Learn Data Cleaning with Python

Learn Data Cleaning with Python, available at Free, has an average rating of 4.2, with 7 lectures, based on 296 reviews, and has 11072 subscribers.

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You will learn about I can standardize a dataset by fixing inconsistent column names. I can perform data type conversion to fix inaccurate data. I can find and fix syntax errors in a dataset. I can find and fix typos in a dataset. I can deal with irrelevant data in a dataset. I can remove any duplicate records in a dataset. I can find and deal with missing data in a dataset. I can find and deal with outliers in a dataset. This course is ideal for individuals who are This course is designed for professionals with an interest in getting hands-on experience with the respective data science techniques and tools. It is particularly useful for This course is designed for professionals with an interest in getting hands-on experience with the respective data science techniques and tools.

Enroll now: Learn Data Cleaning with Python

Summary

Title: Learn Data Cleaning with Python

Price: Free

Average Rating: 4.2

Number of Lectures: 7

Number of Published Lectures: 7

Number of Curriculum Items: 7

Number of Published Curriculum Objects: 7

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

I can standardize a dataset by fixing inconsistent column names.

I can perform data type conversion to fix inaccurate data.

I can find and fix syntax errors in a dataset.

I can find and fix typos in a dataset.

I can deal with irrelevant data in a dataset.

I can remove any duplicate records in a dataset.

I can find and deal with missing data in a dataset.

I can find and deal with outliers in a dataset.

Who Should Attend

This course is designed for professionals with an interest in getting hands-on experience with the respective data science techniques and tools.

Target Audiences

This course is designed for professionals with an interest in getting hands-on experience with the respective data science techniques and tools.

By the end of this course, you will be able to:

I can standardize a dataset by fixing inconsistent column names.

I can perform data type conversion to fix inaccurate data.

I can find and fix syntax errors in a dataset.

I can find and fix typos in a dataset.

I can deal with irrelevant data in a dataset.

I can remove any duplicate records in a dataset.

I can find and deal with missing data in a dataset.

I can find and deal with outliers in a dataset.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Standardisation

Lecture 1: Standardisation

Chapter 3: Syntax Errors

Lecture 1: Syntax Errors

Chapter 4: Irrelevant Data

Lecture 1: Irrelevant Data

Chapter 5: Duplicates

Lecture 1: Duplicates

Chapter 6: Missing Data

Lecture 1: Missing Data

Chapter 7: Outliers

Lecture 1: Outliers

Instructors

Valentine Mwangi
Data Science Curriculum Designer

Rating Distribution

1 stars: 9 votes

2 stars: 17 votes

3 stars: 69 votes

4 stars: 94 votes

5 stars: 107 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!