Lecture for the course "CS 217 – Probability and Statistics for Computer …
Lecture for the course "CS 217 – 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.
Access and explore large datasets from the National Health and Nutrition Examination …
Access and explore large datasets from the National Health and Nutrition Examination Survey (NHANES, 2003). Working with large datasets that emphasize exploration, finding patterns, and modeling is an essential first step in becoming fluent with data. This activity is a great place for students to start, since the dataset is straightforward and students can decide on the data they want to explore, including height, age, weight, and many other health-related attributes. Students begin by selecting and then investigating subsets of the dataset, for example, to find the cholesterol level of U.S. citizens. Then, working with their classmates or individually, students can try their own data science challenges, such as finding health trends in a subset of Americans by their household income, age, or marital status, etc.
Les vidéos 1080 ont pour objectif de vulgariser le savoir scientifique à …
Les vidéos 1080 ont pour objectif de vulgariser le savoir scientifique à destination des étudiants, des journalistes, des chercheurs de tous domaines et du grand public.
This is a path for those of you who want to complete …
This is a path for those of you who want to complete the Data Science undergraduate curriculum on your own time, for free, with courses from the best universities in the World. In our curriculum, we give preference to MOOC (Massive Open Online Course) style courses because these courses were created with our style of learning in mind. OSSU Data Science uses the report Curriculum Guidelines for Undergraduate Programs in Data Science (https://www.amstat.org/asa/files/pdfs/EDU-DataScienceGuidelines.pdf) as our guide for course recommendation.
It is possible to finish within about 2 years if you plan carefully and devote roughly 20 hours/week to your studies. Learners can use this spreadsheet (linked in resource) to estimate their end date. Make a copy and input your start date and expected hours per week in the Timeline sheet. As you work through courses you can enter your actual course completion dates in the Curriculum Data sheet and get updated completion estimates.
Python and R are heavily used in Data Science community and our courses teach you both. Remember, the important thing for each course is to internalize the core concepts and to be able to use them with whatever tool (programming language) that you wish.
The Data Science curriculum assumes the student has taken high school math and statistics.
Project Assignment for the course "CSC 217: Probability and statistics for Computer …
Project Assignment for the course "CSC 217: 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 course was developed and taught by Ben Marwick, Professor of Archaeology …
This course was developed and taught by Ben Marwick, Professor of Archaeology at University of Washington. It is a requirement for the UW Master of Science in Data Science, introduces students to the principles and tools for computational reproducibility in data science using R. Topics covered include acquiring, cleaning and manipulating data in a reproducible workflow using the tidyverse. Students will use literate programming tools, and explore best practices for organizing data analyses. Students will learn to write documents using R markdown, compile R markdown documents using knitr and related tools, and publish reproducible documents to various common formats. Students will learn strategies and tools for packaging research compendia, dependency management, and containerising projects to provide computational isolation.
This is the website for “R for Data Science”. This book will …
This is the website for “R for Data Science”. This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You’ll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You’ll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data.
Stebbins is a game about evolution. Students collect data as predators “eating” …
Stebbins is a game about evolution. Students collect data as predators “eating” colored circles on a colored background, being careful to avoid the poisonous ones. Data analysis reveals how the population changes color over time, and can be used to illuminate a common misconception that individuals change in response to predation. Stebbins is modeled on a non-digital game-like simulation of natural selection created by evolutionary biologist G. Ledyard Stebbins.
In Stella, students act as astronomers, studying stars in a “patch” of …
In Stella, students act as astronomers, studying stars in a “patch” of sky in our own galaxy. Using simulated data from spectroscopy and other real-world instrumentation, students learn to determine star positions, radial velocity, proper motion, and ultimately, degree of parallax. As students establish their expertise in each area, they earn “badges” that allow them greater and easier access to the data. The Teacher Guide includes background on stellar spectroscopy (the brightness of a star), photometry (the breakdown of light from a star), and astrometry (measuring the positions of stars).
Syllabus for the course "CSCI 381/780 - Data Analytics" delivered at Queens …
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 …
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 …
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 …
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 …
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 dynamic data science activity, students use data to build binary …
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 …
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.
Data Carpentry for Biologists is a set of teaching materials for teaching …
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
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