Syllabus for the course "CSCI 381/780 - Data Analytics" delivered at Queens College in Spring 2019 by Kumar Ramansenthil as part of the Tech-in-Residence Corps program.

Syllabus for the course "CIS 4400: Data Warehousing for Analytics" delivered at Baruch College in Fall 2018 by Royce Kok as part of the Tech-in-Residence Corps program.

Syllabus for the course "CSC 59970: Intro to Data Science" delivered at the City College of New York in Fall 2018 by Grant Long as part of the Tech-in-Residence Corps program.

Syllabus for the course "CSC 21700 - Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.

This case study is retrieved from the open book Open Data as Open Educational Resources. Case studies of emerging practice.

Course description:

Metrics and measurement are important strategic tools for understanding the world around us. To take advantage of the possibilities they offer, however, one needs the ability to gather, work with, and analyse datasets, both big and small. This is why metrics and measurement feature in the seminar course Technology and Evolving Forms of Publishing, and why data analysis was a project option for the Technology Project course in Simon Fraser University’s Master of Publishing Program.

The assignment:

“Data Analysis with Google Refine and APIs": Pick a dataset and an API of your choice (Twitter, VPL, Biblioshare, CrossRef, etc.) and combine them using Google Refine. Clean and manipulate your data for analysis. The complexity/messiness of your data will be taken into account”.

In this course you will learn the three laws of thermodynamics, explore concepts like entropy and enthalpy, and investigate the causes and effects of global warming from a thermodynamics perspective. We will also apply these concepts to learning about state-of-the-art energy conversion and storage technologies, for example heat pumps, hydrogen fuel cells, metal-air batteries, artificial photosynthesis, molten salt storage, and concentrated solar power. 
This course was offered as part of MITES Semester (formerly MOSTEC) in Summer 2022. MITES Semester is a 6-month online program for rising high-school seniors. The program offers students an opportunity to learn about diverse science and engineering fields, strengthen their academic STEM foundation, build 21st-century skills in networking, interviewing, collaboration and presentation delivery, prepare for college, and build a strong community of peers and mentors.
MITES Semester is part of MIT Introduction to Technology, Engineering, and Science (MITES), which provides transformative experiences that bolster confidence, create lifelong community, and build an exciting, challenging foundation in STEM for highly motivated 7th–12th grade students from diverse and underrepresented backgrounds.

We will discuss numerous research problems that are related to the internet. Sample topics include: routing algorithms such as BGP, communication protocols such as TCP, algorithms for intelligently selecting a resource in the face of uncertainty, bandwidth sensing tools, load balancing algorithms, streaming protocols, determining the structure of the internet, cost optimization, DNS-related problems, visualization, and large-scale data processing. The seminar is intended for students who are ready to work on challenging research problems. Each lecture will discuss:

methods used today
issues and problems
formulation of concrete problems
potential new lines of research

A modest amount of background information will be provided so that the importance and context of the problems can be understood. No previous study of the internet is required, but experience with algorithms and/or theoretical computer science at the graduate/research level is needed.

In this course, we will present the theory of Probabilistically Checkable Proofs (PCPs), and prove some fundamental consequences of it as well as more recent advances. More specifically, the first half of the course will be devoted to the (algebraic) proof of the basic PCP Theorem and basic relation to approximation problems. We will then move on to more advanced topics, such as hardness amplification, the long-code framework, the Unique-Games Conjecture and its implications, and the 2-to-2 Games Theorem.

In this dynamic data science activity, students use data to build binary trees for decision-making and prediction. Prediction trees are the first steps towards linear regression, which plays an important role in machine learning for future data scientists. Students begin by manually putting “training data” through an algorithm. They can then automate the process to test their ability to predict which alien creatures are sick and which are healthy. Students can “level up” to try more difficult scenarios.

The Turing Way project is open source, open collaboration, and community-driven. We involve and support a diverse community of contributors to make data science accessible, comprehensible and effective for everyone. Our goal is to provide all the information that researchers and data scientists in academia, industry and the public sector need to ensure that the projects they work on are easy to reproduce and reuse.

Robots today move far too conservatively, using control systems that attempt to maintain full control authority at all times. Humans and animals move much more aggressively by routinely executing motions which involve a loss of instantaneous control authority. Controlling nonlinear systems without complete control authority requires methods that can reason about and exploit the natural dynamics of our machines.
This course introduces nonlinear dynamics and control of underactuated mechanical systems, with an emphasis on computational methods. Topics include the nonlinear dynamics of robotic manipulators, applied optimal and robust control and motion planning. Discussions include examples from biology and applications to legged locomotion, compliant manipulation, underwater robots, and flying machines.

Data Carpentry for Biologists is a set of teaching materials for teaching biologists how to work with data through programming, database management and computing more generally.

This repository contains the complete teaching materials (excluding exams and answers to assignments) and website for a university style and self-guided course teaching computational data skills to biologists. The course is designed to work primarily as a flipped classroom, with students reading and viewing videos before coming to class and then spending the bulk of class time working on exercises with the teacher answering questions and demoing the concepts.

More information can be found on the project's GitHub page: https://github.com/datacarpentry/semester-biology/tree/v4.1.0