My tire engineering work at Pratt Miller found me doing heavy data analysis of tire test data and mathematical modeling of tire behavior. I had a co-worker who had a very strong data science background who made interesting use of Python to represent information. I wanted to learn about more about data analysis through some formal instruction, and more about Python, because I used primarily MATLAB but was interested in Python's functionality and open source environment.
Work was done within the IBM Watson Studio environment for most projects, often using Python Jupyter Notebooks. The different tasks required use of various Python toolboxes such as Pandas, Numpy, SciKitLearn, MatPlotLib, Seaborn, Folium, Plotly, etc. The certification included 10 courses:
- What is Data Science?
- Tools for Data Science
- Data Science Methodology
- Python for Data Science, AI & Development
- Python Project for Data Science
- Databases and SQL for Data Science with Python
- Data Analysis with Python
- Data Visualization with Python
- Machine Learning with Python
- Applied Data Science Capstone
The general process/methodology the courses reinforced is shown below.
