These are my personal notes taken while following the Udacity Data Science Nanodegree.
The Nanodegree assumes basic data analysis skills with python libraries (pandas, numpy, matplotlib, sklearn, etc.) and has 5 modules that build up on those skills; each module has its corresponding folder in this repository with its guide Markdown file:
- Introduction to Data Science:
01_Intro_Data_Science. - Software Engineering:
02_SoftwareEngineering. - Data Engineering:
03_DataEngineering. - Experimental Design & Recommendations:
04_ExperimentalDesign_RecSys. - Data Scientist Capstone (Spark):
05_Capstone_Project.
Additionally, it is necessary to submit and pass some projects to get the certification:
- Create a data science project and write a blog post: airbnb_data_analysis.
- Disaster response prediction pipeline deployed on a Flask app: disaster_response_pipeline.
- Recommender System which suggests new articles to the users of the IBM Watson Studio Platform: recommendations_ibm.
- Capstone project: Spark Big Data Guide.
A regular python environment with the usual data science packages should suffice (i.e., scikit-learn, pandas, matplotlib, etc.); any special/additional packages and their installation commands are introduced in the guides. A recipe to set up a conda environment with my current packages is the following:
conda create --name ds pip python=3.10 conda activate ds pip install -r requirements.txt
As a side note, I list here some related free Udacity courses on several topics:
- Big Data
- Databases and APIs
- Interview Preparation
Mikel Sagardia, 2022.
No guarantees.