Online Statistics: An Interactive Multimedia Course of Study is an introductory-level statistics book. The material is presented both as a standard textbook and as a multimedia presentation. The book features interactive demonstrations and simulations, case studies, and an analysis lab.

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.

OpenIntro Statistics is a dynamic take on the traditional curriculum, being successfully used at Community Colleges to the Ivy League

This “How To….” Manual is intended to assist the new user in implementing standard statistical methods, both parametric and non-parametric, using R statistical software. Its focus is on R implementation, not statistical theory. It includes the R commands, with examples, for the following: proportion tests, t-tests, ANOVA, variance tests, several correlation measures and regression models, Mann-Whitney-Wilcoxon tests, Kruskal-Wallis tests, chi-squared tests, multiple pairwise comparisons and effect sizes. Basic graphical methods are also illustrated.

دروس الفصل الثاني التي تم انتاجها في تجربة التعلم عن بعد في دولة قطر

This sample shell is produced by the California Community Colleges CVC-OEI to support faculty in the use of Open Educational Resources and development of courses aligned to the OEI Course Design Rubric. The shell may be used for online, hybrid, &/or face-to-face classes. The shell is available for all faculty, not just those faculty in the CCC system. The team producing this shell includes Helen Graves, Liezl Madrona, Cyrus Helf, Nicole Woolley & Barbara Illowsky. If you are having challenges importing the shell, here are some steps to take. (1) Create an empty shell in your sandbox. (2) Import the Canvas Commons course into your shell. (3) Adapt the content as you wish. (4) If all else fails, contact your college IT person or Canvas administrator.

This sample shell is produced by the California Community Colleges CVC-OEI to support faculty in the use of Open Educational Resources and development of courses aligned to the OEI Course Design Rubric. The shell may be used for online, hybrid, &/or face-to-face classes. The shell is available for all faculty, not just those faculty in the CCC system. The team producing this shell includes Helen Graves, Liezl Madrona, Cyrus Helf, Nicole Woolley & Barbara Illowsky. If you are having challenges importing the shell, here are some steps to take. (1) Create an empty shell in your sandbox. (2) Import the Canvas Commons course into your shell. (3) Adapt the content as you wish. (4) If all else fails, contact your college IT person or Canvas administrator.

This sample shell is produced by the California Community Colleges CVC-OEI to support faculty in the use of Open Educational Resources and development of courses aligned to the OEI Course Design Rubric. The shell may be used for online, hybrid, &/or face-to-face classes. The shell is available for all faculty, not just those faculty in the CCC system. The team producing this shell includes Helen Graves, Liezl Madrona, Cyrus Helf, Nicole Woolley & Barbara Illowsky. If you are having challenges importing the shell, here are some steps to take. (1) Create an empty shell in your sandbox. (2) Import the Canvas Commons course into your shell. (3) Adapt the content as you wish. (4) If all else fails, contact your college IT person or Canvas administrator.

This sample shell is produced by the California Community Colleges CVC-OEI to support faculty in the use of Open Educational Resources and development of courses aligned to the OEI Course Design Rubric. The shell may be used for online, hybrid, &/or face-to-face classes. The shell is available for all faculty, not just those faculty in the CCC system. The team producing this shell includes Helen Graves, Liezl Madrona, Cyrus Helf, Nicole Woolley & Barbara Illowsky. If you are having challenges importing the shell, here are some steps to take. (1) Create an empty shell in your sandbox. (2) Import the Canvas Commons course into your shell. (3) Adapt the content as you wish. (4) If all else fails, contact your college IT person or Canvas administrator.

This sample shell is produced by the California Community Colleges CVC-OEI to support faculty in the use of Open Educational Resources and development of courses aligned to the OEI Course Design Rubric. The shell may be used for online, hybrid, &/or face-to-face classes. The shell is available for all faculty, not just those faculty in the CCC system. The team producing this shell includes Helen Graves, Liezl Madrona, Cyrus Helf, Nicole Woolley & Barbara Illowsky. If you are having challenges importing the shell, here are some steps to take. (1) Create an empty shell in your sandbox. (2) Import the Canvas Commons course into your shell. (3) Adapt the content as you wish. (4) If all else fails, contact your college IT person or Canvas administrator.

Companion Site for Harvard Medical School Canvas Network MOOC Best Practices for Biomedical Research Data Management. This Open Science Framework project site includes all the materials contained in the Canvas course including: readings and resources; slide presentations; video lectures; activity outlines; research case studies and questions; and quiz questions with answer guide.

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.

Open courseware for Macroevolution, focusing on research methods and software packages, such as R.

Course description
Evolutionary thinking provides the underpinnings of modern biology. In recent decades, the field of macroevolution (evolution above the species level) has matured into a rich discipline with a well-developed mathematical theory for testing hypotheses of species diversification, for understanding trait evolution, and evaluating patterns of covariation across the tree of life. This course will provide a synthetic view of biology and how life on earth has changed over time.

Course Outcomes
Upon completion of the course, students will:
Understand patterns of diversity in the fossil record, and changes in that diversity over time
Understand macroevolutionary patterns and processes, and the difference between gradualism, stasis, and punctuated equilibrium
Become familiar with ‘tree thinking’, and understand the principles of using a phylogenetic perspective to address evolutionary questions in biology
Gain experience in applying cutting-edge phylogenetic methods for testing hypotheses in macroevolution

This is a rigorous, open, and equitable Calculus I class. It follows the OpenStax Calculus I book and uses the MyOpenMath course created by Larry Green and the ZTC grant as homework and extra resources. As well as lecture notes and worksheets created in Microsoft Word.

The structure of the course is that it follows a flipped class model, where students are required to watch lecture video created off of the lecture notes. Then students work on sectional exercises embedded in Canvas from MyOpenMath and work by themselves on the hard worksheet before the last day of the class during the week. On that day of class students will have the opportunity to work in groups on the worksheet problems.

They will be motivated to work on the problems in their groups because they will then teach the professor their random problem received as well as their group mates have points associated with their group mates work. The way they do this is through a program called GoReact that is embedded in Canvas. The main thing about this program is that students can easily share a recorded video of them teaching the math to the professor and the professor in turn can give video feedback telling the student how much they rock or letting them know what went wrong. These Teach Me Video are the foundation of the course and are the only thing the professor grades for the week unless there is a test. They are fun for both the instructor and students and increase the teacher student relationship as well as the student to student relationship. It is also easy to bump the Teach Me Videos up to make students who didn't fully understand the problem by making them redo the video to get some points back.

This resource is a collection of full hour-long lectures and accompanying notes covering most of chapters 3-6 of the OpenStax Algebra and Trigonometry book. The notes are provided in both .DOCX and .PDF format to accommodate downloading ease. Students should be encouraged to print notes and fill them out as they watch through the lectures.

Calculus is a year-long course that is comparable to calculus courses at the college or university level. In this course, the technological tools required for exploring, learning, and communicating calculus concepts include Microsoft Word with Microsoft Equation (or its equivalent) for typing math assignments, an approved graphing calculator (see a list of acceptable calculators at College Board AP Central) for the AP Calculus exam, and some type of grapher for creating graphs online such as Graphmatica (Windows) or Grapher (MAC) or an online graphing utility of your choice.

This course will place a emphasis on the continued study of integers, order of operations, variables, expressions, equations and polynomials. You will solve equations, write and solve proportions, explore polynomials and build an understanding of important mathematical properties.

The purpose of this course is to provide background in the ways in which psychologists evaluate data collected from research projects. A researcher may gather many pieces of data that describe a group of research subjects and there are common ways in which these pieces of information are presented. Secondly, statistical tests can help investigators draw inferences about the relationship of the research sample to the general population it is supposed to represent. As a student of psychology or any other discipline that uses research data to explore ideas, it is important that you know how data is evaluated and that you gain an understanding of the ways in which these procedures help to summarize and clarify data.

This is a path for those of you who want to complete the Data Science undergraduate curriculum on your own time, for free, with courses from the best universities in the World. In our curriculum, we give preference to MOOC (Massive Open Online Course) style courses because these courses were created with our style of learning in mind. OSSU Data Science uses the report Curriculum Guidelines for Undergraduate Programs in Data Science (https://www.amstat.org/asa/files/pdfs/EDU-DataScienceGuidelines.pdf) as our guide for course recommendation.

It is possible to finish within about 2 years if you plan carefully and devote roughly 20 hours/week to your studies. Learners can use this spreadsheet (linked in resource) to estimate their end date. Make a copy and input your start date and expected hours per week in the Timeline sheet. As you work through courses you can enter your actual course completion dates in the Curriculum Data sheet and get updated completion estimates.

Python and R are heavily used in Data Science community and our courses teach you both. Remember, the important thing for each course is to internalize the core concepts and to be able to use them with whatever tool (programming language) that you wish.

The Data Science curriculum assumes the student has taken high school math and statistics.