Choose your session:
Choose your session:
Python for Data Science
About this courseSkip About this course
In the information age, data is all around us. Within this data are answers to compelling questions across many societal domains (politics, business, science, etc.). But if you had access to a large dataset, would you be able to find the answers you seek?
This course, part of the Data Science MicroMasters program, will introduce you to a collection of powerful, open-source, tools needed to analyze data and to conduct data science. Specifically, you'll learn how to use:
- jupyter notebooks
- and many other tools.
You will learn these tools all within the context of solving compelling data science problems.
After completing this course, you'll be able to find answers within large datasets by using python tools to import data, explore it, analyze it, learn from it, visualize it, and ultimately generate easily sharable reports.
By learning these skills, you'll also become a member of a world-wide community which seeks to build data science tools, explore public datasets, and discuss evidence-based findings. Last but not least, this course will provide you with the foundation you need to succeed in later courses in the Data Science MicroMasters program.
At a glance
- Institution: UCSanDiegoX
- Subject: Data Analysis & Statistics
- Level: Advanced
Previous experience with any programming language (Java, C, Pascal, Fortran, C++, Python, PHP, etc.) is expected.This includes a high school, or undergraduate equivalent, to an introduction to computer science course.Learners should be comfortable with loops, if/else, and variables.
- Language: English
- Video Transcript: English
- Associated programs:
- MicroMasters® Program in Data Science
- Associated skills: Data Science, Matplotlib, Pandas (Python Package), Business Science, Python (Programming Language), Git (Version Control System), Python Tools For Visual Studio
What you'll learnSkip What you'll learn
- Basic process of data science
- Python and Jupyter notebooks
- An applied understanding of how to manipulate and analyze uncurated datasets
- Basic statistical analysis and machine learning methods
- How to effectively visualize results