DSTar.tar.gz

code-job

r-job

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

In the early stages of a project, creating visualizations helps with Exploratory Data Analysis (EDA). Visualizations allow us to gain insights into our data by making things easier to understand. When we place data into a visual context patterns, trends, and correlations that might have otherwise gone undetected rise to the surface.

In the middle stages of a project, we can use visualizations to gain more insights and generalize model results.


Towards the end of a project, it’s very important to be able to present our results in a **clear, concise, and compelling manner** so that our audience (which is often made up of non-technical people) can understand the results.

Introduction to different data visualization libraries that are used in Python.


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

Data Visualization