Track
Data Manipulation in Python
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Data Manipulation in Python
Master Data Manipulation with Python's Most Powerful Libraries
Unlock the full potential of your data with Python's essential data manipulation libraries: pandas and NumPy. In this Track, you'll learn how to efficiently clean, reshape, and analyze real-world datasets using the most popular tools in the Python data ecosystem. By the end of the Track, you'll have the skills to tackle any data manipulation challenge that comes your way.Become a pandas Power User
Dive deep into the pandas library and learn how to:- Import and clean data from various sources like CSV, Excel, and SQL databases
- Calculate statistics and create informative visualizations
- Reshape data from wide to long format for efficient analysis
- Combine multiple datasets using advanced joining and merging techniques
- Work with hierarchical data using multi-index DataFrames
Streamline Your Workflow with NumPy
Discover the power of NumPy, the foundation of Python's data science stack. You'll learn how to create, sort, filter, and update NumPy arrays while efficiently performing complex mathematical operations on large datasets. Additionally, you'll explore how to integrate NumPy with pandas to create a seamless data manipulation workflow and optimize your code for improved speed and performance.Hands-On Learning with Real Datasets
Practice your new skills on real-world data, including:- New York City's tree census
- Customer purchase data
- Stock market prices
- Online review datasets
Advance Your Data Science Journey
Whether you're an aspiring data scientist or an experienced analyst looking to upgrade your skills, mastering data manipulation is essential. The techniques you'll learn in this Track serve as the foundation for machine learning, data visualization, and statistical analysis. By honing your data manipulation skills, you'll be prepared to tackle advanced data science concepts and real-world challenges.Start Manipulating Data with Confidence
Take the first step towards becoming a data manipulation expert. With a combination of interactive exercises, real-world datasets, and hands-on projects, this Track provides a comprehensive learning experience. By the end of the Track, you'll have the confidence and skills to wrangle any dataset and uncover valuable insights. Start your journey to mastering data manipulation today!Prerequisites
There are no prerequisites for this trackCourse
Learn how to import and clean data, calculate statistics, and create visualizations with pandas.
Project
Dive into sleep data and gain insights about factors that impact sleep quality
Course
Reshape DataFrames from a wide to long format, stack and unstack rows and columns, and wrangle multi-index DataFrames.
Course
Learn to combine data from multiple tables by joining data together using pandas.
Course
Master your skills in NumPy by learning how to create, sort, filter, and update arrays using NYC’s tree census.
Skill Assessment
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Included withPremium or Teams
Enroll NowFAQs
Is this Track suitable for beginners?
Yes, this Track is suitable for beginners to learn the basics of manipulating data with Python. While the Track does not require prior knowledge of Python, you can get up to speed quickly with the introductions and tutorials included in the Track courses.
What is the programming language of this Track?
This Track is taught using Python. You will learn how to use Python libraries such as pandas and NumPy to manipulate data in DataFrames to perform data analysis.
Which jobs will benefit from this Track?
This Track will help those who are working in positions such as data analysts, data engineers, software developers, and machine learning engineers who need to work with and analyze data.
How will this Track prepare me for my career?
This Track will teach you many skills related to manipulating and analyzing data with Python, such as extracting, filtering, transforming, joining, merging, and creating visualizations. By the end of the Track, you will be able to use pandas and NumPy to quickly and accurately analyze data.
How long does it take to complete this Track?
This track is self-paced so users can spend as long or as little time as they like working through exercises and courses. However, the Track usually takes 16 hours to complete, as it consists of several courses.
What's the difference between a skill track and a career track?
A skill track is designed to teach users how to use a specific programming language, library, or technology to develop skills. A career track is designed to teach users specific job skills and prepare them for job interviews and roles in a specific field.
What datasets will be used?
The datasets used will be mostly created from real-world examples, and they will always be provided as part of the Track.
What topics will be covered in the Track?
The Track courses will cover topics such as extracting, filtering, transforming data in DataFrames; combining, merging, and creating visualizations; and working with NumPy arrays and using New York City's tree census data to create, sort, filter, and update arrays.
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