Soource’s Post

I shared this cool article a few months ago from Towards Data Science, 𝐃𝐨𝐰𝐧𝐬𝐜𝐚𝐥𝐢𝐧𝐠 𝐚 𝐒𝐚𝐭𝐞𝐥𝐥𝐢𝐭𝐞 𝐓𝐡𝐞𝐫𝐦𝐚𝐥 𝐈𝐦𝐚𝐠𝐞 𝐟𝐫𝐨𝐦 1000𝐦 𝐭𝐨 10𝐦 (𝐏𝐲𝐭𝐡𝐨𝐧) by Mahyar Aboutalebi With the comment sections, I opened Pandora's box, so I wonder if anyone in my network, especially from remote sensing circles, has seen any more recent articles on downscaling satellite images, and what are the most recent feedback on this matter. Still pretty good read, go for it here: https://lnkd.in/dA3y793c #GIS #spatialanalytics #geospatialdata #geospatial #datascience

interesting article #geospatial #datascience #python

New in CODATA Data Science Journal: Heike Görzig, Alejandra N. Gonzalez Beltran, Felix Engel, Brian Matthews Title: A Framework for Active DMPs in Photon and Neutron Science Large-Scale Facilities https://lnkd.in/e_qCusSJ #codata #FAIRdata #OpenScience #datascience #opendata #datasciencej

My latest article on dev.to explores Space Mission EDA and predictive modeling, unraveling the mysteries of launches, success rates, and the challenges of exploring the final frontier. Dive into the cosmos with data science: https://lnkd.in/gNBbwnce

DATA EXPLORATION

Why aspiring Data scientists and Data engineers should build with GeoSpatial Data? 1) Rich and extensive data sources available for free : A goldmine of rich data sources freely available online 2) Work on a scale from the start : Terabytes of data to work with 3) See climate change in real time : You take up the role of a journalist and see climate change as it is happening 4) Build for the Social Good : Work on problems that have a social impact and send a powerful message backed by data 5) Beautiful plots and visualizations! read more at : https://lnkd.in/gW_GMdZw Sentinel Hub Googleearth Engine #fused #sentinel #geospatial Isaac Brodsky #earthobservation Fused Sina Kashuk Abhishek Patil Plinio Guzman

A really cute piece by Victor Murcia on Towards Data Science titled 𝐒𝐪𝐮𝐢𝐫𝐫𝐞𝐥𝐌𝐋: 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐧𝐠 𝐒𝐪𝐮𝐢𝐫𝐫𝐞𝐥 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐢𝐧 𝐍𝐘𝐂’𝐬 𝐂𝐞𝐧𝐭𝐫𝐚𝐥 𝐏𝐚𝐫𝐤: "NYCOpenData has a treasure trove of both interesting and rich datasets to explore related to topics concerning health, environment, business, and education. I stumbled upon the 2018 Central Park Squirrel Census dataset and I knew immediately that I had to do something with it. This dataset deals with squirrel sightings collected over the course of two weeks by volunteers in Central Park. After looking through the data dictionary, I was drawn to a feature named ‘Approaches’ that denotes whether a squirrel was observed approaching a human. I thought it’d be neat to train a machine learning (ML) model to assist me in determining whether a squirrel located within the bounds of Central Park would approach me. 𝐑𝐞𝐚𝐝 𝐢𝐭 𝐡𝐞𝐫𝐞 : https://lnkd.in/dzU_4Hh6 #GIS #spatialanalytics #geospatialdata #geospatial #datascience #datavisualization

Iris Flower Dataset Classification 👩💻 #CODSOFT I Analyzed the Iris Flower Dataset, comprising 150 samples with features like sepal and petal dimensions, to classify iris species into Setosa, Versicolor, and Virginica. Key Aspects: Data Visualization: Explored feature distributions and relationships. Modeling: Implemented classification algorithms such as logistic regression and decision trees. Evaluation: Assessed model performance with accuracy and other metrics. This project showcases practical applications of classification techniques.

As a data analyst, understanding signal processing fundamentals is key. Recently, I came across the concept of Wiener Process and its application in representing the integral of a white noise Gaussian process. I developed a project around this concept while completing my master's thesis and continuing my work in signal analysis and processing. If you're interested in learning more about my project on white noise processing, check out the link below. I'd love to hear your thoughts and insights on this topic! Link: https://lnkd.in/d7ZBBZtD #dataanalyst #datascience #project #signalprocessing #analysis #mastersdegree #degree

Task 1 Completed in Data Science at InternPedia Titanic Survival Prediction "Sinking into data analysis! I tackled a project predicting passenger survival rates on the Titanic based on factors like age, gender, class, and more. I developed a predictive model using techniques like: - Data preprocessing and feature engineering - Model selection and hyperparameter tuning - Performance evaluation and optimization