More Python Tips, Tricks, and Techniques for Data Science
1h 58mIntermediate2020-10-01
Authors
Harshit Tyagi
Data Science Instructor and Mentor
Course details
The power and versatility of Python—coupled with its large ecosystem of third-party packages—make it indispensable to data scientists. In this course, instructor Harshit Tyagi shares practical tips and techniques that can help you enhance your own Python data science workflow. Harshit covers how to work with IPython notebooks, including how to debug errors. He shows how to use NumPy to manipulate arrays, as well as how to work with pandas, the data manipulation and analysis tool. He provides tips for visualizing your data with Matplotlib, explaining how to add text to plots and annotate elements on a chart. Plus, get best practices for working with scikit-learn, as well as other machine learning tips.
Skills covered
Tips, Tricks, & TechniquesData Science FoundationsPythonProgramming LanguagesData ScienceOpen SourceSoftware Development
Concepts
Introduction
- Tips and tricks in Python
IPython and Jupyter Notebook
- Accessing methods and documentation
- Errors and debugging
- Code profiling and timing
NumPy and Pandas
- Essentials of NumPy arrays
- Broadcasting
- Comparison, masks, and Boolean logic
- Pandas indexing and subsetting
- Handling missing data
- Aggregation and grouping
- Querying and filtering data
Visualization with Matplotlib
- General plotting tips
- Adding text and annotations
- Multiple subplots
Machine Learning Tips
- sklearn Estimator API
- Model validation and hyperparameter tuning
- Feature engineering
- Creating machine learning pipelines
Conclusion
- Next steps