A collection of data analysis and visualization projects designed to uncover insights from diverse datasets. These projects include analyses on COVID-19 trends, stock trading patterns, housing market prices, IoT data, and more, showcasing the power of data-driven storytelling.

A fully interactive data storytelling dashboard for e-commerce analytics. Built with Python, Streamlit, and Plotly, it transforms transactional data into actionable insights through KPIs, cohort retention, RFM segmentation, and global visualizations, perfect for analysts and data scientists.

This repository aims to collect the articles and codes for the Visual Storytelling (VIST) task. VIST is a vision-and-language task. It aims to summarize the idea of a photo stream and tells a story about it (in natural language). Be careful about its difference from the "storytelling with data", which is more related to data visualization.

AI Report Factory automates data storytelling, transforming raw business data into analytical, narrative-driven reports. It ingests structured datasets, computes KPIs, generates visualizations, and produces Markdown & HTML reports with actionable insights for startups, analysts, and enterprises.

Home for tutorials and notebooks to support workshops and online courses on urban data analysis, visualization, and storytelling

Tableau Dashboard Repository This repository hosts a collection of Tableau dashboards designed to visualize and analyze data on various topics. Explore the dashboards for insights and data-driven storytelling. For more details or to collaborate, please feel free to contact the repository owner.

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"Experience the magic of the 'Text to Image' project, where JavaScript transforms your text into captivating visuals using HTML5 and CSS3. Unlock the creative potential of digital storytelling and data visualization in a visually immersive experience."

A Python project showcasing data visualization using NumPy, Pandas, Matplotlib, and Seaborn. It covers data preprocessing, statistical analysis, and creating insightful visualizations like line plots, heatmaps, box plots, and more. Ideal for learning and practicing data analysis and visual storytelling.

This is the repository for our work titled 'DataNarrative: Automated Data-Driven Storytelling with Visualizations and Texts'

Workshop materials for "Open Source Geospatial Workflows in the Cloud: Learn about Tools and Techniques for Data Access, Analysis, Visualization, Storytelling, and Sharing in the Python and Jupyter ecosystem"

Heart Disease Analysis Power BI Dashboard A data-driven Power BI report analyzing heart disease patient data to uncover insights by gender, age, and health metrics. Built using Power BI, Excel, and DAX to demonstrate data modeling, visualization, and business intelligence storytelling for healthcare analytics.

Turning messy data into meaningful insights — my journey through cleaning, visualization, modeling, and storytelling.

Master data visualization: interactive charts, dashboards, and storytelling with tools like Matplotlib, D3.js, and Tableau. Includes examples and datasets.

Power BI dashboard analyzing student productivity, stress levels, sleep patterns, and screen time using data visualization and storytelling.

📊 Data Visualization: Turn raw data into interactive and beautiful visuals! Simplify complex datasets 📂 → 📈 for smarter decisions. Empowering data storytelling through Python, Tableau, and more! 🚀✨

Content for the Data Visualization and Storytelling Course for Scientisst

Learn how elements of storytelling can be applied to data visualization, and how to effectively tell the story in your data.

A collection of beginner-friendly data analysis projects using Python, SQL, Excel, and data visualization tools. Built to demonstrate my skills in data cleaning, exploration, and storytelling as I grow in my data analytics journey.

Interview Coding Challenge Data Science Step 1 of the Data Scientist Interview process. Follow the instructions below to complete this portion of the interview. Please note, although we do not set a time limit for this challenge, we recommend completing it as soon as possible as we evaluate candidates on a first come, first serve basis... If you have any questions, please feel free to email support@TheZig.io. We will do our best to clarify any issues you come across. Prerequisites: A Text Editor - We recommend Visual Studio Code for the ClientSide code, its lightweight, powerful and Free! (https://code.visualstudio.com/) SQL Server Management Studio (https://docs.microsoft.com/en-us/sql/ssms/download-sql-server-management-studio-ssms?view=sql-server-2017) R - Software Environment for statitistal computing and graphics. You can download R at the mirrors listed here (https://cran.r-project.org/mirrors.html) Azure - Microsoft's Cloud Computing platform. You can create an account without a credit card by using the Azure Pass available at this link (https://azure.microsoft.com/en-us/offers/azure-pass/) Git - For source control and committing your final solution to a new private repo (https://git-scm.com/downloads) a. If you're not very familiar with git commands, here's a helpful cheatsheet (https://services.github.com/on-demand/downloads/github-git-cheat-sheet.pdf) 'R' Challenge For each numbered section below, write R code and comments to solve the problem or to show your rationale. For sections that ask you to give outputs, provide outputs in separate files and name them with the section number and the word output "Section 1 - Output". Create a private repo and submit your modified R script along with any supporting files. Load in the dataset from the accompanying file "account-defaults.csv" This dataset contains information about loan accounts that either went delinquent or stayed current on payments within the loan's first year. FirstYearDelinquency is the outcome variable, all others are predictors. The objective of modeling with this dataset is to be able to predict the probability that new accounts will become delinquent; it is primarily valuable to understand lower-risk accounts versus higher-risk accounts (and not just to predict 'yes' or 'no' for new accounts). FirstYearDelinquency - indicates whether the loan went delinquent within the first year of the loan's life (values of 1) AgeOldestIdentityRecord - number of months since the first record was reported by a national credit source AgeOldestAccount - number of months since the oldest account was opened AgeNewestAutoAccount - number of months since the most recent auto loan or lease account was opened TotalInquiries - total number of credit inquiries on record AvgAgeAutoAccounts - average number of months since auto loan or lease accounts were opened TotalAutoAccountsNeverDelinquent - total number of auto loan or lease accounts that were never delinquent WorstDelinquency - worst status of days-delinquent on an account in the first 12 months of an account's life; values of '400' indicate '400 or greater' HasInquiryTelecomm - indicates whether one or more telecommunications credit inquires are on record within the last 12 months (values of 1) Perform an exploratory data analysis on the accounts data In your analysis include summary statistics and visualizations of the distributions and relationships. Build one or more predictive model(s) on the accounts data using regression techniques Identify the strongest predictor variables and provide interpretations. Identify and explain issues with the model(s) such as collinearity, etc. Calculate predictions and show model performance on out-of-sample data. Summarize out-of-sample data in tiers from highest-risk to lowest-risk. Split up the dataset by the WorstDelinquency variable. For each subset, run a simple regression of FirstYearDelinquency ~ TotalInquiries. Extract the predictor's coefficient and p-value from each model. Store the in a list where the names of the list correspond to the values of WorstDelinquency. Load in the dataset from the accompanying file "vehicle-depreciation.csv". The dataset contains information about vehicles that our company purchases at auction, sells to customers, repossess from defaulted accounts, and finally re-sell at auction to recover some of our losses. Perform an analysis and/or build a predictive model that provides a method to estimate the depreciation of vehicle worth (from auction purchase to auction sale). Use whatever techniques you want to provide insight into the dataset and walk us through your results - this is your chance to show off your analytical and storytelling skills! CustomerGrade - the credit risk grade of the customer AuctionPurchaseDate - the date that the vehicle was purchased at auction AuctionPurchaseAmount - the dollar amount spent purchasing the vehicle at auction AuctionSaleDate - the date that the vehicle was sold at auction AuctionSaleAmount - the dollar amount received for selling the vehicle at auction VehicleType - the high-level class of the vehicle Year - the year of the vehicle Make - the make of the vehicle Model - the model of the vehicle Trim - the trim of the vehicle BodyType - the body style of the vehicle AuctionPurchaseOdometer - the odometer value of the vehicle at the time of purchase at the auction AutomaticTransmission - indicates (with value of 1) whether the vehicle has an automatic transmission DriveType - the drivetrain type of the vehicle

A webapp to augment data analysis, visualization and storytelling through data driven mindmaps.

Visualized and analyzed the most-streamed Spotify songs up to 2023 using Python, Power BI, and web scraping, combining data storytelling with interactive dashboards for impactful music insights.

📊🌟 Explore a colorful array of projects showcasing the art of data storytelling using the powerful D3.js library. Dive into captivating visualizations crafted during the FreeCodeCamp certification journey in Data Visualization. Join the visual voyage into this repository and witness data come to life! ✨🎨📈

In the ever-evolving world of data analytics, the ability to present data in a clear and compelling manner is paramount. This document serves as a comprehensive guide for the Industry Solutions Delivery team on customizing visualizations in Power BI, tailored to enhance our project's storytelling and data representation

Our project for the "Data and Results Visualization" exam at Politecnico di Milano. The project was about visualizing scraped data from Spotify, searching for an interesting "storytelling".

This git repository contains the dataset and the notebooks for the following book. The book can be purchased at Amazon.

Data visualization portfolio using Python, R/ggplot2. Weekly exercises exploring design, statistical graphics, and storytelling with data.

A portfolio of dynamic Tableau dashboards demonstrating interactive visualizations, insightful analysis, and data-driven storytelling across diverse projects.

Bilingual, animated Earth science storytelling platform built for the ISS 25th Anniversary. Combines satellite data, community impact, and AR visualization for NASA Space Apps Challenge.

This project scrapes IPL match data from the web, analyzes team and player performance using Python, and presents interactive visualizations on a user-friendly dashboard. It combines web scraping, data wrangling, and visual storytelling to provide valuable insights from IPL seasons.