# Dataquest.io

Guided projects details:

Projects done on data science in python path

This project aims identify what are the more profitable app profiles for the App Store and Google Play markets.
Python for Data Science: Fundamentals (Part I and II)

This project aims to take some conclusions about which posts from Hacker News receive more comments.
Python for Data Science: Intermediate

This project aims to explore the data details of used cars sold in eBay.
Pandas and NumPy Fundamentals
Objective: Explore the dataset and tried to find some details about what kind of cars we have in this dataset.
I propose myself to tried to reply to following questions in the point 4 of this project:

4.1 - What are the most commons brands?
4.2 - What are the relation between the most commons brands and its mean price?
4.3 - What are the relation between the most commons brands and its mean odometer?
4.4 - What are the most common brand/model combination?
4.5 - How much cheaper are cars with damage than their non-damaged counterparts?
4.6 - How much expensive are cars with automatic gearbox?
4.7 - How is the price evoluation according to the vehicle type?

This project aims to find heavy traffic indicators on I-94.
Data Visualization Fundamentals
Objective: Tried to identify the correlation between variables, to find what could be the factors that cause heavy traffic in the highway I-94. * during day time vs night time * during working days vs weekend
Cleaning dataset and using groupby() function to manipulate the information and create the graphics
Created exploratory graphics: histograms, line plot, horizontal bar plot, scatter plots and made a grid chart with small multiple graphics
Reach the oportuniy to explore a little more about how to customise plots using Matplotlib (still without use the styles), adding grid with customised linestyle and Seaborn options. https://github.com/midlourenco/Dataquest.io/blob/main/project_4-%20heavy%20traffic%20indicators/20210719-%20project4%20-%20heavy%20traffic%20indicators.ipynb

Evolution of Euro daily exchange rates
Storytelling Data Visualization and Information Design
Objective: In this project we focus on evolution of EUR/USD rate and related its evolution with some historical facts: * During financial crises (2008) * COVID-19 pandemic (2020)
Cleaned the dataset and made few exploratory graphic to identify what kind of data we have on hands.
Created explanatory graphics which should be enough to let audience easily understand the storyline just checking graphics
Focus on remove excess ink and in gestalt principles
Used style "fivethirtyeight" and customized each plot to reach this project goal; use verticial line/horizontal line, added span areas and used arrow to identify a specific point in graphics https://github.com/midlourenco/Dataquest.io/blob/main/project_5-%20euro%20daily%20exchange%20rates/20210817%20-%20Project%205%20-%20euro%20exchange%20rate.ipynb

Clean and Analyze Employee Exit Surveys from 2 organization in Australia
Objectives: To find the different factors that affect employee resignations and identify the relation between dissatisfaction and the lenght of period that employee worked at the institute.
To reach this goal we applied following tools for data cleaning and analyze. * Vectorized string methods to clean string columns * The apply(), map(), and applymap() methods to transform data * The fillna(), dropna(), and drop() methods to drop missing or unnecessary values * The melt() function to reshape data * The concat() and merge() functions to combine data * Basic Regular Expressions * Create customized dataframe bins with specific label * Create pivot table, pie plots and bar plots to show the results
https://github.com/midlourenco/Dataquest.io/blob/main/project_6%20-%20clean%20and%20analyze%20employee%20exit%20surveys/20210903%20-%20Project%20No.%206%20-%20%20Clean%20and%20Analyze%20Employee%20Exit%20Surveys.ipynb