Welcome to the DataCamp Projects Showcase! This repository contains my solutions to various DataCamp projects, each focusing on real-world data analysis using different tools and programming languages.
DataCamp projects provide a hands-on approach to applying the skills gained from DataCamp courses. With each project, you'll dive into end-to-end analysis of practical tasks using real-world tools and workflows. Whether it's Python, SQL, or other technologies, you'll gain experience in solving data-related challenges.
- Why DataCamp Projects?
- Project Highlights
- Project - Introduction to DataCamp Projects
- Project - A New Era of Data Analysis in Baseball
- Project - The GitHub History of the Scala Language
- Project - The Android App Market on Google Play
- Project - Investigating Netflix Movies and Guest Stars in The Office
- Project - A Visual History of Nobel Prize Winners
- Explore and Learn
Here's a glimpse of some exciting projects in this repository:
Project Description
This is an introduction to DataCamp projects. DataCamp projects allow you to apply the skills you have learned in DataCamp courses. In each project, you will carry out an end-to-end analysis on real-world tasks using real-world tools and workflows.
In doing so, you will learn how to work with Jupyter notebooks: an open-source web application that is great for interactive data analysis.
Current Solution Version : SQL
Project Description
There's a new era of data analysis in baseball. Using a new technology called Statcast, Major League Baseball is now collecting the precise location and movements of its baseballs and players. In this project, you will use Statcast data to compare the home runs of two of baseball's brightest (and largest) stars, Aaron Judge (6'7") and Giancarlo Stanton (6'6"), both of whom now play for the New York Yankees.
The dataset used in this project is from Baseball Savant
Current Solution Version : Python
Project Description
Open source projects contain entire development histories, such as who made changes, the changes themselves, and code reviews. In this project, you'll be challenged to 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). With almost 30,000 commits and a history spanning over ten years, Scala is a mature language. You will find out who has had the most influence on its development and who are the experts.
The dataset includes the project history of Scala retrieved from Git and GitHub as a set of CSV files.
Current Solution Version : Python
Project Description
Mobile apps are everywhere. They are easy to create and can be lucrative. Because of these two factors, more and more apps are being developed. In this project, you will do a comprehensive analysis of the Android app market by comparing over ten thousand apps in Google Play across different categories. You'll look for insights in the data to devise strategies to drive growth and retention. The data for this project was scraped from the Google Play website. While there are many popular datasets for Apple App Store, there aren't many for Google Play apps, which is partially due to the increased difficulty in scraping the latter as compared to the former. The data files are as follows:
apps.csv: contains all the details of the apps on Google Play. These are the features that describe an app. user_reviews.csv: contains 100 reviews for each app, most helpful first. The text in each review has been pre-processed, passed through a sentiment analyzer engine and tagged with its sentiment score.
Current Solution Version : Python
Project Description
In this project, you’ll apply the skills you learned in Introduction to Python and Intermediate Python to solve a real-world data science problem. You’ll press “watch next episode” to discover if Netflix’s movies are getting shorter over time and which guest stars appear in the most popular episode of "The Office", using everything from lists and loops to pandas and matplotlib.
You’ll also gain experience in an essential data science skill — exploratory data analysis. This will allow you to perform critical tasks such as manipulating raw data and drawing conclusions from plots you create of the data. Press play to begin!
Current Solution Version : Python
Project Description
The Nobel Prize is perhaps the world's most well known scientific award. Every year it is given to scientists and scholars in chemistry, literature, physics, medicine, economics, and peace. The first Nobel Prize was handed out in 1901, and at that time the prize was Eurocentric and male-focused, but nowadays it's not biased in any way. Surely, right?
Well, let's find out! What characteristics do the prize winners have? Which country gets it most often? And has anybody gotten it twice? It's up to you to figure this out.
The dataset used in this project is from The Nobel Foundation on Kaggle.
Feel free to explore the projects in this repository to witness how data analysis skills are applied in real-world scenarios. Each project demonstrates the power of data-driven insights and provides an opportunity to learn, experiment, and contribute.
Enjoy your journey through the world of data analysis and exploration!
Current Solution Version : Python