Data Science Projects with Python will help you get comfortable with using the Python environment for data science. This course will start you on your journey to mastering topics within machine learning. These skills will help you deliver the kind of state-of-the-art predictive models that are being used to deliver value to businesses across industries.
- Install the required packages to set up a data science coding environment
- Load data into a Jupyter Notebook running Python
- Use Matplotlib to create data visualizations
- Fit a model using scikit-learn
- Use lasso and ridge regression to reduce overfitting
- Fit and tune a random forest model and compare performance with logistic regression
- Create visuals using the output of the Jupyter Notebook
For an optimal experience, we recommend the following hardware configuration:
- Processor: Intel Core i5 or equivalent
- Memory: 4 GB RAM
- Storage: 35 GB available space
You'll also need the following software installed in advance:
- OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit, Ubuntu
- Linux, or the latest version of OS X
- Browser: Google Chrome/Mozilla Firefox Latest Version
- Notepad++/Sublime Text as IDE (this is optional, as you can practice everything using the Jupyter Notebook on your browser)
- Python 3.7+ (latest version of Python is recommended) installed (from https://python.org)
- Python libraries as needed (Jupyter, NumPy, Pandas, Matplotlib, and so on)