This Library Carpentry lesson introduces archivists to working with data. At the …
This Library Carpentry lesson introduces archivists to working with data. At the conclusion of the lesson you will: be able to explain terms, phrases, and concepts in code or software development; identify and use best practice in data structures; use regular expressions in searches.
Course Description: This course covers the management of healthcare data, selected data …
Course Description: This course covers the management of healthcare data, selected data management topics with current importance in the field, and how to use statistical methods in data analysis.
Learning Objectives: Upon completion of the course students should be able to: 1. Describe analytics and decision support. 2. Validate the reliability and accuracy of secondary data sources. 3. Identify data sources requiring data management and analytics skills. 4. Demonstrate the statistical methods used in data analysis.
Databases are useful for both storing and using data effectively. Using a …
Databases are useful for both storing and using data effectively. Using a relational database serves several purposes. It keeps your data separate from your analysis. This means there’s no risk of accidentally changing data when you analyze it. If we get new data we can rerun a query to find all the data that meets certain criteria. It’s fast, even for large amounts of data. It improves quality control of data entry (type constraints and use of forms in Access, Filemaker, etc.) The concepts of relational database querying are core to understanding how to do similar things using programming languages such as R or Python. This lesson will teach you what relational databases are, how you can load data into them and how you can query databases to extract just the information that you need.
This is an alpha lesson to teach Data Management with SQL for …
This is an alpha lesson to teach Data Management with SQL for Social Scientists, We welcome and criticism, or error; and will take your feedback into account to improve both the presentation and the content. Databases are useful for both storing and using data effectively. Using a relational database serves several purposes. It keeps your data separate from your analysis. This means there’s no risk of accidentally changing data when you analyze it. If we get new data we can rerun a query to find all the data that meets certain criteria. It’s fast, even for large amounts of data. It improves quality control of data entry (type constraints and use of forms in Access, Filemaker, etc.) The concepts of relational database querying are core to understanding how to do similar things using programming languages such as R or Python. This lesson will teach you what relational databases are, how you can load data into them and how you can query databases to extract just the information that you need.
Good data organization is the foundation of any research project. Most researchers …
Good data organization is the foundation of any research project. Most researchers have data in spreadsheets, so it’s the place that many research projects start. We organize data in spreadsheets in the ways that we as humans want to work with the data, but computers require that data be organized in particular ways. In order to use tools that make computation more efficient, such as programming languages like R or Python, we need to structure our data the way that computers need the data. Since this is where most research projects start, this is where we want to start too! In this lesson, you will learn: Good data entry practices - formatting data tables in spreadsheets How to avoid common formatting mistakes Approaches for handling dates in spreadsheets Basic quality control and data manipulation in spreadsheets Exporting data from spreadsheets In this lesson, however, you will not learn about data analysis with spreadsheets. Much of your time as a researcher will be spent in the initial ‘data wrangling’ stage, where you need to organize the data to perform a proper analysis later. It’s not the most fun, but it is necessary. In this lesson you will learn how to think about data organization and some practices for more effective data wrangling. With this approach you can better format current data and plan new data collection so less data wrangling is needed.
Lesson on spreadsheets for social scientists. Good data organization is the foundation …
Lesson on spreadsheets for social scientists. Good data organization is the foundation of any research project. Most researchers have data in spreadsheets, so it’s the place that many research projects start. Typically we organize data in spreadsheets in ways that we as humans want to work with the data. However computers require data to be organized in particular ways. In order to use tools that make computation more efficient, such as programming languages like R or Python, we need to structure our data the way that computers need the data. Since this is where most research projects start, this is where we want to start too! In this lesson, you will learn: Good data entry practices - formatting data tables in spreadsheets How to avoid common formatting mistakes Approaches for handling dates in spreadsheets Basic quality control and data manipulation in spreadsheets Exporting data from spreadsheets In this lesson, however, you will not learn about data analysis with spreadsheets. Much of your time as a researcher will be spent in the initial ‘data wrangling’ stage, where you need to organize the data to perform a proper analysis later. It’s not the most fun, but it is necessary. In this lesson you will learn how to think about data organization and some practices for more effective data wrangling. With this approach you can better format current data and plan new data collection so less data wrangling is needed.
This page shares five units of youcubed lessons for grades 6-10 that …
This page shares five units of youcubed lessons for grades 6-10 that introduce students (and teachers) to data science. The units start with an introduction to the concept of data and move to lessons that invite students to explore their own data sets. These lessons teach important content through a pattern-seeking, exploratory approach, and are designed to engage students actively.
Data Science LessonsThis page shares five units of youcubed lessons for grades 6-10 that introduce students (and teachers) to data science. The units start with an introduction to the concept of data and move to lessons that invite students to explore their own data sets. These lessons teach important content through a pattern-seeking, exploratory approach, and are designed to engage students actively. The culminating unit is a citizen science project that gives students an opportunity to conduct a data inquiry. The lessons accompany a new online course for teachers, where some of the lessons are featured, along with other lesson ideas. These lessons are offered with ideas for in-person or online teaching, and can be taught at any time of year.
LessonsTeacher Online Course: 21st Century Teaching and LearningUnit 1: Data Is EverywhereUnit 2: Working With Data Analysis ToolsUnit 3: Measures of Center & SpreadUnit 4: Understanding VariabilityUnit 5: A Community Data Collection Project
ResourcesHigh School Data Science CourseCODAPWhat's Going On In This Graph?Data Science Initiative VideoThe Data Science K-12 MovementData Talks
Data talks are short 5-10 minute classroom discussions to help students develop …
Data talks are short 5-10 minute classroom discussions to help students develop data literacy. This pedagogical strategy is similar in structure to a number talk, but instead of numbers students are shown a data visual and asked what interests them.
Data Carpentry lesson to learn how to use command-line tools to perform …
Data Carpentry lesson to learn how to use command-line tools to perform quality control, align reads to a reference genome, and identify and visualize between-sample variation. A lot of genomics analysis is done using command-line tools for three reasons: 1) you will often be working with a large number of files, and working through the command-line rather than through a graphical user interface (GUI) allows you to automate repetitive tasks, 2) you will often need more compute power than is available on your personal computer, and connecting to and interacting with remote computers requires a command-line interface, and 3) you will often need to customize your analyses, and command-line tools often enable more customization than the corresponding GUI tools (if in fact a GUI tool even exists). In a previous lesson, you learned how to use the bash shell to interact with your computer through a command line interface. In this lesson, you will be applying this new knowledge to carry out a common genomics workflow - identifying variants among sequencing samples taken from multiple individuals within a population. We will be starting with a set of sequenced reads (.fastq files), performing some quality control steps, aligning those reads to a reference genome, and ending by identifying and visualizing variations among these samples. As you progress through this lesson, keep in mind that, even if you aren’t going to be doing this same workflow in your research, you will be learning some very important lessons about using command-line bioinformatic tools. What you learn here will enable you to use a variety of bioinformatic tools with confidence and greatly enhance your research efficiency and productivity.
Software Carpentry lesson that teaches how to use databases and SQL In …
Software Carpentry lesson that teaches how to use databases and SQL In the late 1920s and early 1930s, William Dyer, Frank Pabodie, and Valentina Roerich led expeditions to the Pole of Inaccessibility in the South Pacific, and then onward to Antarctica. Two years ago, their expeditions were found in a storage locker at Miskatonic University. We have scanned and OCR the data they contain, and we now want to store that information in a way that will make search and analysis easy. Three common options for storage are text files, spreadsheets, and databases. Text files are easiest to create, and work well with version control, but then we would have to build search and analysis tools ourselves. Spreadsheets are good for doing simple analyses, but they don’t handle large or complex data sets well. Databases, however, include powerful tools for search and analysis, and can handle large, complex data sets. These lessons will show how to use a database to explore the expeditions’ data.
In this lab, students are introduced to the difference between relative and …
In this lab, students are introduced to the difference between relative and absolute dating, using the students themselves as the material to be ordered. Initially, the students are asked to develop physical clues to put themselves in order from youngest to oldest (exposing the inferences we make unconsciously about people's ages), and this will be refined/modified using a list of current events from an appropriate historical period that more and more of the students will remember, depending on their age (among other variables). Absolute age is introduced by having the students order themselves by birth decade, year, month, and day, and comparing the absolute age order to the order worked out in the relative-dating exercise, with a discussion of dating precision and accuracy.
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In this lesson, students learn that sound is energy and has the …
In this lesson, students learn that sound is energy and has the ability to do work. Students discover that sound is produced by a vibration and they observe soundwaves and how they travel through mediums. They understand that sound can be absorbed, reflected or transmitted. Through associated activities, videos and a PowerPoint presentation led by the teacher, students further their exploration of sound through discussions in order to build background knowledge.
Spreadsheets Across the Curriculum/Geology of National Parks module. Students calculate the haze …
Spreadsheets Across the Curriculum/Geology of National Parks module. Students calculate the haze index and standard visual range from concentrations of particulate matter.
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Students are given 4 hypothetical stratigraphic columns (each roughly 30 m thick) …
Students are given 4 hypothetical stratigraphic columns (each roughly 30 m thick) of deltaic deposits, 3 base maps with section locations, and a map scale. Students subdivide the stratigraphic units into subfacies and interpret subenvironments (delta plain, delta front, prodelta, marine) and describe/list features used to make these interpretations. Using depositional interpretations, 3 bentonite marker beds, and paleocurrent information, students draw 3 successive paleogeographic maps of the region showing delta migration through time.
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A demonstration (with full class participation) to illustrate radioactive decay by flipping …
A demonstration (with full class participation) to illustrate radioactive decay by flipping coins. Shows students visually the concepts of exponential decay, half-life and randomness. Works best in large classes -- the more people, the better.
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This hands-on lab activity is designed to teach students about how density …
This hands-on lab activity is designed to teach students about how density differences, due to salinity, drive the flow of currents in the ocean. It also helps develop skills in performing and designing simple laboratory measurements; data entry, calculations and graph plotting in a spreadsheet; and comparing experimental data with a theoretical equation. Key words: ocean circulation; density driven flows; salinity; ocean density; thermohaline circulation.
This module addresses the problem of how to determine the density of …
This module addresses the problem of how to determine the density of the earth and has students do some field experiments to get the data they need to answer the problem.
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