Data Cleaning in Python

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Product is rated as #77 in category Data Science
Learning Experience8.2

Data Cleaning in Python, towards making the data more constant and high quality prior to training predictive designs. This course targets students that are beginners to data science and machine learning.

Last updated on November 13, 2021 7:45 pm

About this course

Data cleaning in python is really crucial from the point of view of building intelligent systems. Data cleaning is a preprocessing action that enhances the data credibility, precision, efficiency, consistency, and uniformity. It is important for developing reliable machine learning models that can produce excellent outcomes. Otherwise, no matter how excellent the model is, its outcomes cannot be relied on. Beginners with machine learning start dealing with the openly available datasets that are completely examined with such problems and are for that reason prepared to be utilized for training models and getting excellent outcomes.

But it is far from how the data is, in real life. Common issues with the data might consist of missing values, sound values or univariate outliers, multivariate outliers, data duplication, enhancing the quality of data through standardizing and stabilizing it, handling categorical functions.

The datasets that are in raw form and have all such problems cannot be benefited from, without understanding the data cleaning and preprocessing. The data retrieved from several online sources, for developing a beneficial application, is a lot more exposed to such issues. Therefore, finding out the data cleaning abilities assist users to make beneficial analyses with their organization data. Otherwise, the term ‘trash in trash out’ describes the truth that without figuring out the problems in the data, no matter how effective the design is, the outcomes would be undependable.

You’ll learn

In this course on Data cleaning in python, we will talk about the typical issues with data, originating from various sources. We likewise talk about and carry out how to solve these problems handsomely. Each principle has 3 elements that are theoretical description, mathematical assessment, and code. Lecture one describes the theory and mathematical assessment of a principle while the lectures. Lecture two describes the useful code of each principle.  All the codes have been written in Python using Jupyter Notebook.

Syllabus

  • Introduction to Data cleaning in python
  • Detecting Missing target and Noise Values (Univariate Outliers)
  • Handling Missing Noise Values (Univariate Outliers)
  • Multivariate Outliers
  • Anomalies in Textual data
  • Structuring Textual Documents

Similar courses on Data Cleaning

$19.99

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  • Udemy
  • Online Course
  • Self-paced
  • Beginner
  • Less Than 24 Hours
  • Paid Course (Paid certificate)
  • English
  • Python
  • Jupyter Notebook
  • Basic Scripting in Python
  • Data Cleaning Python Programming
Learning Experience
8.2
PROS: Well designed course content Every concept is explained very well Programming aspects of each concept can be understood by anyone who has basic knowledge of Python.
CONS: The example code is mostly list based instead of data frame-based

Description

About this course

Data cleaning in python is really crucial from the point of view of building intelligent systems. Data cleaning is a preprocessing action that enhances the data credibility, precision, efficiency, consistency, and uniformity. It is important for developing reliable machine learning models that can produce excellent outcomes. Otherwise, no matter how excellent the model is, its outcomes cannot be relied on. Beginners with machine learning start dealing with the openly available datasets that are completely examined with such problems and are for that reason prepared to be utilized for training models and getting excellent outcomes.

But it is far from how the data is, in real life. Common issues with the data might consist of missing values, sound values or univariate outliers, multivariate outliers, data duplication, enhancing the quality of data through standardizing and stabilizing it, handling categorical functions.

The datasets that are in raw form and have all such problems cannot be benefited from, without understanding the data cleaning and preprocessing. The data retrieved from several online sources, for developing a beneficial application, is a lot more exposed to such issues. Therefore, finding out the data cleaning abilities assist users to make beneficial analyses with their organization data. Otherwise, the term ‘trash in trash out’ describes the truth that without figuring out the problems in the data, no matter how effective the design is, the outcomes would be undependable.

You’ll learn

In this course on Data cleaning in python, we will talk about the typical issues with data, originating from various sources. We likewise talk about and carry out how to solve these problems handsomely. Each principle has 3 elements that are theoretical description, mathematical assessment, and code. Lecture one describes the theory and mathematical assessment of a principle while the lectures. Lecture two describes the useful code of each principle.  All the codes have been written in Python using Jupyter Notebook.

Syllabus

  • Introduction to Data cleaning in python
  • Detecting Missing target and Noise Values (Univariate Outliers)
  • Handling Missing Noise Values (Univariate Outliers)
  • Multivariate Outliers
  • Anomalies in Textual data
  • Structuring Textual Documents

Similar courses on Data Cleaning

Specification:

  • Udemy
  • Online Course
  • Self-paced
  • Beginner
  • Less Than 24 Hours
  • Paid Course (Paid certificate)
  • English
  • Python
  • Jupyter Notebook
  • Basic Scripting in Python
  • Data Cleaning Python Programming

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Data Cleaning in Python

$19.99

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