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

<h2>Algorithm families</h2>
When we start solving a problem, we can do it in different ways. Not every way is optimal. Based on the problem, we have to decide which technique is best. Here are some of the techniques we’ll be learning in the upcoming lessons: 


- Greedy algorithm
- Divide and conquer
- Backtracking
- Branch and bound
- Dynamic programming




<h2>Greedy algorithms </h2>
Greedy algorithms, as the name suggests, take the greedy, or best, approach at any moment. The overall best solution is the combination of the smaller best solutions.

To make the correct choice, we should follow an objective function. Algorithm decisions are based on the value of the objective function at that time. Consider the example of minimizing cost. Our objective is to get the minimum value. We make the minimum value decision at each step, which will give us the minimum cost.



<h2>Properties of greedy algorithms </h2>
<li> Easy to start with the solution. 
<li> Analyzing the run time of a greedy solution is easy.
<li> Verifying the correctness is difficult.

We can understand the greedy approach through the graph below. Our objective is to go from A to K with the minimum cost. When you travel from any edge, the overall cost increases with each step. You can only move forward. Let’s take a look with this graph.




The greedy approach will choose the best decision at any point. The movement from $A -> B$ and from $A->C$, $A->B$ is selected, as they have the minimum cost for this data. Using the greedy technique, we get the path $A->B->D->G->J->K (Cost = 12+ 9 + 11 + 9 + 23 = 64)$


Introduction to the concept of Greedy Algorithms.

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

Greedy Algorithms

Algorithm families

Greedy algorithms

Properties of greedy algorithms