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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Data Wrangling

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.

Data Wrangling With Python

Data wrangling is the process of finding and extracting meaningful patterns from large datasets using algorithms and techniques.

Data wrangling is the process of representing data using visual elements to communicate insights to users.

Data wrangling is the process of transforming data from its raw format into a meaningful format for further analysis.

When do you perform data wrangling?

To aggregate data from multiple data sources

To save time for stakeholders that will use the output data

To make it easy to extract meaningful insights from large datasets

**(Select all that apply.)** Why should you perform data wrangling?

(Select all that apply.) Why should you perform data wrangling?

Which Python library is specifically used for handling numbers when wrangling data?

Which Python library is specifically used for handling numbers when wrangling data?

Splitting, merging, and concatenating data from multiple sources

Creating a machine learning model to predict a target variable

Which option is *not* a data wrangling technique?

Which option is not a data wrangling technique?

Test yourself on the knowledge you learned about Introduction to Data Wrangling.

About This Course

Introduction to Data Wrangling

Reading Data

Standardization

Syntax Errors and Irrelevant Data

Duplicate and Missing Data

Filtering and Sorting

Splitting, Combining, and Merging

Handling Outliers

Exporting Data

Humanitarian Aid Project

Conclusion

Quiz Yourself: Introduction to Data Wrangling