Lecture for the course "CSC 59970 – Intro to Data Science" delivered at the City College of New York in Spring 2019 by Grant Long as part of the Tech-in-Residence Corps program.

Lecture for the course "CSC 59970 – Intro to Data Science" delivered at the City College of New York in Spring 2019 by Grant Long as part of the Tech-in-Residence Corps program.

Lecture for the course "CSC 59970 – Intro to Data Science" delivered at the City College of New York in Spring 2019 by Grant Long as part of the Tech-in-Residence Corps program.

This course is a project-based introduction to manipulating and characterizing cells and biological molecules using microfabricated tools. It is designed for first year undergraduate students. In the first half of the term, students perform laboratory exercises designed to introduce (1) the design, manufacture, and use of microfluidic channels, (2) techniques for sorting and manipulating cells and biomolecules, and (3) making quantitative measurements using optical detection and fluorescent labeling. In the second half of the term, students work in small groups to design and test a microfluidic device to solve a real-world problem of their choosing. Includes exercises in written and oral communication and team building.

An array is an ordered collection of items, usually of the same type. In this lesson, students learn ways to access either a specific or random value from a list using its index. They then learn how to access the colorLEDs array that controls the behavior of the color LEDs on the Circuit Playground. Students will control the color and intensity of each LED, then use what they have learned to program light patterns to create a light show on their Circuit Playground.

This curriculum was designed for high school students with no prior coding experience who are interested in learning Python programming for data science. However, this course material would be useful for anyone interested in teaching or learning basic programming for data analysis.

The curriculum features short lessons to deliver course material in “bite sized” chunks, followed by practices to solidify the learners' understanding. Pre-recorded videos of lessons enable effective virtual learning and flipped classroom approaches.

The learning objectives of this curriculum are:

1. Write code in Python with correct syntax and following best practices.
2. Implement fundamental programming concepts when presented with a programmatic problem set.
3. Apply data analysis to real world data to answer scientific questions.
4. Create informative summary statistics and data visualizations in Python.
5. These skills provide a solid foundation for basic data analysis in Python. Participation in our program exposes students to the many ways coding and data science can be impactful across many disciplines.

Our curriculum design consists of 27 lessons broken up into 5 modules that cover Jupyter notebook setup, Python coding fundamentals, use of essential data science packages including pandas and numpy, basic statistical analyses, and plotting using seaborn and matplotlib. Each lesson consists of a lesson notebook, used for teaching the concept via live coding, and a practice notebook containing similar exercises for the student to complete on their own following the lesson. Each lesson builds on those before it, beginning with relevant content reminders from the previous lessons and ending with a concise summary of the skills presented within.

In this unplugged activity, students are introduced to the concept of algorithms. They will use the Computer Programming video from Brainpop to prompt a discussion around giving directions and the value of iteration. Students will then engage by creating their own algorithm to help get their “robot teacher” from point A to point B.

In this lesson, students will be presented with a project that they will decompose with their partners without having access to its code and without access to a computer. Students will work in teams to recreate the project shown in the following lesson.

Lecture for the course "CSC 59970 – Intro to Data Science" delivered at the City College of New York in Spring 2019 by Grant Long as part of the Tech-in-Residence Corps program.

Lecture for the course "CSC 59970 – Intro to Data Science" delivered at the City College of New York in Spring 2019 by Grant Long as part of the Tech-in-Residence Corps program.

Abigail Potter and Laurie Allen (Library of Congress) present 'Exploring Computational Description while Developing Responsible AI: An experiment to create MARC records from ebooks helps to define an AI planning and implementation framework' during the AI & Bibliographic Data session at the Fantasti... This item belongs to: movies/fantastic-futures-annual-international-conference-2023-ai-for-libraries-archives-and-museums-02.

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Lecture for the course "CSC 59970 – Intro to Data Science" delivered at the City College of New York in Spring 2019 by Grant Long as part of the Tech-in-Residence Corps program.

Lecture for the course "CSC 59970 – Intro to Data Science" delivered at the City College of New York in Spring 2019 by Grant Long as part of the Tech-in-Residence Corps program.

Lecture for the course "CSC 59970 – Intro to Data Science" delivered at the City College of New York in Spring 2019 by Grant Long as part of the Tech-in-Residence Corps program.

Lecture for the course "CSC 59970 – Intro to Data Science" delivered at the City College of New York in Spring 2019 by Grant Long as part of the Tech-in-Residence Corps program.

This workshop demonstrates how using R can advance open science practices in education. We focus on R and RStudio because it is an increasingly widely-used programming language and software environment for data analysis with a large supportive community. We present: a) general strategies for using R to analyze educational data and b) accessing and using data on the Open Science Framework (OSF) with R via the osfr package. This session is for those both new to R and those with R experience looking to learn more about strategies and workflows that can help to make it possible to analyze data in a more transparent, reliable, and trustworthy way. Access the workshop slides and supplemental information at https://osf.io/vtcak/​.

Resources:

1) Download R: https://www.r-project.org/​
2) Download RStudio (a tool that makes R easier to use): https://rstudio.com/products/rstudio/...​
3) R for Data Science (a free, digital book about how to do data science with R): https://r4ds.had.co.nz/​
4) Tidyverse R packages for data science: https://www.tidyverse.org/​
5) RMarkdown from RStudio (including info about R Notebooks): https://rmarkdown.rstudio.com/​
6) Data Science in Education Using R: https://datascienceineducation.com/​

In preparation for this chapter's final project, students will learn how to develop a prototype of a physical object that includes a Circuit Playground. Using a modelled project planning guide, students will learn how to wire a couple of simple circuits and to build prototypes that can communicate the intended design of a product, using cheap and easily found materials such as cardboard and duct tape.

This article describes many strategies for meeting the needs of visual and kinesthetic students, including deaf students.

The chapters in their current form have been made available to students who used Python in my Decision Science course in Fall 2019 (the course I had to prep for. Most students used R, but this helped those who choose Python). It has also been used as reference for students and project partners who use Python but have not had any training on using Python for data management.

This work is still useful for those learning Python as a data analysis platform as well as those who need to convert R code into Python due to deployment needs or to take advantage of Python resources in other domains. While it was not used as a textbook, the material was used by students in my decision models course and in senior capstone course for those who choose to use Python instead of R. While it seemed to help, the students had more difficulty than students who used R.