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

A record that is similar to the others in the dataset

A record that stands out from the others due to its significant deviation from the rest of the data

A record that is an average of the rest of the data

A farmer record with an average sales column value compared with other farmers in the market

A patient record with a much shorter or longer hospital stay than other patients

A student record with a much lower or higher grade than the rest of the class

A house record with a much lower or higher price than the rest of the houses in the dataset

Which is not an example of an outlier record in a dataset?

Which is not an example of an outlier record in a dataset?

Outliers can impact statistical analyses, including measures of central tendency and variability.

Outliers can affect the fit of statistical models.

Outliers can result from data collection or entry errors and may not represent the population.

Why is it essential to deal with outliers in a dataset?

Why is it essential to deal with outliers in a dataset?

What is a method of dealing with outliers?

What is a method of dealing with outliers?

**(Select all that apply.)** Which methods of dealing with outliers can you use to maintain the size of the dataset?

(Select all that apply.) Which methods of dealing with outliers can you use to maintain the size of the dataset?

Which outlier detection method works best when the data is highly skewed?

Which outlier detection method works best when the data is highly skewed?

Test yourself on the knowledge you learned in the "Handling Outliers" chapter.

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: Handling Outliers