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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

Data Science FoundationsTips, Tricks, & TechniquesPythonProgramming LanguagesData ScienceOpen SourceSoftware Development

Concepts

0. Introduction

01 - Tips and tricks in Python

1. IPython and Jupyter Notebook

02 - Accessing methods and documentation
03 - Errors and debugging
04 - Code profiling and timing

2. NumPy and Pandas

05 - Essentials of NumPy arrays
06 - Broadcasting
07 - Comparison, masks, and Boolean logic
08 - Pandas indexing and subsetting
09 - Handling missing data
10 - Aggregation and grouping
11 - Querying and filtering data

3. Visualization with Matplotlib

12 - General plotting tips
13 - Adding text and annotations
14 - Multiple subplots

4. Machine Learning Tips

15 - sklearn Estimator API
16 - Model validation and hyperparameter tuning
17 - Feature engineering
18 - Creating machine learning pipelines

Conclusion

19 - Next steps