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

Hashing is an efficient way to sort key values in memory. Hashing is used to get the values associated with the key very fast. Some examples of hash tables are:

- Employee ID, which is used to extract all the records of an employee.

- Stock ID, which is used to get the transaction of stock during the day/week/month.

- Book ISBN number, which is used to extract author, pages, price, and other relevant information.
 

As we can see in the above examples, keys can go to a large number, and each are unique. The value associated with the key needs to be extracted fast. Hash tables do this work.




Learn about the hash tables. Hash tables are required to store the data in key-value pairs for faster access on the keys.


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

Hash Tables