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Data Wrangling With Python

Name: Data Wrangling With Python
Price: 59 USD
Author: Valentine Mwangi

Gain insights into data wrangling using Python. Learn about cleaning, transforming, and organizing data with libraries like NumPy, pandas, and scikit-learn for DS and ML projects.

47 lessons

1 project

Created by Valentine Mwangi

What's included

47 lessons across 11 modules
9 quizzes with instant feedback
1 project in preconfigured environments
AI Tutor on every lesson

Join 3.1 million developers at

What You'll Learn

Understanding of data wrangling and its application in developing data solutions
A working knowledge of common data wrangling challenges and their solutions
Hands-on experience applying data wrangling techniques using Python to prepare data for further analysis
Familiarity with the core components of data wrangling and the ability to differentiate between data wrangling techniques to understand their suitability
Hands-on experience with datasets to practice the data wrangling techniques in a mini project at the end of the course

Learning Roadmap

47 Lessons1 Project9 Quizzes29 Challenges

About This Course

Get familiar with data wrangling, essential techniques, and Python libraries NumPy and pandas.

Course Introduction

Introduction to Data Wrangling

Look at transforming raw data into usable formats, improving quality, and applying core techniques.

Data Wrangling

Applications of Data Wrangling

How to Apply Data Wrangling

Quiz Yourself: Introduction to Data Wrangling

Reading Data

5 Lessons

Delve into reading and manipulating CSV, Excel, and local datasets using pandas.

Standardization

5 Lessons

Grasp the fundamentals of column fixing, data type conversion, string conversion, and coding challenges in standardization.

Syntax Errors and Irrelevant Data

5 Lessons

Take a closer look at cleaning data by correcting typos, removing spaces, handling irrelevant data, and coding challenges.

Duplicate and Missing Data

6 Lessons

See how it works to manage and resolve duplicate and missing data issues with Python.

Filtering and Sorting

6 Lessons

Master data filtering and sorting techniques in Python using pandas through practical examples.

Splitting, Combining, and Merging

5 Lessons

Step through splitting, joining, and merging DataFrames with hands-on coding challenges.

Handling Outliers

5 Lessons

Walk through outlier identification, advanced detection methods, and practical handling strategies in Python.

10.

Exporting Data

4 Lessons

Go hands-on with exporting DataFrames to CSV and Excel, and tackle coding challenges.

Humanitarian Aid Project

Project

11.

Conclusion

Grasp the fundamentals of data wrangling using pandas and NumPy for effective data manipulation.

Course Summary

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

About this course

WHY I WROTE THIS COURSE

Data wrangling is cleaning, transforming, and organizing data for further analysis. In this course, you will learn how to use Python to effectively wrangle and prepare data for use in data science and machine learning projects. Throughout the course, you will learn about the common challenges that arise when working with data and how to overcome them. You will use Python and several libraries commonly used in data wrangling, including NumPy and pandas. Then, you will learn how to use pandas to clean, transform, and aggregate data. Moreover, you will also use scikit-learn, a library for machine learning, to identify outliers in our data. By the end of the course, you will be able to use Python to effectively wrangle and prepare data for use in data science and machine learning projects. With these tools at your disposal, you can efficiently apply machine learning models and get realistic predictions after applying various wrangling techniques to the dataset.

ABOUT THE AUTHOR

Valentine Mwangi

Highly Experienced Educator and Data Scientist Empowering Thousands of Students Worldwide with Cutting-Edge Data Science Education and Interactive Workshops.

Learn more about Valentine

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