  • You will learn how to implement convolutional neural networks (CNN)s on imagery data using the Keras framework
  • You will learn to apply these frameworks to real life data including credit card fraud data, tumor data, images among others for classification and regression applications.  
With this course, you’ll have the keys to the entire R Neural Networks and Deep Learning Kingdom!

You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.
My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real-life.
After taking this course, you’ll easily use data science packages like caret, h2o, mxnet, keras to implement novel deep learning techniques in R. You will get your hands dirty with real life data, including real-life imagery data which you will learn to pre-process and model
You’ll even understand the underlying concepts to understand what algorithms and methods are best suited for your data.
We will also work with real data and you will have access to all the code and data used in the course. 
Who this course is for:
  • People Wanting To Master The R & R Studio Environment For Data Science
  • Anyone With Prior Exposure To Common Machine Learning Concepts Such As Supervised Learning
  • Students Wishing To Learn The Implementation Of Neural Networks On Real Data In R
  • Students Wishing To Learn The Implementation Of Basic Deep Learning Concepts In R


92% discount #coupon #udemy #course for
Complete #Deep #Learning In R With Keras & Others
#couponcode
https://www.udemy.com/complete-deep-learning-in-r-with-keras-others/

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