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AI 101
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Machine vision. Data wrangling. Reinforcement learning. What do these terms even mean? In AI 101, MIT researcher Brandon Leshchinskiy offers an introduction to artificial intelligence that’s designed specifically for those with little to no background in the subject. The workshop starts with a summary of key concepts in AI, followed by an interactive exercise where participants train their own algorithm. Finally, it closes with a summary of key takeaways and Q/A. All are welcome!

Subject:
Applied Science
Computer Science
Engineering
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Leshchinskiy, Brandon
Date Added:
09/01/2021
AI skills for Engineers: Supervised Machine Learning
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Learn the fundamentals of machine learning to help you correctly apply various classification and regression machine learning algorithms to real-life problems.

Subject:
Applied Science
Computing and Information
Engineering
Mathematics
Statistics and Probability
Material Type:
Full Course
Provider:
Delft University of Technology
Provider Set:
TU Delft OpenCourseWare
Author:
Hanne Kekkonen
Tom Viering
Date Added:
07/28/2023
Applied Econometrics: Mostly Harmless Big Data
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This course covers empirical strategies for applied micro research questions. Our agenda includes regression and matching, instrumental variables, differences-in-differences, regression discontinuity designs, standard errors, and a module consisting of 8–9 lectures on the analysis of high-dimensional data sets a.k.a. “Big Data”.

Subject:
Applied Science
Computer Science
Economics
Engineering
Mathematics
Social Science
Statistics and Probability
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Angrist, Joshua
Chernozhukov, Victor
Date Added:
09/01/2014
Averages: Still Flawed
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This learning video continues the theme of an early BLOSSOMS lesson, Flaws of Averages, using new examples—including how all the children from Lake Wobegon can be above average, as well as the Friendship Paradox. As mentioned in the original module, averages are often worthwhile representations of a set of data by a single descriptive number. The objective of this module, once again, is to simply point out a few pitfalls that could arise if one is not attentive to details when calculating and interpreting averages. Most students at any level in high school can understand the concept of the flaws of averages presented here. The essential prerequisite knowledge for this video lesson is the ability to calculate an average from a set of numbers. Materials needed include: pen and paper for the students; a blackboard or equivalent; and coins (one per student) or something similar that students can repeatedly use to create a random event with equal chances of the two outcomes (e.g. flipping a fair coin). The coins or something similar are recommended for one of the classroom activities, which will demonstrate the idea of regression toward the mean. Another activity will have the students create groups to show how the average number of friends of friends is greater than or equal to the average number of friends in a group, which is known as The Friendship Paradox. The lesson is designed for a typical 50-minute class session.

Subject:
Mathematics
Material Type:
Lecture
Provider:
MIT
Provider Set:
MIT Blossoms
Author:
Dan Livengood, Rhonda Jordan
Date Added:
02/13/2015
Big Data Analytics: IOT BASED RECOMMENDATION SYSTEM FOR TOURISM
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The IOT services are for customer convenience, control in online booking IOT services such as radio station, smart coffee makers, dim lights and energy programmed lights. Our System will able to recommend the valid customer opinion by analyzing UAE, UK and Oman hotel aspects like services, value, cleanliness and location from customers’ reviews. it include the Big Analytics, Hadoop, HDFS, Sentiment Analytics, Emotion Analytics, ANOVA in Map-Reduce.

Subject:
Computer Science
Material Type:
Module
Author:
Sharjeel Imtiaz
Date Added:
04/11/2019
Big Data Analytics: IOT Recomendation system for Tourism
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CC BY-NC-SA
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This project will recommend a big data analytics tool for the customers, ministry and hotels in Oman to adapt new hotel services after considering together hotel services with customer opinions. The IOT services are for customer convenience, control in online booking IOT services such as radio station, smart coffee makers, dim lights and energy programmed lights.The big data analytics will analyze the hotel information , rating and reviews of UK , Dubai to recomend aspect like services especially IOT services. The coverage of Analysis in R: Big data Analytics with Hadoop/HDFS Sentiment AnalysisEmotion Analysis Machine Learning K-mean , Regression and Neural NetworkAnova version to analyze Big data of 90k reviews 

Subject:
Information Science
Material Type:
Module
Author:
sharjeel imtiaz
Date Added:
04/11/2019
Biostatistics Methods 2
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CC BY-SA
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The aim of this course is to provide fundamental statistical concepts and tools relevant to the practice of summarizing, analyzing, and visualizing data. This course will build your knowledge of the fundamental principles of biostatistical inference. The course will focus on linear regression and generalized linear regression models. We will use a variety of examples and exercises from scientific, medical, and public health research.

Material Type:
Activity/Lab
Full Course
Homework/Assignment
Interactive
Lecture Notes
Lesson Plan
Simulation
Syllabus
Provider:
University of Massachusetts
Provider Set:
Individual Authors
Author:
Jeff Goldsmith
Nicholas G Reich
Date Added:
04/07/2014
Data Science: A First Introduction with Python
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This is a textbook for teaching data science using the Python programming language at a general first year undergraduate level.

Subject:
Mathematics
Measurement and Data
Statistics and Probability
Material Type:
Textbook
Author:
Joel Ostblom
Lindsey Heagy
Melissa Lee
Tiffany Timbers
Trevor Campbell
Date Added:
04/02/2024
How to Process, Analyze and Visualize Data
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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.

Subject:
Applied Science
Computer Science
Engineering
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Marcus, Adam
Wu, Eugene
Date Added:
01/01/2012
Identifying a Theft Suspect
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This model-eliciting activity (MEA) challenges students to develop a model for predicting the characteristics of a person who has committed a crime. Students work with real data on shoe length, height, and gender to develop the model. Students write a report to the crime victim that identifies a suspect and justifies their decision. The activity sets the stage for students to learn about regression models, and reinforces their understanding of central tendency and variability. It is suggested that this activity be used prior to a formal introduction to linear relationships.

Subject:
Mathematics
Material Type:
Activity/Lab
Assessment
Provider:
Science Education Resource Center (SERC) at Carleton College
Provider Set:
Measuring Study Effectiveness
Date Added:
08/28/2012
Introduction to Modern Statistics
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This is the website for Introduction to Modern Statistics, First Edition by Mine Çetinkaya-Rundel and Johanna Hardin. Introduction to Modern Statistics, which we’ll refer to as IMS going forward, is a textbook from the OpenIntro project.

Copyright © 2021.
First Edition.
Version date: June 27, 2021.

This textbook and its supplements, including slides, labs, and interactive tutorials, may be downloaded for free at
openintro.org/book/ims.

This textbook is a derivative of OpenIntro Statistics 4th Edition and Introduction to Statistics with Randomization and Simulation 1st Edition by Diez, Barr, and Çetinkaya-Rundel, and it’s available under a Creative Commons Attribution-ShareAlike 3.0 Unported United States License. License details are available at the Creative Commons website: creativecommons.org.

Source files for this book may be found on GitHub at github.com/openintrostat/ims.

Subject:
Mathematics
Statistics and Probability
Material Type:
Textbook
Author:
Johanna Hardin
Mine Çetinkaya-Rundel
Date Added:
06/28/2021
Introductory Business Statistics
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CC BY
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The book "Introductory Business Statistics" by Thomas K. Tiemann explores the basic ideas behind statistics, such as populations, samples, the difference between data and information, and most importantly sampling distributions. The author covers topics including descriptive statistics and frequency distributions, normal and t-distributions, hypothesis testing, t-tests, f-tests, analysis of variance, non-parametric tests, and regression basics. Using real-world examples throughout the text, the author hopes to help students understand how statistics works, not just how to "get the right number."

Subject:
Mathematics
Statistics and Probability
Material Type:
Textbook
Provider:
BCcampus
Provider Set:
BCcampus Open Textbooks
Author:
Thomas K. Tiemann
Date Added:
12/02/2019
Introductory Business Statistics with Interactive Spreadsheets - 1st Canadian Edition
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"Introductory Business Statistics with Interactive Spreadsheets - 1st Canadian Edition" is an adaptation of Thomas K. Tiemann's book, "Introductory Business Statistics". In addition to covering basics such as populations, samples, the difference between data and information, and sampling distributions, descriptive statistics and frequency distributions, normal and t-distributions, hypothesis testing, t-tests, f-tests, analysis of variance, non-parametric tests, and regression basics, the following information has been added: the chi-square test and categorical variables, null and alternative hypotheses for the test of independence, simple linear regression model, least squares method, coefficient of determination, confidence interval for the average of the dependent variable, and prediction interval for a specific value of the dependent variable. This new edition also allows readers to learn the basic and most commonly applied statistical techniques in business in an interactive way -- when using the web version -- through interactive Excel spreadsheets. All information has been revised to reflect Canadian content.

Subject:
Business and Communication
Mathematics
Statistics and Probability
Material Type:
Textbook
Provider:
BCcampus
Provider Set:
BCcampus Open Textbooks
Author:
Mohammad Mahbobi, Thompson Rivers University; Thomas K. Tiemann, Elon University
Date Added:
04/19/2016
Lab in Psycholinguistics
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Hands-on experience designing, conducting, analyzing, and presenting experiments on the structure and processing of human language. Focuses on constructing, conducting, analyzing, and presenting an original and independent experimental project of publishable quality. Develops skills in reading and writing scientific research reports in cognitive science, including evaluating the methods section of a published paper, reading and understanding graphical displays and statistical claims about data, and evaluating theoretical claims based on experimental data. Instruction and practice in oral and written communication provided.

Subject:
Arts and Humanities
Life Science
Linguistics
Physical Science
Social Science
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Gibson, Edward
Date Added:
02/01/2017
Linear Regression (Excel) and Cellular Respiration for Biology, Chemistry and Mathematics [version 1.0]
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Students typically find linear regression analysis of data sets in a biology classroom challenging. These activities could be used in a Biology, Chemistry, Mathematics, or Statistics course. The collection provides student activity files with Excel instructions and Instructor Activity files with Excel instructions and solutions to problems.

Students will be able to perform linear regression analysis, find correlation coefficient, create a scatter plot and find the r-square using MS Excel 365. Students will be able to interpret data sets, describe the relationship between biological variables, and predict the value of an output variable based on the input of an predictor variable.

Subject:
Algebra
Biology
Life Science
Mathematics
Statistics and Probability
Material Type:
Activity/Lab
Full Course
Lecture
Lesson Plan
Provider:
BioQUEST Curriculum Consortium
Provider Set:
Quantitative Biology at Community Colleges
Date Added:
12/04/2021
Manpower Planning: HR Demand Forecasting Lecture 4
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CC BY-ND
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This video lecture describes manpower planning process, various trends affecting HR demand forecasting. It also discusses quantitative and qualitative techniques of HR Demand forecasting.

Subject:
Business and Communication
Management
Material Type:
Lecture
Author:
Dr. Chandrakanta Sahoo
Date Added:
07/31/2021
Math 1010: Math for General Studies
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This is a three-credit course which covers topics that enhance the students’ problem solving abilities, knowledge of the basic principles of probability/statistics, and guides students to master critical thinking/logic skills, geometric principles, personal finance skills. This course requires that students apply their knowledge to real-world problems. A TI-84 or comparable calculator is required. The course has four main units: Thinking Algebraically, Thinking Logically and Geometrically, Thinking Statistically, and Making Connections. This course is paired with a course in MyOpenMath which contains the instructor materials (including answer keys) and online homework system with immediate feedback. All course materials are licensed by CC-BY-SA unless otherwise noted.

Date Added:
07/08/2021
Math 1010: Math for General Studies, Making Connections, Regression Project
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In this project, students will have the opportunity to explore regression in more detail. Specifically, they will be looking at how Covid-19 Stimulus money was distributed to each state in the country.

Subject:
Mathematics
Material Type:
Full Course
Author:
Jillian Miller
Megan Simmons
Stefanie Holmes
Jessica Chambers
Brad Fox
Heather Doncaster
Ashley Morgan
Misty Anderson
Date Added:
07/08/2021