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 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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Content
2.
Introduction to Data Wrangling
4 Lessons
Look at transforming raw data into usable formats, improving quality, and applying core techniques.
3.
Reading Data
5 Lessons
Delve into reading and manipulating CSV, Excel, and local datasets using pandas.
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.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
Course Author:
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