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

In this lesson, we will apply feature engineering to a small dataset. You will use all the techniques that have been presented in the previous lessons. On each step, you also have the choice to apply other similar data processing techniques. 

First, we start by taking the library and importing the data.

Date_Of_Action,Country,Operating_System,Number_of_hits,Malicious
24/05/2020,US,Windows,15,Yes
25/05/2020,US,Unix,78,No
25/05/2020,US,Windows,3,No
26/05/2020,IND,Windows,9,Yes
26/05/2020,US,Unix,87,No
26/05/2020,IND,Windows,2,No
25/05/2020,IND,Unix,41,Yes
25/05/2020,US,Unix,33,Yes
27/05/2020,IND,Windows,,Yes
29/05/2020,IND,Windows,76,No
29/05/2020,US,Unix,84,No
29/05/2020,US,Windows,61,No
30/05/2020,US,Unix,48,No
1/6/2020,UK,Unix,,No
1/6/2020,UK,Windows,37,No
1/6/2020,US,Windows,44,No
1/6/2020,US,Unix,59,No
2/6/2020,UK,Windows,,No
2/6/2020,UK,Unix,98,No
2/6/2020,US,Unix,97,No


#Import pandas library
import pandas

#Read data
data = pandas.read_csv("data_feature_engineering.csv")

#We convert the Date_of_action feature to pandas datetime
data['Date_Of_Action'] =  pandas.to_datetime(data['Date_Of_Action'], infer_datetime_format=True)

#Create is_Weekend feature
data['is_Weekend'] = (data['Date_Of_Action'].dt.dayofweek // 5 == 1).astype(int)

#print top 10 rows
print(data.head(10))

In the above code, we have imported a pandas library. We read the data into the data frame which contains the features. We have a total of four features - Date_of_Action, Country, Operating_System, and Number_of_hits. The data has one label Malicious. 

First, we convert the Date_of_action feature to pandas datetime. We will run the following code.

``` python
data['Date_of_Action'] =  pandas.to_datetime(data['Date_of_Action'], infer_datetime_format=True)
```

Now, we have the date feature correctly formulated. The next task is to create the feature is_Weekend. We can do this by running the following code.

``` python
data['is_Weekend'] = (data['Date_of_Action'].dt.dayofweek // 5 == 1).astype(int)
```

Now, let's put the complete code together.


Let's put all the concepts together regarding feature engineering, 

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

Putting Everything Together!