Geospatial APIs For Data Science Applications In Python
- Description
- Curriculum
- FAQ
- Reviews
ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT OBTAINING AND WORKING WITH WITH FREE GEOSPATIAL DATA OBTAINED VIA APPLICATION PROGRAMMING INTERFACES (APIs) USING DATA SCIENCE TECHNIQUES.
- Are you currently enrolled in any of my GIS and remote sensing related courses?
- Or perhaps you have prior experiences in GIS or tools like R and QGIS?
- You want to quickly analyse large amounts of geospatial data
- Implement machine learning models on remote sensing data
- You don’t want to spend 100s and 1000s of dollars on buying commercial software for imagery analysis?
- You want to have access to a multi-petabyte catalogue of satellite imagery and geospatial datasets with planetary-scale analysis capabilities
The next step for you is to gain proficiency in obtaining free geospatial datasets from a variety of sources, from Foursquare to Google Earth Engine via their Python-friendly APIs and analyse these using data science techniques
MY COURSE IS A HANDS-ON TRAINING WITH REAL REMOTE SENSING AND GIS DATA ANALYSIS WITH GOOGLE EARTH ENGINE- A planetary-scale platform for Earth science data & analysis; including implementing machine learning models on imagery data, powered by Google’s cloud infrastructure. !
My course provides a foundation to carry out PRACTICAL, real-life remote sensing and GIS analysis tasks in this powerful cloud-supported platform. By taking this course, you are taking an important step forward in your GIS journey to become an expert in geospatial analysis.
Why Should You Take My Course?
I have an MPhil (Geography and Environment) from the University of Oxford, UK. I also completed a PhD at Cambridge University (Tropical Ecology and Conservation).
I have several years of experience in analyzing real-life spatial geospatial data from different sources and producing publications for international peer-reviewed journals.
In this course, actual geospatial data obtained via Foursquare and GEE APIs will be used to give you hands-on experience of applying data science and machine learning techniques to these data to answer real-life questions such as identifying the best locations for a restaurant or changes in socio-economic dynamics of a territory.
This course will ensure you learn & put geospatial data analysis into practice today and increase your proficiency in using APIs for obtaining these data and deriving valuable insights from them.
This is a fairly comprehensive course, i.e. we will focus on learning the most essential and widely encountered data science techniques applied to geospatial data
In addition to all the above, you’ll have MY CONTINUOUS SUPPORT to make sure you get the most value out of your investment!
ENROLL NOW 🙂
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1What Is This Course About?Video lesson
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2Data and CodeText lesson
The data and code for the course can be obtained from this google drive: https://drive.google.com/drive/folders/1SChg6AVFkqGw5SoLKLGvnfPl7ny_XAf8?usp=sharing
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3Python InstallationVideo lesson
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4What Is Google CoLab?Video lesson
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5Google Colabs and GPUVideo lesson
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6Google Colab PackagesVideo lesson
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7Introduction To Basic Spatial Data ConceptsVideo lesson
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8What Are APIsVideo lesson
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9Singapore MRTVideo lesson
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10Basic GeocodingVideo lesson
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11Geocode A Dataframe of CitiesVideo lesson
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12Introduction To The Foursquare APIVideo lesson
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13Get Started With the Foursquare APIVideo lesson
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14Obtain Venues and Their Details Around a Particular LocationVideo lesson
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15Visualise the Foursquare VenuesVideo lesson
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16Retrieve Venues On the Basis of Lat Long CoordinatesVideo lesson
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17Retrieve the Venues Corresponding To Mumbai's NeighbourhoodsVideo lesson
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23Accessing GEE API Within PythonVideo lesson
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24Introduction To GeemapVideo lesson
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25Start Exploring Feature CollectionsVideo lesson
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26Filter and Visualise ShapefilesVideo lesson
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27Identify the Biggest CountryVideo lesson
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28Filter Based on Numerical AttributesVideo lesson
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29Grouping Feature Collections By AttributesVideo lesson
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30Create a GeoJSON Bounding BoxVideo lesson
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31Clip Image To Shapefile ExtentVideo lesson
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32Upload External Data On GEEVideo lesson
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33Access Image Collections Within Google ColabVideo lesson
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34See Images Side By SideVideo lesson
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35Topographic ComputationsVideo lesson
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36Clip Image Collection To Shapefile ExtentVideo lesson
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37Improve Your Clipped ImageVideo lesson
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38Time Series VisualizationVideo lesson
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39What Are Multispectral Data?Video lesson
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40Using Multispectral Data: Case of Tonle SapVideo lesson
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41Flood MappingVideo lesson
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42Why Do We Need Radar DataVideo lesson
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43Obtaining Sentinel-1 Data From GEEVideo lesson
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44Visualise Sentinel-1 DataVideo lesson
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45Obtain Time Series Landsat Data From GEEVideo lesson
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46What Are Pandas?Video lesson
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47Principles of Data VisualisationVideo lesson
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48Some Theoretical Principles Behind Data VisualisationVideo lesson
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49Visualise Time Series Geospatial Data With PandasVideo lesson
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50Where Are Singapore's MRT Stations Located?Video lesson
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51Let's Colour Code These Stations-Part 1Video lesson
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52Let's Colour Code These Stations- Part 2Video lesson
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53What is Machine Learning (ML)?Video lesson
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54Training DataVideo lesson
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55Unsupervised Learning:TheoryVideo lesson
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56k-meansVideo lesson
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57Clustering Landcovers in Cambodia-Part1Video lesson
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58Clustering Landcovers in Cambodia-Part 2Video lesson
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59Supervised ClassificationVideo lesson
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60Random ForestVideo lesson
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61Basic Supervised Classification With MODIS For Training SamplesVideo lesson
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62How Good Are My Results?Video lesson
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63AccuracyVideo lesson
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64Spectral UnmixingVideo lesson
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65Supervised Classification With Geolocations: Introduction (Part 1)Video lesson
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66Supervised Classification: Geolocation Training DataVideo lesson
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67Classify The ImageVideo lesson
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68Combine EO Data From Different Sensors-ProblemVideo lesson
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69Supervised Classification: Sentinel-1 and Sentinel-2Video lesson
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70Supervised Classification: Sentinel VIsVideo lesson
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71Visualise the Classification resultsVideo lesson

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