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

KNIME is an analytic platform that visualizes data. It is a free tool meaning we do not need a paid license to install and use KNIME.

The KNIME analytics platform is based on the graphical programming paradigm. The platform has a number of extensions for text mining, big data operations, and language integrations (for languages such as Java, Python, and R).


<h2>Nodes</h2>
KNIME contains small nodes. Nodes are able to perform doing some tasks and produce their results. Nodes have different states. Initially, nodes are in the 'not configured state'. After configuration, nodes moved to the idle state. If the execution is successful, nodes move to the executed state. If the execution is failed due to some error, nodes move to the error state. Here is the example of a node:


Introduction to a data analytics tool KNIME. KNIME is a freely  and  widely used tool in the data science community.


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

KNIME

Nodes