The repository documents the individual projects of the WBS Data Science Bootcamp.
| Prep | Setting up Tableau for use in Linux (Manjaro/Arch) |
| Project 1: Basic and advanced SQL queries | Answering business questions with MySQL and Tableau. The project assesses the viability of using the services of a logistics company as part of a tech company's market entry (e.g. assessing product spaces, delivery quality). |
| Project 2: Python, Pandas, and Seaborn | Answering business questions using Python libraries Pandas and Seaborn. Cleaning and categorizing data by price, product, and discount categories to perform discount analysis on sales events. |
| Project 3: Data Engineering | Read my Medium article on how to set up a data pipeline (ETL) using web scraping, API requests, SQL storage, AWS cloud storage, and automation. |
| Project 4: Unsupervised Machine Learning | Clustering Spotify songs according to their audio features using KMeans (2D-,3D-, radar plots). Paste your own Spotify playlist and after pre-processing data (i.e. calculating inertia and silhouette scores) choose the number of clusters. |
| Project 5: Supervised Machine Learning | Joining students' (1,2) and Kaggle competitions to estimate house prices using classification and regression techniques with various machine learning models in Scikit Learn. Also, classifying poisonous mushrooms adjusting thresholds to avoid false negative predictions. |
| Project 6: Recommender system | Developing a movie recommender system (to be uploaded) |
| Final Project: Data Visualization | Developing a dashboard for political and economic data of developing countries, using plotly dash. The result can be viewed in this separate repo: https://github.com/jarisdata/asian_development_data/ |
