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prerequisites
basic Python (Jupyter Notebook, pandas, RegEx) • basic machine learning and feature engineering
skills learned
feature engineering with pandas • cleaning, standardizing, and transforming data with pandas
Ruihao Qiu
1 week · 2+4 hours per week · INTERMEDIATE
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Master the basic methods for handling most real-world scenarios as you play the role of a data scientist in an online recruiting tech company or a large organization’s HR department. Using pandas, you’ll process, extract, and transform numerical, categorical, time series, and text data into structured features that are ready for data analysis and ML model training. When you’re done, you’ll have hands-on experience working with most data types you’ll find in the real world, as well as useful skills for extracting and engineering features.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Ruihao Qiu

Ruihao Qiu is the senior data scientist at a German tech company and has more than five years experience in data science and machine learning. As part of the process of earning his PhD in statistical physics, he developed statistical models to simulate and search for new nanomaterials. In his early days as a data science consultant, he helped his clients from DAX30 multinational companies solve real-world data challenges. As a senior data scientist, he designed and built data pipeline and ML recommender systems for online recruitment applications. He enjoys taking on different career roles and sharing ideas about his data science work in tech blogs and in public presentations.

prerequisites

This liveProject is for Python beginners interested in learning feature extraction and engineering using pandas. To begin these liveProjects you’ll need to be familiar with the following:

TOOLS
  • Basic Python
  • Basic Jupyter Notebook/JupyterLab
  • Intermediate pandas
TECHNIQUES
  • Basics of regular expressions
  • Basics of trie data structure
  • Basics of data types

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