Data and the insights gained from analyzing it are transforming our world.
This course offers an introduction to the field of data science, as well as the basic tools and algorithms used by data scientists.
Data science is at the intersection of several disciplines, so you will also learn the basics of programming with Python; underlying mathematical and statistical principles; data collection, cleaning, and manipulation; and machine learning.
The course will introduce you to the most popular open source data science libraries and tools that you can apply to your own work.
You will also learn where to find free, publicly available datasets that you can use for learning as well as for gaining real-world insights.
- Describe what is Data Science
- Learn the basics of Python with emphasis on solving Data Science problems
- Learn basic concepts in linear algebra, statistics, and probability and understand how they are applied in Data Science
- Use the IPython Interactive shell as a development environment
- Learn basic NumPy features
- Perform data analysis with tools in the pandas library
- Load, clean, transform, merge, and reshape data
- Use matplotlib to create scatter plots and visualizations
- Slice, dice, and summarize datasets with pandas’ groupby facility
- Measure data by points in time
- Use hands-on examples to solve problems in web analytics, social sciences, finance, and economics
- Work with real-world data sets
Who Should Take this Class?
- Professionals who work indirectly with data science, data analytics or business intelligence and would like to widen their knowledge of the subject
- Individuals considering a career change to the growing field of data science
- Anyone interested in applying data science tools to gain meaningful insights in their field of work
- Students with Python programming skills who would like to apply their knowledge of Python to data science problems
- Anyone interested in learning the fundamentals of one of the most important fields today: Data science
The goal of this class is to democratize access to the field of data science, so there are no prerequisites; however, a background in mathematics, statistics, or computer science can be helpful.