DataCamp Projects
My Collection of Projects at DataCamp. These exercises are taken from the course Associate Data Scientist in Python from DataCamp. Here’s a brief description of each project.
Introduction to DataCamp Projects
This notebook serves as an Introduction to DataCamp Projects.
Introduces what a Jupyter notebook is, showing how to run code cells.
Show how to load and show pandas DataFrames.
Show how to show inline matplotlib plots.
Show the interactive output of Maps.
Habilities
Data Manipulation,Data Visualization,Importing & Cleaning Data,Case Studies
Links
Investigating Netflix Movies and Guest Stars in The Office
In this project, I apply the foundational Python skills by manipulating and visualizing movie and TV data.
Some data manipulation with Pandas is done.
The database is loaded from a CSV file.
Matplotlib is used to show visualizations of the data.
The films are analyzed by genre, to determine if this is correlated with their duration.
Habilities
Data Manipulation,Data Visualization,Programming,Case Studies
Links
The GitHub History of the Scala Language
Find the true Scala experts by exploring its development history in Git and GitHub.
We read in, clean up, and visualize the real-world project repository of Scala that spans data from a version control system (Git) as well as a project hosting site (GitHub).
We find out who has had the most influence on its development and who are the experts.
Habilities
Data Manipulation,Data Visualization,Importing & Cleaning Data
Links
The Android App Market on Google Play
Load, clean, and visualize scraped Google Play Store data to gain insights into the Android app market.
We do a comprehensive analysis of the Android app market by comparing over ten thousand apps in Google Play across different categories.
We look for insights into the data to devise strategies to drive growth.
Habilities
Data Manipulation,Data Visualization,Probability & Statistics,Importing & Cleaning Data
Links
A Visual History of Nobel Prize Winners
Explore a dataset from Kaggle containing a century’s worth of Nobel Laureates.
The dataset is analyzed to look for biased data according to gender and nationality.
Other analyses are carried out, including how old the winners of Nobel prizes are, the differences between price categories, the oldest and younger winners, etc.
Habilities
Data Manipulation,Data Visualization,Importing & Cleaning Data
Links
Skills
Python
Pandas
Sckit-Learn
Habilities
Data Visualization
Data Manipulation
Importing & Cleaning Data
Programming
Probability & Statistics