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

**Missing data**, also called missing values, is information not present in a dataset. This can occur for various reasons, such as the data not being recorded or collected correctly. Identifying and addressing missing data when analyzing a dataset is crucial because it can impact the accuracy and reliability of the results. Failing to address missing data can lead to biased results. Using statistical models to fill in missing values can help improve the accuracy of the analysis.

Learn about missing data, its types, and examples in this lesson. 

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

Missing Data

Introduction

Types of missing data