David McCandless turns complex data sets (like worldwide military spending, media buzz, Facebook status updates) into beautiful, simple diagrams that tease out unseen patterns and connections. Good design, he suggests, is the best way to navigate information glut -- and it may just change the way we see the world. A quiz, thought provoking question, and links for further study are provided to create a lesson around the 18-minute video. Educators may use the platform to easily "Flip" or create their own lesson for use with their students of any age or level.
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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.
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 an introduction to statistical data analysis. Topics are chosen from applied probability, sampling, estimation, hypothesis testing, linear regression, analysis of variance, categorical data analysis, and nonparametric statistics.
A whirl-wind tour of the statistics used in behavioral science research, covering topics including: data visualization, building your own null-hypothesis distribution through permutation, useful parametric distributions, the generalized linear model, and model-based analyses more generally. Familiarity with MATLABA, Octave, or R will be useful, prior experience with statistics will be helpful but is not essential. This course is intended to be a ground-up sketch of a coherent, alternative perspective to the "null-hypothesis significance testing" method for behavioral research (but don't worry if you don't know what this means).
This case study is retrieved from the open book Open Data as Open Educational Resources. Case studies of emerging practice.
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.
“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”.
Using two different coins and recording the results of both coins helps students dispel this initial misconception as they analyze the graph results. Class discussion should focus on analyzing the data to determine if the game is fair or not. Directions and gameboard are included in the download.
These activities support students as they conceptually develop a sense of how probability affects the outcome of games. Students will find that applying their knowledge of probability will help them win some of the games
This assembles material for the LPHY2131 data analysis lab at UCLouvain. The documentation covers the three sessions of the laboratory and provides some additional information. The results that are obtained in this lab can be compared to the published cross-section measurement for the Z and W at the LHC, at 7TeV, by the CMS collaboration: Measurement of the Inclusive W and Z Production Cross Sections in pp Collisions at sqrt(s) = 7 TeV
The applet in this section allows for simple data analysis of univariate data. Users can either generate normal or uniform data for k samples or copy and paste data from another source to a text box. A univariate analysis is performed for all k samples.
- Statistics and Probability
- Material Type:
- Consortium for the Advancement of Undergraduate Statistics Education
- Provider Set:
- C. Anderson-Cook, S. Dorai-Raj, T. Robinson, Virginia Tech Department of Statistics
- Date Added:
This course emphasizes the analysis of ethnographic and other forms of qualitative data in public health research. We introduce various interpretive analytic approaches, explore their use, and guide students in applying them to data. We also introduce the use of computer software for coding textual data (Atlas.ti). Students analyze data they have collected as part of fieldwork projects initiated in 410.690 and write up the results in a final paper. Classroom sessions include lectures, discussions, intensive group work related to the fieldwork projects, and instruction in the computer lab.
The objectives of this lesson are to understand the characteristics of the data that is collected using the Radio JOVE antenna/receiver system. Using calibrations for the equipment, one can determine a proper measure of the peak intensity of the output, identify the duration of the solar or Jovian radio activity, and calculate the approximate total power emitted by the source. Master these concepts by completing example problems.
This is an introduction to R designed for participants with no programming experience. These lessons can be taught in a day. They start with some basic information about R syntax, the RStudio interface, and move through how to import CSV files, the structure of data frames, how to deal with factors, how to add/remove rows and columns, and finish with how to calculate summary statistics for each level and a very brief introduction to plotting.
Institute for Health Metrics and Evaluation (IHME) strives to make its data freely and easily accessible and to provide innovative ways to visualize complex topics. Our data visualizations allow you to see patterns and follow trends that are not readily apparent in the numbers themselves. Here you can watch how trends in mortality change over time, choose countries to compare progress in a variety of health areas, or see how countries compare against each other on a global map.
Students build on their existing air quality knowledge and a description of a data set to each develop a hypothesis around how and why air pollutants vary on a daily and seasonal basis. Then they are guided by a worksheet through an Excel-based analysis of the data. This includes entering formulas to calculate statistics and creating plots of the data. As students complete each phase of the analysis, reflection questions guide their understanding of what new information the analysis reveals. At activity end, students evaluate their original hypotheses and “put all of the pieces together.” The activity includes one carbon dioxide worksheet/data set and one ozone worksheet/data set; providing students and/or instructors with a content option. The activity also serves as a good standalone introduction to using Excel.
- Atmospheric Science
- Material Type:
- Provider Set:
- Ashley Collier
- Ben Graves
- Daniel Knight
- Drew Meyers
- Eric Ambos
- Eric Lee
- Erik Hotaling
- Hanadi Adel Salamah
- Joanna Gordon
- Katya Hafich
- Michael Hannigan
- Nicholas VanderKolk
- Olivia Cecil
- Victoria Danner
- Date Added:
This OER is an online tutorial for students learning the software R. The OER is anintroductory guide to exploratory data analysis in R. It is aimed primarily at graduate students in the biomedical sciences, but could be more broadly applicable.
The materials are an interactive tutorial that guides students through some basic analysis, asking them to input code to answer questions about conducting such analysis in R. Hints for the correct code are provided and a short quiz tests them on what they have learnt in the tutorial.
This guide is written using the ‘learnr’ package in the R software environment. The underlying R Markdown source code for this OER material is provided for download, with the intention that lecturers can modify it according to specific requirements. Students can use these materials as a standalone learning resource and use these materials for further learning.
This course is an introduction to data cleaning, analysis and visualization. We will teach the basics of data analysis through concrete examples. You will learn how to take raw data, extract meaningful information, use statistical tools, and make visualizations. This was offered as a non-credit course during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month.
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.
This article discusses how the study of weather can meet the NCTM Data Analysis and Probability standard. Links to lessons for grades K-2 and 3-5 are provided.
- Environmental Science
- Material Type:
- Lesson Plan
- Ohio State University College of Education and Human Ecology
- Provider Set:
- Beyond Penguins and Polar Bears: An Online Magazine for K-5 Teachers
- Jessica Fries-Gaither
- Date Added:
An quick overview of AI from both the technical and the philosophical points of view. Topics discussed include search, A*, Knowledge Representation, Neural Nets. Video of each class is available, as are problem sets.