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.
Start your data science journey right with this hands-on introduction to the discipline of data analysis. In this course you'll learn the 5 key stages of the data analysis process and apply them to real data sets using Python libraries NumPy, pandas, and Matplotlib.
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Data analysis is a rigorous discipline that can reveal illuminating insights, but a strong grasp of the fundamentals is essential to generating accurate, useful results. This hands-on course will teach you the industry-standard framework for analyzing data of any kind, and show you how to apply it using some of the most popular tools in data science.
Python.
Learn to use Anaconda to manage packages and environments for use with Python.
Learn to use this open-source web application to combine explanatory text, math equations, code and visualizations in one sharable document.
Learn about the keys steps of the data analysis process and investigate multiple datasets using Python and Pandas.
Perform the entire data analysis process on a dataset and learn to use NumPy and Pandas to wrangle, explore, analyze and visualize data.
Learn about how to carry out analysis outside Jupyter notebook using IPython or the command line interface.
In this project, you’ll choose one of Udacity’s curated datasets and investigate it using NumPy and pandas. You’ll complete the entire data analysis process, starting by posing a question and finishing by sharing your findings.
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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Mat is a former physicist, research neuroscientist, and data scientist. He did his PhD and Postdoctoral Fellowship at the University of California, Berkeley.
Juno is the curriculum lead for the School of Data Science. She has been sharing her passion for data and teaching, building several courses at Udacity. As a data scientist, she built recommendation engines, computer vision and NLP models, and tools to analyze user behavior.
The 5 stages of the data analysis process and work through them with Python libraries Pandas, NumPy, and Matplotlib.
On average, successful students take one 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 data in Python, ideally with the NumPy and/or pandas libraries.
The Intro to Data Analysis course is comprised of content and curriculum to support one project. We estimate that students can complete the program in one 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.