Establish a foundation of the concepts and technologies behind modern Data Science and work on real data sets and problems to put your learning into practice. This is a modern and comprehensive introduction to data science and machine learning in Python. After completing the course, students will have a work-applicable understanding of the data science process and how to use the methodologies and tools to solve real world problems.

None

Recommended

• Working experience with basic Python programming, professionally or as a hobby
• Experience or a strong interest in solving complex problems through data analysis and data science
• Familiarity with linear Algebra
• Complete prep-work before day-1 of class

Data Prep w/ Python & DS Libraries

• Learn the many ways of preparing and cleaning data with Python and scientific libraries, such as Pandas and Numpy
• Technologies: Python, Pandas, and Numpy

Data Analysis & Scientific Visualization

• Analyze, explore, and visualize large data sets in the same way that real Data Scientists do on the job.
• Technologies: Matplotlib and Seaborn

Foundations of Data Modeling & Machine Learning

• Build statistical models — regression and classification — that generate usable information from raw data. Apply decision trees and random forests to your data modeling via SciKit-Learn, a machine learning library utilizing Python.
• Technologies: SciKit-Learn

Data Visualization & Project Week – Build, Deliver, and Present

• Put your learning from the last 3 weeks to the test and present/visualize a large-scale Data Science project.
• Technologies: Bokeh