Data Cleaning in Python faq

star-rating
4.6
learnersLearners: 2,269
instructor Instructor: Taimoor khan instructor-icon
duration Duration: duration-icon

This course on Data Cleaning in Python is perfect for beginners who want to learn the preprocessing steps to improve the validity, accuracy, completeness, consistency and uniformity of data. It covers common problems with data such as missing values, noise values, outliers, data duplication, standardizing and normalizing data, and dealing with categorical features. The course provides theoretical explanation, mathematical evaluation and code for each concept, all written in Python using Jupyter Notebook. Click now to learn the data cleaning skills and make useful analysis with your business data!

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Course Feature Course Overview Course Provider Discussion and Reviews
Go to class

Course Feature

costCost:

Paid

providerProvider:

Udemy

certificateCertificate:

Paid Certification

languageLanguage:

English

start dateStart Date:

2022-08-17

Course Overview

❗The content presented here is sourced directly from Udemy platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [July 27th, 2023]

This course provides an introduction to data cleaning and preprocessing in Python. It covers common issues with data, such as missing values, noise values or univariate outliers, multivariate outliers, data duplication, and improving the quality of data through standardizing and normalizing it, as well as dealing with categorical features. The course provides theoretical explanation, mathematical evaluation, and code for each concept. Lectures are organized into sections, with the first number referring to the section number and the second number referring to the lecture number within the section. All code is written in Python using Jupyter Notebook. Data cleaning is essential for building reliable machine learning models that can produce good results. Without sorting out the issues in the data, the results of even the most efficient models would be unreliable. This course provides the skills necessary to make useful analysis with business data acquired from multiple online sources.

Course Syllabus

Introduction

Detecting Missing and Noise Values (Univariate Outliers)

Handling Missing and Noise Values (Univariate Outliers)

Multivariate Outliers

Anomalies in Textual data

Structuring Textual Documents

Feature Scaling (Normalization)

Handling Categorical Features

Machine Learning Overview

Data Acquisition

Course Provider

Provider Udemy's Stats at AZClass

Discussion and Reviews

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faq FAQ for Data Preprocessing Courses

Q1: Does the course offer certificates upon completion?

Yes, this course offers a paid certificate. AZ Class have already checked the course certification options for you. Access the class for more details.

Q2: How do I contact your customer support team for more information?

If you have questions about the course content or need help, you can contact us through "Contact Us" at the bottom of the page.

Q3: How many people have enrolled in this course?

So far, a total of 2269 people have participated in this course. The duration of this course is hour(s). Please arrange it according to your own time.

Q4: How Do I Enroll in This Course?

Click the"Go to class" button, then you will arrive at the course detail page.
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(Please note that the following steps should be performed on Udemy's official site.)
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Once in the cart, select the course you want and click "Enroll."
Udemy may offer a Personal Plan subscription option as well. If the course is part of a subscription, you'll find the option to enroll in the subscription on the course landing page.
If you're looking for additional Data Preprocessing courses and certifications, our extensive collection at azclass.net will help you.

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