Given here are a set of projects that I had to complete for my Udacity Data Analyst Nanodegree. What you can also see here are some extra projects I set out to do myself. I have also cleaned a lot of the projects required for the Nanodegree.
All of the projects deal with some aspect of data science, namely:
- Data Wrangling
- Data Cleaning
- Exploratory Data Analysis
- Data Visualization
- Machine Learning
- Natural Language Processing
- Descriptive Statistics
- Inferential Statistics
- Web Scraping
The languages used are:
- Python
- Jupyter notebooks
- R
While the READMEs for the individual projects list the techniques used, and almost all projects use all data analysis techniques, some techniques are used more often or are the focus of the projects. These are mentioned in bold in parenthesis.
Projects done during the course:
- Investigating the Titanic dataset (INVESTIGATING DATA)
- Wrangling OpenStreetMap Data (DATA WRANGLING & CLEANING)
- Determining Factors Responsible for Red Wine Quality (EXPLORATORY DATA ANALYSIS)
- Drawing Cards from a Deck (DESCRIPTIVE STATISTICS)
- Testing a Perceptual Phenomenon (INFERENTIAL STATISTICS)
- Creating a Story in Tableau (VISUALIZATION)
- Detecting Fraud at Enron (MACHINE LEARNING)
Extra projects undertaken:
- Scraping US Air Traffic Data (WEB SCRAPING)
- Analyzing Amazon.com Reviews (MACHINE LEARNING & NLP)
- Scraping Amazon.com Ratings (MACHINE LEARNING & NLP)