Through eight lessons, students are introduced to many facets of dams, including their basic components, the common types (all designed to resist strong forces), their primary benefits (electricity generation, water supply, flood control, irrigation, recreation), and their importance (historically, currently and globally). Through an introduction to kinetic and potential energy, students come to understand how dams generate electricity. They learn about the structure, function and purpose of locks, which involves an introduction to Pascal's law, water pressure and gravity. Other lessons introduce students to common environmental impacts of dams and the engineering approaches to address them. They learn about the life cycle of salmon and the many engineered dam structures that aid in their river passage, as they think of their own methods and devices that could help fish migrate past dams. Students learn how dams and reservoirs become part of the Earth's hydrologic cycle, focusing on the role of evaporation. To conclude, students learn that dams do not last forever; they require ongoing maintenance, occasionally fail or succumb to "old age," or are no longer needed, and are sometimes removed. Through associated hands-on activities, students track their personal water usage; use clay and plastic containers to model and test four types of dam structures; use paper cups and water to learn about water pressure and Pascal's Law; explore kinetic energy by creating their own experimental waterwheel from two-liter plastic bottles; collect and count a stream's insects to gauge its health; play an animated PowerPoint game to quiz their understanding of the salmon life cycle and fish ladders; run a weeklong experiment to measure water evaporation and graph their data; and research eight dams to find out and compare their original purposes, current status, reservoir capacity and lifespan. Woven throughout the unit is a continuing hypothetical scenario in which students act as consulting engineers with a Splash Engineering firm, assisting Thirsty County in designing a dam for Birdseye River.

Short Description:
Innocent trends may foreshadow a grimmer future. You may wonder why the title refers to pleasures. If you have read Huxley's Brave New World, you may understand how pleasures can be motors of control and manipulation, which makes them dangerous.

Long Description:
Canceling” and calling out appear as the struggle against the opposite world views. I invite you to look at this cultural phenomenon from an economic perspective that outlines the social stakes of its practice. This book will encourage you to consider the unintended consequences of cancel culture and question its reliability as a tool of activism.

Word Count: 24104

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Are you ready to leave the sandbox and go for the real deal? Have you followed Data Analysis: Take It to the MAX() and Data Analysis: Visualization and Dashboard Design and are ready to carry out more robust data analysis?

In this project-based course you will engage in a real data analysis project that simulates the complexity and challenges of data analysts at work. Testing, data wrangling, Pivot Tables, sparklines? Now that you have mastered them you are ready to apply them all and carry out an independent data analysis.

For your project, you will pick one raw dataset out of several options, which you will turn into a dashboard. You will begin with a business question that is related to the dataset that you choose. The datasets will touch upon different business domains, such as revenue management, call-center management, investment, etc.

This course is for all of those struggling with data analysis. That crazy data collection from your boss? Megabytes of sensor data to analyze? Looking for a smart way visualize your data in order to make sense out of it? We’ve got you covered!

Using video lectures and hands-on exercises, we will teach you cutting-edge techniques and best practices that will boost your data analysis and visualization skills.

This course has been awarded with the Wharton-QS gold education award in the category Regional awards Europe.
We will take a deep dive into data analysis with spreadsheets: PivotTables, VLOOKUPS, Named ranges, what-if analyses, making great graphs – all those will be covered in the first weeks of the course. After that, we will investigate the quality of the spreadsheet model, and especially how to make sure your spreadsheet remains error-free and robust.

Finally, once we have mastered spreadsheets, we will demonstrate other ways to store and analyze data. We will also look into how Python, a programming language, can help us with analyzing and manipulating data in spreadsheets.

This course is created using Excel 2013 and Windows. Most assignments can be made using another spreadsheet program and operating system as well, but we cannot offer full support for all configurations.

Struggling with data at work? Wasting valuable time working in multiple spreadsheets to gain an overview of your business? Find it hard to gain sharp insights from piles of data on your desktop?

If you are looking to enhance your efficiency in the office and improve your performance by making sense of data faster and smarter, then this advanced data analysis course is for you.

If you have already sharpened your spreadsheet skills in Data Analysis: Take It to the MAX(), this course will help you dig deeper. You will learn advanced techniques for robust data analysis in a business environment. This course covers the main tasks required from data analysts today, including importing, summarizing, interpreting, analyzing and visualizing data. It aims to equip you with the tools that will enable you to be an independent data analyst. Most techniques will be taught in Excel with add-ons and free tools available online. We encourage you to use your own data in this course but if not available, the course team can provide.

These course materials are part of an online course of TU Delft. Do you want to experience an active exchange of information between academic staff and students? Then join the community of online learners and enroll in this MOOC. This course is part of the Data Analysis XSeries.

Short Description:
This open resources textbook contains 10 Units that describe and explain the main concepts in statistical analysis of psychological data. In addition to conceptual descriptions and explanations of the basic analyses for descriptive statistics, this textbook also explains how to conduct those analyses with common statistical software (Excel) and open-source free software (R).

Word Count: 27173

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Short Description:
Data analytics is a rapidly evolving field. In today's labour market, knowing how to acquire, process, and interpret large amounts of data to make optimal decisions is crucial for many professionals, especially those in business and engineering. This open textbook, "a new online course" if you will, focuses on three key concept areas: data acquisition, data processing, and decision-making models. In this course, students will be able to develop advanced knowledge and skills to acquire related data for operations of business or projects; apply quantitative literacy skills such as statistics and machine learning; and use predictive or prescriptive modeling to make timely, actionable, and meaningful decisions.

Long Description:
Data analytics is a rapidly evolving field. In today’s labour market, knowing how to acquire, process, and interpret large amounts of data to make optimal decisions is crucial for many professionals, especially those in business and engineering. This open textbook, “a new online course” if you will, focuses on three key concept areas: data acquisition, data processing, and decision-making models. In this course, students will be able to develop advanced knowledge and skills to acquire related data for operations of business or projects; apply quantitative literacy skills such as statistics and machine learning; and use predictive or prescriptive modeling to make timely, actionable, and meaningful decisions.

Word Count: 2038

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Beta Version

Word Count: 92165

ISBN: 979-8-88895-422-5

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Data Carpentry trains researchers in the core data skills for efficient, shareable, and reproducible research practices. We run accessible, inclusive training workshops; teach openly available, high-quality, domain-tailored lessons; and foster an active, inclusive, diverse instructor community that promotes and models reproducible research as a community norm.

6.263J / 16.37J focuses on the fundamentals of data communication networks. One goal is to give some insight into the rationale of why networks are structured the way they are today and to understand the issues facing the designers of next-generation data networks. Much of the course focuses on network algorithms and their performance. Students are expected to have a strong mathematical background and an understanding of probability theory. Topics discussed include: layered network architecture, Link Layer protocols, high-speed packet switching, queueing theory, Local Area Networks, and Wide Area Networking issues, including routing and flow control.

The MIT Libraries Data Management Group hosts a set of workshops during IAP and throughout the year to assist MIT faculty and researchers with data set control, maintenance, and sharing. This resource contains a selection of presentations from those workshops. Topics include an introduction to data management, details on data sharing and storage, data management using the DMPTool, file organization, version control, and an overview of the open data requirements of various funding sources.

A Claremont Graduate University EDUC 448 Fall 2021 Course Publication

Short Description:
This glossary is intended to support professionals who are seeking to understand Data Management and Governance in the context of K-12 and higher education. The definitions included in this ebook provide a fundamental understanding of common Data Management and Governance terms. This glossary was co-created by education professionals and graduate students enrolled in Claremont Graduate University’s EDUC 448: Data Management & Governance course taught by Dr. Gwen Garrison, PhD during the Fall 2021 semester.

Word Count: 2578

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Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Data mining is a rapidly growing field that is concerned with developing techniques to assist managers to make intelligent use of these repositories. A number of successful applications have been reported in areas such as credit rating, fraud detection, database marketing, customer relationship management, and stock market investments. The field of data mining has evolved from the disciplines of statistics and artificial intelligence.
This course will examine methods that have emerged from both fields and proven to be of value in recognizing patterns and making predictions from an applications perspective. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to-use software and cases.

This course is designed to introduce first-year Sloan MBA students to the fundamental techniques of using data. In particular, the course focuses on various ways of modeling, or thinking structurally about decision problems in order to make informed management decisions.

The Data Renaissance delves into the complexities of data's role in various industries and its broader impact on society. It highlights the challenges in investigating data practices, citing examples like TikTok, where algorithms and data handling are closely guarded secrets. The content, contributed by students under the guidance of an expert, covers a wide range of topics, including the ethical aspects of generative AI in education and the workplace, and case studies reflecting real-world experiences. This evolving text, intended to be updated with each class, serves as a dynamic resource for educators and students alike, offering insights and discussion guides for an in-depth understanding of the ever-changing landscape of data in our digital age.

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



Cool Extras




What are Data Talks?






A Picture Book Introduction to Data Science






Measures of Center and Spread Animated Movies






Stanford News Press Release

Word Count: 6664

Included H5P activities: 11

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Cleaning, reshaping, and transforming data for analysis and visualization, with R and the Tidyverse

Word Count: 3515

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Short Description:
Database Design - 2nd Edition covers database systems and database design concepts. New to this edition are SQL info, additional examples, key terms and review exercises at the end of each chapter.

Long Description:
This second edition of Database Design book covers the concepts used in database systems and the database design process. Topics include: The history of databases Characteristics and benefits of databases Data models Data modelling Classification of database management systems Integrity rules and constraints Functional dependencies Normalization Database development process

Word Count: 30650

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Database Design - 2nd Edition covers database systems and database design concepts. New to this edition are SQL info, additional examples, key terms and review exercises at the end of each chapter.