Learn Data Wrangling with Python

Learn Data Wrangling with Python, available at $19.99, has an average rating of 4.3, with 18 lectures, based on 61 reviews, and has 6696 subscribers.

You will learn about To load a local dataset from CSV and Excel files. To import a dataset from CSV and Excel files via a URL. To determine the size of a dataset. To explore the first and last records of a dataset. To explore the datatypes of the features of a dataset. To check for missing data in a dataset. To deal with missing data in a dataset. To filter for records with certain values from a dataset. To filter records with multiple filters from a dataset. To filter for records from a dataset through the use of conditions. To perform sorting in ascending and descending order. To split a column in a dataset. To merge data frames to form a dataset. To concatenate two columns to one column in a dataset. To export a dataset in CSV and Excel formats. 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 Wrangling with Python

Summary

Title: Learn Data Wrangling with Python

Price: $19.99

Average Rating: 4.3

Number of Lectures: 18

Number of Published Lectures: 18

Number of Curriculum Items: 18

Number of Published Curriculum Objects: 18

Original Price: $29.99

Quality Status: approved

Status: Live

What You Will Learn

To load a local dataset from CSV and Excel files.

To import a dataset from CSV and Excel files via a URL.

To determine the size of a dataset.

To explore the first and last records of a dataset.

To explore the datatypes of the features of a dataset.

To check for missing data in a dataset.

To deal with missing data in a dataset.

To filter for records with certain values from a dataset.

To filter records with multiple filters from a dataset.

To filter for records from a dataset through the use of conditions.

To perform sorting in ascending and descending order.

To split a column in a dataset.

To merge data frames to form a dataset.

To concatenate two columns to one column in a dataset.

To export a dataset in CSV and Excel formats.

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:

Load a local dataset from CSV and Excel files.

Import a dataset from CSV and Excel files via a URL.

Determine the size of a dataset.

Explore the first and last records of a dataset.

Explore the datatypes of the features of a dataset.

Check for missing data in a dataset.

Deal with missing data in a dataset.

Filter for records with certain values from a dataset.

Filter records with multiple filters from a dataset.

Filter for records from a dataset through the use of conditions.

Perform sorting in ascending and descending order.

Split a column in a dataset.

Merge data frames to form a dataset.

Concatenate two columns to one column in a dataset.

Export a dataset in CSV and Excel formats.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Learning Outcomes

Lecture 1: Learning Outcomes

Chapter 3: Overview of Data Wrangling

Lecture 1: Overview of Data Wrangling

Chapter 4: Notebook Introduction

Lecture 1: Notebook Introduction

Chapter 5: Prerequisites

Lecture 1: Prerequisites

Chapter 6: Reading Data

Lecture 1: Reading Data

Chapter 7: Data Exploration

Lecture 1: Data Exploration

Chapter 8: Standardisation

Lecture 1: Standardisation

Chapter 9: Syntax Errors

Lecture 1: Syntax Errors

Chapter 10: Irrelevant Data

Lecture 1: Irrelevant Data

Chapter 11: Duplicates

Lecture 1: Duplicates

Chapter 12: Missing Data

Lecture 1: Missing Data

Chapter 13: Filtering

Lecture 1: Filtering

Chapter 14: Sorting

Lecture 1: Sorting

Chapter 15: Splitting, Merging and Concatenation

Lecture 1: Splitting, Merging and Concatenation

Chapter 16: Outliers

Lecture 1: Outliers

Chapter 17: Exporting Data

Lecture 1: Exporting Data

Chapter 18: Next Steps

Lecture 1: Next Steps

Instructors

Valentine Mwangi
Data Science Curriculum Designer

Rating Distribution

1 stars: 3 votes

2 stars: 1 votes

3 stars: 11 votes

4 stars: 22 votes

5 stars: 24 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!