Title: Python for Data Science

Location: US

Company: The University of California, San Diego

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:

python

jupyter notebooks

pandas

numpy

matplotlib

git

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.

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

Learn to use powerful, open-source, Python tools, including Pandas, Git and Matplotlib, to manipulate, analyze, and visualize complex datasets.

Duration: 10 weeks

Skills:
- Business Science
- Data Science
- Git (Version Control System)
- Matplotlib (Python Package)
- Pandas (Python Package)
- Python (Programming Language)
- Python Tools For Visual Studio (Python Package)

Curriculum:
- 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