visualizing-data
There are 1 repository under visualizing-data topic.
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 554drwaseemsheikh / 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# 19shubhamchouksey / Analyzing-Visualizing-Data-PowerBI-Solution
# Analyzing-Visualizing-Data-PowerBI  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
cgatama / Python-Project-for-Data-Science
Foundational Python skills for Working with a real-world data set and a real-world inspired scenario to identify patterns and trends
Language:Jupyter Notebook 4desi109 / 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 3AllanOtieno254 / importing-and-visualizing-data
Initial import of dataset for analysis & Data Visualization
Language:Jupyter Notebook 2saddamarbaa / 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 2adityatripathiiit / DataScience
Repository for implementation details for Data-Science
Language:Jupyter Notebook 1GabbyOlivares / Leaflet-Challenge
14.Leaflet - Visualizing Data with Leaflet
Language:JavaScript 1SamLeBlanc / CensOculus
CensusMapper is a browser-based, interactive mapping application used to visualize the results of the United States Census.
Language:JavaScript 1- engineering-glucose-model-ode
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.
0 Students learn basic functional R commands/procedures whilst tying in key statistical content. Repository for ENGS93 text files
Language:Jupyter Notebook 0ekmanch / IBM-Python-Data-Science
Program offered by IBM on learning to develop SW in Python, geared towards Data Science.
Language:Jupyter Notebook 0jzstats / chatgpt-numbers-and-the-meaning-of-life
This repo supports the project I produced for the Honors Track Assignment of the final Course 'Capstone - Retrieving, Processing and Visuallizing Data with Python' from 'Python for Everybody' Specialization by the 'University of Michigan' on Coursera.
Language:Python 0NGASHBAUGH / JavaScript-Leaflet_Earthquake
Leaflet Challenge - Visualizing Data with Leaflet- Completed for UC Davis's data analytics and visualization Bootcamp
Language:JavaScript 0satishrath185 / Customer-Churn
Predict Customer Churn for Banking Industry.
Language:Jupyter Notebook 0antimattercorrade / Data_Science_Assignments
Assignments of Data Science Class
Language:Jupyter Notebookdiannejardinez / leaflet-challenge
Creating an interactive map with Leaflet.js
Language:JavaScript- 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.
Students model air quality dispersion using the “openair” package in R, analyze air quality datasets in Germany, and make recommendations based on their findings.
InfiniteCuriosity / Shiny
This is where I'll store files to demonstrate skills making interactive data visualizations using Shiny
KoussaySAM / BI---Data-Professional-Survey-Breakdown-
BI Project
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:JavaScriptmiguelangelnieto / 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