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Data Wrangling With Python

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

Beginner

47 Lessons

9h

Certificate of Completion

Gain insights into data wrangling using Python. Learn about cleaning, transforming, and organizing data with libraries like NumPy, pandas, and scikit-learn for data science and machine learning projects.
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This course includes

1 Project
65 Playgrounds
29 Challenges
9 Quizzes
Course Overview
What You'll Learn
Course Content
Apply Your Skills

Course Overview

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, trans...Show More
Data wrangling is cleaning, transforming, and organizing data for further analysis. In this course, you will learn how to use Py...Show More

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
Understanding of data wrangling and its application in developing data solutions

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Course Content

1.

About This Course

1 Lessons

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

2.

Introduction to Data Wrangling

4 Lessons

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

4.

Standardization

5 Lessons

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

5.

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.

6.

Duplicate and Missing Data

6 Lessons

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

7.

Filtering and Sorting

6 Lessons

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

8.

Splitting, Combining, and Merging

5 Lessons

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

9.

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.

12.

Conclusion

1 Lessons

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

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