In this course you will get an introduction to the main tools and ideas in the data
scientist's toolbox. The course gives an overview of the data, questions, and tools
that data analysts and data scientists work with. There are two components to this
course. The first is a conceptual introduction to the ideas behind turning data
into actionable knowledge. The second is a practical introduction to the tools that
will be used in the program like version control, markdown, git, GitHub, R, and
RStudio.
Machine Learning is a first-class ticket to the most exciting careers in data analysis
today. As data sources proliferate along with the computing power to process them,
going straight to the data is one of the most straightforward ways to quickly gain
insights and make predictions. Machine learning brings together computer science
and statistics to harness that predictive power. It’s a must-have skill for all
aspiring data analysts and data scientists, or anyone else who wants to wrestle
all that raw data into refined trends and predictions.
Artificial Intelligence (AI) is a field that has a long history but is still constantly
and actively growing and changing. In this course, you’ll learn the basics of modern
AI as well as some of the representative applications of AI. Along the way, we also
hope to excite you about the numerous applications and huge possibilities in the
field of AI, which continues to expand human capability beyond our imagination.Artificial
Intelligence (AI) technology is increasingly prevalent in our everyday lives. It
has uses in a variety of industries from gaming, journalism/media, to finance, as
well as in the state-of-the-art research fields from robotics, medical diagnosis,
and quantum science. In this course you’ll learn the basics and applications of
AI, including: machine learning, probabilistic reasoning, robotics, computer vision,
and natural language processing.