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This repo involves a series of projects engaged in @ Springboard's Data Science: Machine Learning Bootcamp program.
Throughout the course, candidates get exposed to several Data Science Industry leaders, concepts, tools, and software libraries. The devices are critical for the success of practicing Data Scientists, especially in the machine learning world. Topics include everything from ML libraries to deployment tools. There are also refreshers on software engineering best practices and foundational math concepts that every ML Engineer should know.
Overview of topics:
Chapter point 1: The Python Data Science Stack: Pandas, scikit-learn, Keras, and TensorFlow
Chapter point 2: Data engineering tools: Spark/PySpark, Luigi, Containers, AWS, and Azure
Chapter point 3: Software engineering tools: Continuous integration, version control with Git, logging, testing, and debugging
Chapter point 4: Data structures and algorithms refresher and optimizing Python to write faster code
I hope you enjoy checking out this Data Wrangling Exercise on api as it was challenging but fun working through the material!
Reference:
- Connect with me on LinkedIn!!: https://www.linkedin.com/in/alfredhull/
- Python SQL Documentation: https://pypi.org/project/python-sql/.
- http://news.datascience.org.ua/2019/01/11/unleash-the-power-of-jupyter-notebooks/