Data Analysis: A/B Testing; Data Visualization; Data Wrangling; Numpy; Pandas; Practical Statistics; Python; SQL.
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Updated
Jun 21, 2019
Data Analysis: A/B Testing; Data Visualization; Data Wrangling; Numpy; Pandas; Practical Statistics; Python; SQL.
Predicting price and customer satisfaction: Airbnb data
My collection of visualizing different datasets using (Matplotlib, Seaborn, and Folium) packages for Python
Determines the price of the launch. Also, determines if SpaceX will reuse the first stage.
Acest repo conține materiale, seturi de date și soluții care au fost folosite în cadrul Școlii de vară Astra, prima ediție, 2021
Visualising World Mortality Rates
• Applied data wrangling and data visualisation skills on stroke dataset and created visualisations using the R programming language in Rstudio. • Prepared a report using Latex which included a set of decision supports using visualisations • Created a web-based application with interactive graphs (using shiny package from R) to tell a direct story.
Explore Diwali Sales Data Analysis: a project dissecting 11,251 records with 15 columns to uncover customer demographics and buying trends during the festive season. The objective? Understand customer behavior, identify key demographics and product categories driving sales. Utilizing Python libraries like Pandas, NumPy, Matplotlib, and Seaborn, thi
Extensively worked on data cleaning and data wrangling to analyze the number of deaths between years 2000-2023 with the help of pie charts, bar charts, word cloud and heat maps. Used time series analysis to check if the events of death follow stationarity and forecasted the trends.
This folder contains work I submitted as part of the technical interview for a data science position at CEMA
A set of handy functions for digital data analysis purposes
Data wrangling in Python
Example of how to use CasperJS to scrape data from a website
Converting and integrating data from multiple sources is often tricky business. Luckily there are some great tools available that make this a breeze. I use a genetic annotation file (Brachypodium) and incorporate gene ontology definitions. This Uses dplyr and tidyr to do the data wrangling.
The package reaches out to scientists that seek to estimate MOI and lineage frequencies at molecular markers using the maximum-likelihood framework described in https://doi.org/10.1371/journal.pone.0261889. Users can import data from Excel files in various formats, and perform maximum-likeli
Some materials I used to Train basics of R
Analysis of NOAA storm database with R to determine most severe types of weather event
🏡 🚥 Python - SQL / Wrangling and cleaning up the OpenStreeMap data from San Francisco area and running queries on it using SQL
[Data wrangling, Seaborn visualization] A data science exercise for food safety evaluation in SF
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