The ultimate guide to data science interviews. Developed by FAANG engineers, it covers Python, algorithms, ML concepts, and interview questions. Get interview-ready in just a few hours.

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This course will increase your skills to crack the data science or machine learning interview. You will cover all the most common data science and ML concepts coupled with relevant interview questions.
You will start by covering Python basics as well as the most widely used algorithms and data structures. From there, you will move on to more advanced topics like feature engineering, unsupervised learning, as well as neural networks and deep learning.
This course takes a non-traditional approach to interview prep, in that it focuses on data science fundamentals instead of open-ended questions.

In all, this course will get you ready for data science interviews. By the time you finish this course, you will have reviewed all the major concepts in data science and will have a good idea of what interview questions you can expect.

Data Science Interview Handbook

Categorical features take a fixed or small set of values. Consider the example of Gender (Male, Female) or education of a person (High school, college, masters, Ph.D., etc). Even if the data contains features that have a fixed set of values, we can mark it as a categorical feature. For example, consider the error code in the manufacturing process. We can receive an error code in case of any process failures. This code may belong to a predefined set of values and are qualified as categorical features.


# Types
Categorical features are classified into two categories:

- Nominal features – Labels are given without any order or precedence over each other.
- Ordinal features – Labels have some order associated with them.

# Challenges
Challenges for categorical feature transformations:

- Some machine learning algorithms require data in the numerical format. We cannot use all possible conversions of categorical data in that case.
- High Cardinality - Categorical features can have a high number of distinct values which refers to high cardinality, e.g., variables with 1000+ categories where a few are used only once or twice in the dataset.
- Categorical data conversion requires some domain knowledge. For example, encoding treats California, New Mexico, and Mumbai the same. If we want to create one encoding for all US locations and another for locations outside of the US, we have to give that input explicitly.


Learn about categorical features variants and their encoding methods.


Are You Ready to Become a Data Scientist?

Python Basics

Python Libraries

More Data Science Tools

Data Structures and Algorithms - I

Data Structures and Algorithms - II

Statistics and Probability

Feature Engineering

Basics of Machine Learning

Regression

Classification

Unsupervised Learning

Advanced Topics in Machine Learning

Conclusion

Categorical Features