- On this repo, I shared the projects that I completed on the Data Scientist with Python Career Track of Data Camp.
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With Intermediate Python under your belt, you can already analyze and extract meaningful insights from various sources. For this set of projects, you will use a combination of data manipulation and visualization to explore television data.
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In this project's guided variant, you will look at Super Bowl Data, generating insights into game outcomes, viewership, and even halftime shows. In the unguided variant of this project, you'll develop an informative plot that helps to visualize the viewership and quality of The Office throughout its nine seasons.
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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.
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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.
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The dataset includes the project history of Scala retrieved from Git and GitHub as a set of CSV files.
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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.
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The dataset used in this project is from The Nobel Foundation on Kaggle.
- In 1847, the Hungarian physician Ignaz Semmelweis made a breakthough discovery: he discovers handwashing. Contaminated hands was a major cause of childbed fever and by enforcing handwashing at his hospital he saved hundreds of lives.
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Commercial banks receive a lot of applications for credit cards. Many of them get rejected for many reasons, like high loan balances, low income levels, or too many inquiries on an individual's credit report, for example. Manually analyzing these applications is mundane, error-prone, and time-consuming (and time is money!). Luckily, this task can be automated with the power of machine learning and pretty much every commercial bank does so nowadays. In this project, you will build an automatic credit card approval predictor using machine learning techniques, just like the real banks do.
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The dataset used in this project is the Credit Card Approval dataset from the UCI Machine Learning Repository.