Data Cleaning With Polars

Data Cleaning With Polars, available at $19.99, has an average rating of 4.67, with 41 lectures, based on 3 reviews, and has 40 subscribers.

You will learn about Master the Fundamentals of Polars Clean and Manipulate Data Like a Pro Detect Outliers and Handle Missing Different Ways to Clean String Data This course is ideal for individuals who are Data Scientists and Engineers looking to Speed-Up Their Data Cleaning Process. or Data Analysts and other Data Cleaning Professionals. It is particularly useful for Data Scientists and Engineers looking to Speed-Up Their Data Cleaning Process. or Data Analysts and other Data Cleaning Professionals.

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Summary

Title: Data Cleaning With Polars

Price: $19.99

Average Rating: 4.67

Number of Lectures: 41

Number of Published Lectures: 41

Number of Curriculum Items: 41

Number of Published Curriculum Objects: 41

Original Price: $69.99

Quality Status: approved

Status: Live

What You Will Learn

Master the Fundamentals of Polars
Clean and Manipulate Data Like a Pro
Detect Outliers and Handle Missing
Different Ways to Clean String Data

Who Should Attend

Data Scientists and Engineers looking to Speed-Up Their Data Cleaning Process.
Data Analysts and other Data Cleaning Professionals.

Target Audiences

Data Scientists and Engineers looking to Speed-Up Their Data Cleaning Process.
Data Analysts and other Data Cleaning Professionals.

Description

80% of data science work is data cleaning. Building a machine learning model using unclean or messy data can lead to inaccuracies in your model performance. Therefore, it is important for you to know how to clean various real-world datasets. If you’re looking to enhance your skills in data manipulation and cleaning, this course will arm you with the essential skills needed to make that possible. This course is carefully crafted to provide you with a deeper understanding of data cleaning using Polars, a new blazingly fast DataFrame library for Python that enables you to handle large datasets with ease.

Five Different Datasets

All clean datasets are the same, but every unclean dataset is messy in its own way. This course includes five unique datasets and gives you a walkthrough of how to clean each one of them

Data Transformation

Data cleaning is about transforming the data from changing data types to removing unnecessary columns or rows. It’s also about dropping or replacing missing values as well as handling outliers. You will learn how to do all that in this course.

Ready-to-Use Skills

The lectures in this course are designed to help you conquer essential data cleaning tasks. You’ll gain job-ready skills and knowledge on how to clean any type of dataset and make it ready for model building.

Course Curriculum

Chapter 1: Introduction

Lecture 1: About the Instructor

Lecture 2: Why Learn Data Cleaning?

Lecture 3: Installing the Libraries

Chapter 2: Detecting Outliers in Your Dataset

Lecture 1: Outlier Detection with Boxplot

Lecture 2: Outlier Detection with Histogram

Lecture 3: Mean and Standard Deviation Method

Lecture 4: Inter Quartile Range

Lecture 5: The Z-score Method

Lecture 6: Percentile Calculation

Lecture 7: Flooring and Capping

Lecture 8: Binning

Chapter 3: Cleaning FIFA Data

Lecture 1: Cleaning Club Column

Lecture 2: Cleaning Contract Column part 1

Lecture 3: Cleaning Contract Column part 2

Lecture 4: Cleaning Height Column

Lecture 5: Cleaning Weight Column

Lecture 6: Creating a Glorius Cleaning Function

Lecture 7: Cleaning Weak Foot Column

Lecture 8: Cleaning the Hits Column

Chapter 4: Cleaning Meal Invoices Data

Lecture 1: Changing String to Category

Lecture 2: Changing Float to Integer

Lecture 3: Creating Columns from Date

Lecture 4: Checking the Presence of Substrings

Lecture 5: Reshaping Dataframes with Transpose

Lecture 6: Reshaping Dataframes with Melt

Lecture 7: Reshaping Dataframes with Pivot

Chapter 5: Cleaning Microplastics Data

Lecture 1: Selecting Specific Columns

Lecture 2: Selecting Specific Columns with Exclude

Lecture 3: Cleaning sampleid Column

Lecture 4: Creating a Count Column

Lecture 5: Counting Blank Particles

Lecture 6: Joining Dataframes

Lecture 7: Calculate Count of Correct Values

Lecture 8: Replace Negatives with Zero

Lecture 9: Assigning Correct Color

Chapter 6: Cleaning Diabetes Data

Lecture 1: Renaming a Column

Lecture 2: Handling Missing Values

Lecture 3: Cleaning Class Column

Lecture 4: Replacing Outliers

Lecture 5: Creating a Column with Condition

Chapter 7: Conclusion

Lecture 1: Congratulations

Instructors

Joram Mutenge
Udemy Instructor

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4 stars: 1 votes
5 stars: 2 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!

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