Miscellaneous notes from the last few courses of my datascience journey through Datacamp.
These were notes taken from within the [Data Scientist with Python](https://www.datacamp.com/tracks/data-scientist-with-python. track.
- Bokeh Plotting - using bokeh for interactive plots.
- Conda essentials - maintaining your conda environment - using venv.
- PostGreSQL - misc PostGreSQL commands for backend data manipulation.
- Unsupervised Learning in python - mainly clustering using methods such as KMeans,
- Deep Learning in Python using the Keras framework.
- Network Analysis in python - Misc. Graph theory exploration using NxViz.
- Datascience Project - Estimation of school budgets using Supervised Learning - Classification
- Stamform Open Policing project - Estimation of factors that cause bookings (Clustering problem)
Books that were read on the subject, to perfect the art of Data science in general.
- Machine Learning Yearning, by Andrew Ng
- Python for Data Analysis: Data Wrangling With Pandas, NumPy and IPython, by Wes McKinney
- The Signal And The Noise: Why So Many Predictions Fail – But Some Don’t, by Nate Silver
- Automate This: How Algorithms Came To Rule Our World, by Christopher Steiner
- Storytelling With Data: A Data Visualization Guide for Business Professionals, by Cole Nussbaumer Knaflic
- Inflection Point: How the Convergence of Cloud, Mobility, Apps, and Data Will Shape the Future of Business, by Scott Stawski
- Hadoop, the Definitive Guide: Storage and Analysis at an Internet Level, by Tom White
- Doing Data Science: Straight Talk from the Frontline, by Cathy O’Neil and Rachel Schutt