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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

To improve the accuracy and reliability of the results

Why is it important to address missing data in a dataset?

Why is it important to address missing data in a dataset?

What can a lack of validation checks lead to?


What can a lack of validation checks lead to?

By assigning a unique number to each record

By notifying the user of a duplicate entry and prompting them to correct it

By ensuring that data being entered is not a copy of existing data

By creating a new entry for each duplicate record

**(Select all that apply.)** How can a validation check help prevent data duplication?

(Select all that apply.) How can a validation check help prevent data duplication?

To identify and remove duplicate records from the dataset

To combine duplicate records into a single record

To establish and enforce policies, procedures, and controls for managing and protecting the data

To reconcile inconsistencies or errors in duplicate records

What is the goal of data governance?

The probability of a missing value is related to other variables in the dataset but not the missing value itself.

The probability of a missing value is the same for all observations in the dataset.

The probability of a missing value is related to the value itself.

The probability of a missing value is not related to the missing value or any other variables in the dataset.

What is the definition of missing completely at random (MCAR)?

What is the definition of missing completely at random (MCAR)?

Ignoring the missing values will likely lead to biased results in the analysis. What type of missing data scenario is this?

Ignoring the missing values will likely lead to biased results in the analysis. What type of missing data scenario is this?

Test yourself on the knowledge you learned in the "Duplicate and Missing Data" 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: Duplicate and Missing Data