Data Cleaning in Python - Edu Lowcostlivin

Course Feature

Cost

Paid
Provider

Udemy
Certificate

Paid Certification
Language

English
Start Date

2022-08-17
Learners

No Information
Duration

No Information
Instructor

Taimoor khan

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4.6

2,269 Ratings

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 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

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