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Geospatial Data Science: Statistics and Machine Learning I

Vector data analysis in Python with GeoPandas, statsmodels, Scikit-learn, and PySAL
4.4
4.4/5
(11 reviews)
286 students
Created by

8.9

CourseMarks Score®

9.9

Freshness

8.0

Feedback

8.1

Content

Platform: Udemy
Price: $11.99
Video: 12h 6m
Language: English
Next start: On Demand

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Detailed Analysis

CourseMarks Score®

8.9 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness Score

9.9 / 10
This course was last updated on 3/2021.

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Student Feedback

8.0 / 10
We analyzed factors such as the rating (4.4/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Content Score

8.1 / 10
Video Score: 9.4 / 10
The course includes 12h 6m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
The average video length is 5 hours 07 minutes of 67 GIS courses on Udemy.
Detail Score: 9.3 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 5.5 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

0 article.
0 resource.
0 exercise.
0 test.

Table of contents

Description

In this course I demonstrate open source python packages for the analysis of vector-based geospatial data.  I use Jupyter Notebooks as an interactive Python environment.  GeoPandas is used for reading and storing geospatial data, exploratory data analysis, preparing data for use in statistical models (feature engineering, dealing with outlier and missing data, etc.), and simple plotting.  Statsmodels is used for statistical inference as it provides more detail on the explanatory power of individual explanatory variables and a framework for model selection.  Scikit-learn is used for machine learning applications as it includes many advanced machine learning algorithms, as well as tools for cross-validation, regularization, assessing model performance, and more.
This is a project-based course.  I use real data related to biodiversity in Mexico and walk through the entire process, from both a statistical inference and machine learning perspective.  I use linear regression as the basis for developing conceptual understanding of the methodology and then also discuss Poisson Regression, Logistic Regression, Decision trees, Random Forests, K-NN classification, and unsupervised classification methods such as PCA and K-means clustering.
Throughout the course, the focus is on geospatial data and special considerations for spatial data such as spatial joins, map plotting, and dealing with spatial autocorrelation.   
Important concepts including model selection, maximum likelihood estimation, differences between statistical inference and machine learning and more are explained conceptually in a manner intended for geospatial professionals rather than statisticians.

You will learn

✓ Basic concepts of statistical modeling
✓ Pandas tools for data preparation
✓ Feature engineering methods
✓ Linear Regression
✓ Logistic Regression
✓ Other supervised classification methods
✓ Unsupervised classification methods
✓ Non-parametric regression
✓ Dealing with spatial autocorrelation

Requirements

• You should be familiar with Python, Geo
• Pandas, and Jupyter Notebooks and have a working environment. This knowledge can be gained through my courses “Survey of Python for GIS applications” and “Geospatial Data Science with Python: Geo
• Pandas”You should have some familiarity with basic statistics, especially Linear Regression.

This course is for

• Geospatial professionals who are interested in learning more about the machine learning tools for vector data in the Python geospatial stack.

How much does the Geospatial Data Science: Statistics and Machine Learning I course cost? Is it worth it?

The course costs $11.99. And currently there is a 87% discount on the original price of the course, which was $94.99. So you save $83 if you enroll the course now.
The average price is $12.2 of 67 GIS courses. So this course is 2% cheaper than the average GIS course on Udemy.

Does the Geospatial Data Science: Statistics and Machine Learning I course have a money back guarantee or refund policy?

YES, Geospatial Data Science: Statistics and Machine Learning I has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any SCHOLARSHIPS for this course?

Currently we could not find a scholarship for the Geospatial Data Science: Statistics and Machine Learning I course, but there is a $83 discount from the original price ($94.99). So the current price is just $11.99.

Who is the instructor? Is Michael Miller a SCAM or a TRUSTED instructor?

Michael Miller has created 18 courses that got 3,933 reviews which are generally positive. Michael Miller has taught 19,715 students and received a 4.5 average review out of 3,933 reviews. Depending on the information available, Michael Miller is a TRUSTED instructor.
GIS Programming
I have been programming and working with database applications for over 30 years, and specializing in geospatial applications for over 20 years.  I am a believer in the 80/20 pareto principle which suggests that you only need to understand 20% of a subject in order to do 80% of your work. My goal in all my courses is to teach at the level of that 20% sweet spot and to provide my students with the background and the tools they need to learn the rest of what they need on their own.

8.9

CourseMarks Score®

9.9

Freshness

8.0

Feedback

8.1

Content

Platform: Udemy
Price: $11.99
Video: 12h 6m
Language: English
Next start: On Demand

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