Data Science 🐍 Prepare Data

Data Science 🐍 Prepare Data

Assessment

Interactive Video

Information Technology (IT), Architecture, Social Studies

12th Grade - University

Practice Problem

Hard

Created by

Wayground Content

FREE Resource

This video tutorial covers the essentials of preparing data for data science projects. It begins with an introduction to data preparation, emphasizing the importance of conditioning raw data. The tutorial then explores techniques for visualizing and cleaning data, including handling outliers using tools like Numpy and Pandas. It further delves into methods for identifying and removing outliers through statistical tests and conditions. Finally, the video discusses the significance of scaling data and splitting it into training and testing sets to ensure effective machine learning model development.

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10 questions

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1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Why is it important to condition raw data before applying data science techniques?

To increase the size of the dataset

To ensure the data is in a usable format

To make the data more colorful

To make the data more complex

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which library function can be used to drop missing values in a dataset?

Numpy's dropna

Pandas' dropna

Scikit-learn's dropna

Matplotlib's dropna

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the purpose of using a box plot in data visualization?

To make the data more complex

To increase the data size

To identify outliers in the data

To add colors to the data

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is a limitation of the Grubbs test?

It can only detect a single outlier

It can only detect positive numbers

It can only detect missing values

It can only detect even numbers

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Why is scaling data important for machine learning algorithms?

To make the data more complex

To increase the data size

To ensure algorithms perform optimally

To make the data more colorful

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What does the standard scaler do to the data?

It adds random noise

It normalizes data to zero mean and unit variance

It removes all outliers

It duplicates the data

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the typical split ratio for training and testing datasets?

50% training, 50% testing

70% training, 30% testing

80% training, 20% testing

90% training, 10% testing

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