Real-world projects from industry experts
With real-world projects and immersive content built in partnership with top-tier companies, you’ll master the tech skills companies want.
Learn the most efficient methods for gathering raw “dirty” data and cleaning it up for analysis. After finishing this course, you’ll be able to gather messy data from a variety of sources and “tidy” it up in no time using the powerful Python library, pandas.
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Knowing how to “wrangle data” (aka write code that cleans up “dirty” data and shapes it into useful formats) is a universally sought-after skill across data-driven companies. In this course you’ll learn how to leverage the power of Python to quickly gather, assess & clean messy data so it can be explored and analyzed easily.
Python & SQL.
Identify each step of the data wrangling process (gathering assessing, and cleaning) and wrangle a CSV file downloaded from Kaggle using fundamental gathering, assessing and cleaning code.
Gather data from multiple sources, including gathering files, programmatically downloading files, web-scraping data and accessing data from APIs.
Assess data visually and programmatically using pandas and identify data quality issues and categorize them using metrics: validity, accuracy, completeness, consistency and uniformity.
Identify each step of the data cleaning process (defining, coding and testing) and clean data using Python and Pandas.
Real-world data rarely comes clean. Using Python, you’ll gather data from a variety of sources, assess its quality and tidiness, then clean it. You’ll document your wrangling efforts in a Jupyter Notebook, plus showcase them through analyses and visualizations using Python and SQL.
With real-world projects and immersive content built in partnership with top-tier companies, you’ll master the tech skills companies want.
On demand help. Receive instant help with your learning directly in the classroom. Stay on track and get unstuck.
Validate your understanding of concepts learned by checking the output and quality of your code in real-time.
Tailor a learning plan that fits your busy life. Learn at your own pace and reach your personal goals on the schedule that works best for you.
We provide services customized for your needs at every step of your learning journey to ensure your success.
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Josh has been sharing his passion for data for nearly a decade at all levels of university, and as Lead Data Science Instructor at Galvanize. He's used data science for work ranging from cancer research to process automation.
Formerly a chemical engineer and data analyst, David created a personalized data science master's program using online resources. He has studied hundreds of online courses and is excited to bring the best to Udacity students.
Sam is the Product Lead for Udacity’s Data Analyst, Business Analyst, and Data Foundations programs. He’s worked as an analytics consultant on projects in several industries, and is passionate about helping others improve their data skills.
How to quickly gather, assess, and clean data from multiple sources using Python’s popular pandas library.
On average, successful students take 1 month to complete this program.
No. This Course accepts all applicants regardless of experience and specific background.
In order to succeed in this program, we recommend having experience working with SQL and with data in Python, ideally with the NumPy and/or pandas libraries.
The Data Wrangling course is comprised of content and curriculum to support one project. We estimate that students can complete the program in 1 month.
The project will be reviewed by the Udacity reviewer network and platform. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.
Access to this course runs for the length of time specified in the payment card above. If you do not graduate within that time period, you will continue learning with month to month payments. See the Terms of Use and FAQs for other policies regarding the terms of access to our programs.
Please see the Udacity Program Terms of Use and FAQs for policies on enrollment in our programs.
You will need access to the Internet, and a 64 bit computer. Additional software such as Python and its common data analysis libraries (e.g., Numpy and Pandas) will be required, but the program will guide students on how to download once the course has begun.