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Data Cleaning in Python

Learners: 2,269

Instructor: Taimoor khan

Duration:

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!▼▲

Course Feature Course Overview Course Provider

Go to class

Course Feature

Cost:

Paid

Provider:

Udemy

Certificate:

Paid Certification

Language:

English

Start 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.