An open API service providing repository metadata for many open source software ecosystems.

GitHub topics: visualizing-data

stefmolin/Hands-On-Data-Analysis-with-Pandas-2nd-edition

Materials for following along with Hands-On Data Analysis with Pandas – Second Edition

Language: Jupyter Notebook - Size: 70.1 MB - Last synced at: 5 days ago - Pushed at: 3 months ago - Stars: 619 - Forks: 1,459

hoomanhanaei/Data_Science_Decoded

Unraveling the rationale behind real-world data, and developing structured approaches to solve large-scale problems using data science.

Language: Jupyter Notebook - Size: 1.23 MB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 0 - Forks: 0

GMeghana19/solar-power-output

Solar power prediction using liner regression

Language: Jupyter Notebook - Size: 1.44 MB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 0 - Forks: 0

drwaseemsheikh/audiometry

Audiometry is an application framework written in C# and based on WPF and .NET that enables accurate digital recording, search, analysis, graphical visualization, and reproduction of human audio-vestibular impairment test data to assist in hearing loss or disability diagnosis.

Language: C# - Size: 123 MB - Last synced at: 7 days ago - Pushed at: almost 5 years ago - Stars: 23 - Forks: 9

AllanOtieno254/importing-and-visualizing-data

Initial import of dataset for analysis & Data Visualization

Language: Jupyter Notebook - Size: 197 KB - Last synced at: 2 months ago - Pushed at: about 1 year ago - Stars: 2 - Forks: 0

shubhamchouksey/Analyzing-Visualizing-Data-PowerBI-Solution

# Analyzing-Visualizing-Data-PowerBI ![Analyzing-Visualizing-Data-PowerBI](https://www.edx.org/sites/default/files/course/image/promoted/dat207x-course_card_image11122015-378x225.png) This repository contains the lab files and other resources for the free Microsoft course DAT207x: Analyzing and Visualizing Data with Power BI. To learn how to connect, explore, and visualize data with Power BI, sign up for this course on [edX](https://www.edx.org/course/analyzing-visualizing-data-power-bi-microsoft-dat207x). ## DataSet / Examples Terms of Usage and Disclaimer Throughout the course you will use examples and datasets provided through text files, Excel workbooks, SQL backup, and Access database. They are provided "as-is." Information and views expressed in the workbooks, including URL and other Internet Web site references, may change without notice. You bear the risk of using it. Some examples are for illustration only and are fictitious. No real association is intended or inferred. Microsoft makes no warranties, express or implied, with respect to the information provided here. These datasets/examples do not provide you with any legal rights to any intellectual property in any Microsoft product. You may copy and use these resources for your internal, reference purposes. The workbooks and related data are provided by [obviEnce](www.obvience.com). ObviEnce is an ISV and an Intellectual Property (IP) Incubator focused on Microsoft Business Intelligence. ObviEnce works closely with Microsoft to develop best practices and thought leadership for jump-starting and deploying Microsoft Business Intelligence solutions. The workbooks and data are property of obviEnce, LLC and have been shared solely for the purpose of demonstrating Power BI functionality with industry sample data. Any uses of the workbooks and/or data must include the above attribution (that is also on the Info worksheet included with each workbook). The workbook and any visualizations must be accompanied by the following copyright notice: obviEnce ©. By clicking any of the links to download the files, you are agreeing to the terms above. ###Important All the data you need for this course is provid

Size: 519 MB - Last synced at: over 1 year ago - Pushed at: about 5 years ago - Stars: 5 - Forks: 7

ekmanch/IBM-Python-Data-Science

Program offered by IBM on learning to develop SW in Python, geared towards Data Science.

Language: Jupyter Notebook - Size: 4.33 MB - Last synced at: over 1 year ago - Pushed at: about 4 years ago - Stars: 0 - Forks: 0

GabbyOlivares/Leaflet-Challenge

14.Leaflet - Visualizing Data with Leaflet

Language: JavaScript - Size: 15.3 MB - Last synced at: 6 months ago - Pushed at: over 4 years ago - Stars: 1 - Forks: 0

InfiniteCuriosity/Shiny

This is where I'll store files to demonstrate skills making interactive data visualizations using Shiny

Size: 9.77 KB - Last synced at: over 1 year ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0

difuse-dartmouth/engineering-statistics-in-R

Students learn basic functional R commands/procedures whilst tying in key statistical content. Repository for ENGS93 text files

Language: Jupyter Notebook - Size: 2.57 MB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0

difuse-dartmouth/engineering-glucose-model-ode

Students find numerical solutions to a first order ordinary differential equation (ODE) model of glucose-insulin system using Euler’s method and least squares in MATLAB.

Size: 2.12 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

difuse-dartmouth/earth-science-environmental-change

Students collect and analyze solar incidence angles over time to evaluate their own hypotheses, coupling this with the additional data analysis in Excel to draw conclusions about environmental change.

Size: 5.91 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 1

difuse-dartmouth/engineering-visualize-air-quality

Students model air quality dispersion using the “openair” package in R, analyze air quality datasets in Germany, and make recommendations based on their findings.

Size: 112 MB - Last synced at: about 1 month ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 1

saddamarbaa/python-for-everybody-specialization

==> I made this repository to post my Programming code with Python and document my progress while Learning Python for Everybody Specialization From the University of Michigan in Coursera

Language: Python - Size: 129 KB - Last synced at: 23 days ago - Pushed at: over 4 years ago - Stars: 2 - Forks: 1

adityatripathiiit/DataScience

Repository for implementation details for Data-Science

Language: Jupyter Notebook - Size: 290 KB - Last synced at: about 2 years ago - Pushed at: almost 4 years ago - Stars: 1 - Forks: 0

desi109/kafka-druid-superset

Real time analytics: Kafka + Druid + Superset. Setting up streaming analytics using open source technologies -> Apache {Kafka, Superset, Druid} to set up a system that allows you to get a visualization of some data.

Language: Shell - Size: 4.59 MB - Last synced at: almost 2 years ago - Pushed at: about 5 years ago - Stars: 3 - Forks: 0

nsrinidhibhat/Visualizing-kernels

Language: Jupyter Notebook - Size: 461 KB - Last synced at: about 2 years ago - Pushed at: over 5 years ago - Stars: 1 - Forks: 0

SamLeBlanc/CensOculus

CensusMapper is a browser-based, interactive mapping application used to visualize the results of the United States Census.

Language: JavaScript - Size: 916 MB - Last synced at: about 2 years ago - Pushed at: about 3 years ago - Stars: 1 - Forks: 1

kshittijagrawal/Geocoding

The repository contains certain lines of code that uses the concept of "Multi-Step Data Analysis" and utilizes Google Maps API to find particular addresses to visualize the latter on a map.

Language: JavaScript - Size: 22.5 KB - Last synced at: about 2 months ago - Pushed at: about 4 years ago - Stars: 0 - Forks: 0

antimattercorrade/Data_Science_Assignments

Assignments of Data Science Class

Language: Jupyter Notebook - Size: 369 KB - Last synced at: about 2 years ago - Pushed at: almost 4 years ago - Stars: 0 - Forks: 0

diannejardinez/leaflet-challenge

Creating an interactive map with Leaflet.js

Language: JavaScript - Size: 4.5 MB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 0 - Forks: 0

miguelangelnieto/Creating-Customer-Segments

Reviewed unstructured data to understand the patterns and natural categories that the data fits into. Used multiple algorithms and both empirically and theoretically compared and contrasted their results. Made predictions about the natural categories of multiple types in a dataset, then checked these predictions against the result of unsupervised analysis.

Language: HTML - Size: 796 KB - Last synced at: about 2 years ago - Pushed at: about 7 years ago - Stars: 0 - Forks: 0