Data Science Nanodegree Repository

Description

"Data scientists interpret, extrapolate from, and prescribe from data to deliver actionable recommendations."

• Udacity

Important files and Notes

The Data Science Nanodegree is split into several sections each with their own lecture materials and projects:

1. Supervised Learning
2. Deep Learning
3. Unsupervised Learning
4. Data Science Blog Post
5. Disaster Response Pipeline (Twitter Webapp)
6. Recommendation Engines
7. Spark ML

Hence, I have broken up the files numerically according to this order.

Learning Material Files:

• Lecture notes as GIMP xcf files (In part 2 notes are in PDF format)
• Folders containing code implementations

Project Files Notes:

• For 02_deep-learning_project
  • Notebooks were written in an GPU enabled workspace. Hence, this file will only work if CUDA is installed on your PC.
  • Data images are also too large to be committed onto GitHub
• For 07_capstone_spark-ML_project
  • Notebook was written in a Spark enabled workspace and will likely not work if you haven't downloaded and installed spark on your local machine.
  • Data Used is only a subset of the whole gigabyte size dataset, also wont be commited onto GitHub

References and Citations#

See project subdirectories