Students will learn how to identify and repair potential app-level security and performance problems. Students will learn how to exercise best practices in planning, effort estimation, and coordination of the efforts of small software teams, using appropriate tools to support those practices Students will learn how to construct unit- and module-level tests and measure their coverage Students will learn how to apply the key ideas of learning a new language in order to construct programs in Ruby Students will learn how to apply the key ideas of learning a new framework to construct and deploy simple Rails applications Students will learn how to apply behavior-driven development (BDD) to elicit customer needs and express them as user stories that will drive development Student Learning Outcomes: Students will learn how to apply BDD & TDD to identify the main parts of a legacy code base, measure code quality, and refactor code to improve its quality Students will be able to use visualizations to understand univariate data and to identify associations or causal relationships in bivariate data. Students will be able to use good practices in Python programming. Students will be able to understand the syntactic structure of Python code. Students will be able to understand basic probabilistic simulations. Student Learning Outcomes: Students will be able to perform basic computations in Python, including working with tabular data.

C6 is a rapid introduction to Python programming, visualization, and data analysis, which will prepare students for success in Data 8. For some students, particularly those with little prior exposure to computing, developing these skills benefits from further time and practice. Data 8 builds students’ computing skills in the first month of the semester, and students rely on these skills as the course progresses.

Data 8) is an increasingly popular class for entering students at Berkeley.

Students learn computer science concepts and immediately apply them to plot functions, visualize data, and simulate random events.įoundations of Data Science (CS/Info/Stat C8, a.k.a. This will help prepare students for computational and quantitative courses other than Data 8.Ĭ6 takes advantage of the complementarity of computing and quantitative reasoning to enliven abstract ideas and build students’ confidence in their ability to solve real problems with quantitative tools. These include certain topics in: principles of data visualization simulation of random processes and understanding numerical functions through their graphs. Course Objectives: C6 also includes quantitative reasoning concepts that aren’t covered in Data 8.

Usc cs mobile development professors