Course Catalog

Select a course that interests you.

LiMS University offers a number of courses related to laboratory information management systems (LIMS).

An Introduction to Electronic Medical Records – HIBBs 1890

This 2011 HIBBs course module provides an introductory look at electronic medical records. Specifically, the module will allow users who complete it to:

Define what an electronic medical record is.
Explain what electronic medical records encompass.
Understand why electronic medical records are important.
List examples of electronic medical record applications.

Course Organization

This represents a learning module and not so much a full course. The user may choose how they wish to view this material: audio-only, slides-only, slides with video, full streaming video, and/or downloadable video files.

Recommended Reading

No recommended reading is outlined for this learning module.

An Introduction to Medical Informatics – HIBBs 1891

This 2011 HIBBs course module provides an introductory look at the applied field of medical informatics, which arguably is an early name for what is now commonly called health informatics. Specifically, the module will allow users who complete it to:

define medical informatics.
explain what medical informatics encompasses.
understand why medical informatics is important.
list examples of medical informatics applications.

Course Organization

This represents a learning module and not so much a full course. The user may choose how they wish to view this material: audio-only, slides with notes, full video, and/or video divided into parts.

Recommended Reading

No recommended reading is outlined for this learning module.

Biochemistry I – CMU 03-232

NOTE: If you want to view the associated course material without logging into CMU OLI’s system but can’t seem to access the sessions, then please click the “Enter Course” button found on this page first. We are unable to link directly to sessions.

—–

This 2005 Carnegie Mellon University Open Learning Initiative course introduces biochemistry to both biology and chemical engineering majors. A consistent theme in this course is the development of a quantitative understanding of the interactions of biological molecules from a structural, thermodynamic, and molecular dynamic point of view. A molecular simulation environment provides the opportunity for you to explore the effect of molecular interactions on the biochemical properties of systems.

The major topics in the course are:

protein function, including oxygen transport, antibody function, and enzyme catalyzed reactions.
structure and function of carbohydrates and their importance in central metabolism.
lipids and biological membranes.
central aspects of metabolism and metabolic control.
nucleic acid biochemistry, with emphasis on recombinant DNA technology.

This course assumes students have taken introductory chemistry courses (including the topic of basic thermodynamics), as well as an introductory organic chemistry course. An introductory biology course is not a prerequisite for the course; however, students would benefit from some prior exposure to biology, even at the high school level. Required mathematical skills include simple algebra and differential calculus.

Learning Objectives

The two main learning goals of the course are:

predicting how changes in structure affect function.
utilizing quantitative approaches to characterize structure-function relationships in biochemical systems.

The course begins with amino acids and transitions into protein structure and thermodynamics. Protein-ligand binding is treated for both non-cooperative and cooperative binding using immunoglobulins and oxygen transport as examples. The enzymatic function of proteins is explored using serine and HIV proteases as examples. Enzyme kinetics is treated using steady-state kinetic analysis. Enzyme inhibition is treated quantitatively using HIV protease as a key example.

Carbohydrate and lipids are presented in sufficient depth to allow the student to fully understand major aspects of central metabolism. The discussion of metabolism is focused on energy generation, fermentation, and metabolic control.

The course concludes with an extensive section on nucleic acid biochemistry. The focus of this section is to provide the student with sufficient background so that they are literate in the recombinant DNA technologies as they relate to protein production using recombinant methods.

After a treatment of molecular forces and solution properties, the course builds on molecular and energetic descriptions of fundamental monomeric building blocks to develop a comprehensive understanding of the biological function of polymers and molecular assemblies at the molecular and cellular level. In addition to multiple case studies, the course concludes with a capstone exercise that leads students through the steps required to produce recombinant proteins for drug discovery.

The course was designed to be used as a stand-alone (with no instruction in the background) course; however, studies have shown that it is best and most effectively used in the hybrid mode together with face to face instruction.

Course Organization

The structure of this entire course is based on 11 sessions spread out over two units: an introductory glossary covering important concepts you’ll need for the course and the lectures. Sessions in the course are constructed from the following types of components:

Scaffolded homework activities provide learners with hints and feedback on an as-needed basis and fade this help appropriately such that learners remain challenged but not floundering.
Virtual laboratory activities couple the mathematics of the course with authentic biochemistry experiments, helping learners see how their calculations relate to biochemistry practice.
End-of-module quizzes help learners assess their knowledge.

The course is different than OpenCourseWare and other courses in that you will utilize Carnegie Mellon’s Open Learning Initiative (OLI) website to progress through the course. The sessions include learning objects and online labs to help you apply and remember what you learn.

If you wish to save your progress through the course at OLI, create an account and log in each time you wish to continue the course.

Further Reading

No textbook is associated with this course. However, the free online ChemPRIME wiki and Biochemistry wiki may be useful in helping you further understand chemical and biochemical concepts within a broad range of contexts that relate to other disciplines and everyday life.

Other Requirements

To do these activities, you will need to have Flash, Quicktime, and Java installed. These programs are free. More detailed information is provided when you enter the course, found under “Test and Configure Your System.”

Chemistry I: Stoichiometry – CMU 09-105

—–

This 2005 Carnegie Mellon University Open Learning Initiative course introduces chemical stoichiometry, which is a set of tools chemists use to count molecules and determine the amounts of substances consumed and produced by reactions. The course is set in a scenario that shows how stoichiometry calculations are used in real-world situations. The covered topics are similar to that of a high school chemistry course, although with a greater focus on reactions occurring in solution and on the use of the ideas to design and carry out experiments.

The course also uses a scenario — arsenic contamination of the Bangladesh’s water supply — to motivate and organize the content. Traditional courses tend to follow a bottom-up approach to learning chemistry. This traditional approach teaches abstract concepts and tools before discussing their practical application, which results in students learning bits of unconnected knowledge that are rarely usable let alone memorable. In this stoichiometry course, scenarios are used both to motivate the material and to provide a framework in which students can organize their knowledge.

Learning Objectives

This course is designed to take you from the starting point of high school chemistry to a point where you feel comfortable with the primary tools of stoichiometry. Learn how chemists discuss the amounts of chemical substances. Learn various techniques chemists use to measure the amount of a substance in water or solution. Perform stoichiometric experiments that characterize the ability of various powders to remove a substance from water or solution.

The course was designed to be used as a stand alone (with no instruction in the background) course; however, studies have shown that it is best and most effectively used in the hybrid mode together with face to face instruction.

Course Organization

The structure of this entire course is based on 15 sessions spread out over two units. Sessions in the course are constructed from the following types of components:

Videos introduce the scenarios and chemical concepts, and provide worked examples of stoichiometry computations.
Scaffolded homework activities provide learners with hints and feedback on an as-needed basis, and fade this help appropriately such that learners remain challenged but not floundering.
Virtual laboratory activities couple the mathematics of the course with authentic chemistry experiments, helping learners see how their calculations relate to chemistry practice.
End-of-module quizzes help learners assess their knowledge.

If you wish to save your progress through the course at OLI, create an account and log in each time you wish to continue the course.

Further Reading

No textbook is associated with this course. However, the free online ChemPRIME wiki may be useful in helping you further understand stoichiometry and other chemical concepts within a broad range of contexts that relate to other disciplines and everyday life.

Other Requirements

Chemistry II: Beyond Stoichiometry – CMU 09-106

—–

This 2005 Carnegie Mellon University Open Learning Initiative course provides an overview of themodynamics, kinetics, and chemical equilibrium. Topics include the flow of energy in chemical systems; the spontaneity of chemical processes, i.e. entropy and free energy; the mechanisms and rates of chemical reations; and the use of chemical equilibrium to reason about acid-base chemistry, solubility and redox chemistry. Applications include the energy economy, biological systems, and environmental chemistry.

Many of the topics covered in this course are concerned with quantitative aspects of chemical reactions. For instance, thermodynamics considers the amount of heat given off when a given amount of chemicals react, kinetics considers the rate at which reactants are converted into products, and equilibrium considers the relative amounts of reactants and products present at steady state. For all of these topics, it is important that you are comfortable with the tools of stoichiometry, since these are the tools that allow us to express and work with the amounts of chemical species present in a given system. Since most of the course involves species in solution, solution stoichiometry concepts such as molarity and dilution are especially important. The stoichiometry of chemical reactions is also of central importance, including especially limiting reagent calculations.

Learning Objectives

This course is designed to take you from the application of stoichiometry to more quantitative aspects of chemical reactions. By the end of the course you should be able to demonstrate the application of concepts like thermodynamics, kinetics, and chemical equalibrium to chemical reactions. The various applications of acid-based and redox chemistry should be fully undestood, as well as managing solubility equilibria.

Course Organization

The structure of this entire course is based on 32 sessions spread out over nine units. The course contains a full semester of lectures interspersed with concept quizzes, practice problems, and virtual lab activities. The course is broken up by topic into units and modules. This course is quite different from other OLI courses, in that it is based on videos from a live classroom.

Lecture notes and resources will be associated with each session. You are highly recommended to download and/or access all the associated resources before beginning the video parts of the session.

Also note that this course is different than OpenCourseWare and other courses in that you will utilize Carnegie Mellon’s Open Learning Initiative (OLI) website to progress through the course. The sessions include learning objects and online labs to help you apply and remember what you learn.

If you wish to save your progress through the course at OLI, create an account and log in each time you wish to continue the course.

Further Reading

The free online ChemPRIME wiki is used as the main source for session readings.

Other Requirements

It is important that you are comfortable with the tools of stoichiometry, since these are the tools that allow us to express and work with the amounts of chemical species present in a given system. If you’re not familiar with this topic, please go through Chemistry I: Stoichiometry first.

Designing and Sustaining Technology Innovation for Global Health Practice – MIT HST939

This 2008 MIT OpenCourseWare course provides guidance on tackling and solving problems in innovation for global health practice through the rationale design of technology and service solutions.

Learning Objectives

The learning objectives for this course are:

1. Show how innovative technologies and service based solutions will shape and redefine the global health care marketplace.

2. Learn how technologies can enhance service delivery, distribution systems, in-service training, and medical education.

3. Design an innovative field based solution to address a current or emerging global health problem, drawing upon understanding of tools and principles acquired.

Course Organization

The course combines a series of lectures with a semester-long design project in collaboration with a sponsor around a real world field-based problem.

The course included three “reflection papers,” an oral presentation, and a final paper.

Recommended Reading

A significant amount of recommended reading was assigned for each lecture session. Please reference the associated MIT page for more information.

Additional study material was handed out for the semester, consisting of three case studies:

* Deploying Rapid CD-4 Tests for HIV/AIDS

* Public Private Partnerships for Health

* Refugee Absorption and Sustainability

EMR Implementation Considerations – HIBBs 1899

This 2011 HIBBs course module provides an introductory look at what to consider when implementing an electronic medical record (EMR). Specifically, the module will allow users who complete it to:

assess the value of a medical informatics solution.
be aware of issues associated with the rolling out of any type of electronic medical record system.
explain what is necessary for a successful implementation of a medical informatics solution.

Course Organization

This represents a learning module and not so much a full course. The user may choose how they wish to view this material: slides-only and/or slides with video.

Recommended Reading

No recommended reading is outlined for this learning module.

Ethical Issues in Public Health – JHSPH 306.655

This 2006 JHSPH OpenCourseWare graduate course focuses on ethical theory and current ethical issues in public health and health policy, including resource allocation, the use of summary measures of health, the right to health care, and conflicts between autonomy and health promotion efforts.

Learning Objectives

By the end of this course, students should be able to:

identify and define moral issues in the context of public health practice.
distinguish between a moral issue or argument and other types of issues or arguments.
articulate moral arguments for or against public health policies or practices.

Course Organization

The content of this course is divided into eight separate lectures held over a period of eight weeks.

Student evaluation for this course was originally based on class participation, a group project, and a paper evaluating ethical issues in the student’s area of public health specialization.

A description of case studies is included for all but the first session. (It appears to be missing from JHSPH OpenCourseWare). PDF slides from the lectures are also available in some cases; however, JHSPH did not make any audio or video available to go along with the slides.

Note: The case studies act as assignments. Read each one carefully and perform the requested task as if it were an assignment.

Further Reading

No text was originally assigned for this course. However, both required and recommended reading was assigned with each session. Consult the Recommended Reading associated with each session.

Fundamentals of Epidemiology I – JHSPH 550.694.81

This 2008 JHSPH OpenCourseWare graduate course examines the basic concepts of epidemiology and biostatistics as applied to public health problems. Emphasis is placed on the principles and methods of epidemiologic investigation, appropriate summaries and displays of data, and the use of classical statistical approaches to describe the health of populations.

Topics include the dynamic behavior of disease; usage of rates, ratios and proportions; methods of direct and indirect adjustment, and clinical life table which measures and describes the extent of disease problems. Various epidemiologic study designs for investigating associations between risk factors and disease outcomes are also introduced, culminating with criteria for causal inferences. The application of these disciplines in the areas of health services, screening, genetics, and environment policy are presented. The influence of epidemiology and biostatistics on legal and ethical issues are also discussed.

Learning Objectives

After completion of this course and Fundamentals of Epidemiology II, students will be able to apply principles of epidemiology and biostatistics to the prevention of disease and the improvement of health.

Objectives of the course include:

distinguishing the roles and relationships between epidemiology and biostatistics in the prevention of disease and the improvement of health.
computing basic descriptive statistics and explore data analytic methods.
demonstrating a basic understanding of epidemiologic methods and study design.
combining appropriate epidemiological concepts and statistical methods.

Course Organization

The content of this first of two courses is divided into eleven lectures spread out over four “modules.” The four modules are:

Module 1: Roles of Quantitative Methods in Public Health – Sessions 1–4
Module 2: Quantifying and Comparing Public Health Measures- Sessions 5–7
Module 3: Quantifying the Natural History of Disease- Sessions 8–9
Module 4: Probability Concepts and their Use in Evaluation of Diagnostic and Screening Tests – Sessions 10–11

PDF slides from the lectures are available; however, JHSPH did not make any audio or video available to go along with the slides. No mention of a final exam or other course assignments made in the OCW content.

Further Reading

Two texts were originally required for the course:

Gordis, Leon. (2004). Epidemiology. Third edition. Philadelphia: Elsevier Saunders.
Pagano, Marcello and Kimberlee Gauvreau. (2000). Principles of Biostatistics. Belmont, CA: Wadsworth.

Additional Resources

The instructors of this course are using WikiEducator to collect case studies to illustrate different types of epidemiologic investigations. The instructors are particularly interested in studies conducted in Western Asia and the Arabian Peninsula, and they are looking to OCW users to help them find the best examples.

The WikiEducator page includes previously submitted case studies which may be applicable to this course. Users are recommended to review the content in the scope of the course. You are also encouraged to submit a case study by editing the wiki page. Registration is required to submit a case study.

Fundamentals of Epidemiology II – JHSPH 550.695.81

This 2008 JHSPH OpenCourseWare graduate course follows in the footsteps of “Fundamentals of Epidemiology I”, continuing to examine the basic concepts of epidemiology and biostatistics as applied to public health problems. Emphasis is placed on the principles and methods of epidemiologic investigation, appropriate summaries and displays of data, and the use of classical statistical approaches to describe the health of populations.

Learning Objectives

After completion of this course, students will be able to apply principles of epidemiology and biostatistics to the prevention of disease and the improvement of health.

Objectives of the course include:

distinguishing the roles and relationships between epidemiology and biostatistics in the prevention of disease and the improvement of health.
computing basic descriptive statistics and explore data analytic methods.
demonstrating a basic understanding of epidemiologic methods and study design.
combining appropriate epidemiological concepts and statistical methods.

Course Organization

The content of this second of two courses is divided into thirteen lectures spread out over three “modules.” The three modules are:

Module 5: Epidemiologic Study Designs – Sessions 12–15
Module 6: Estimating Risk and Interpretation of Data from Epidemiologic Studies – Sessions 16–20
Module 7: Applying Epidemiology to Evaluation and Roles of Genetics, Public Policy and Epidemiology – Sessions 21–24

Note: Numbering continues on from the first part of this course.

Additionally, note that lecture slides from session 21–23 are missing and unavailable through JHSPH OpenCourseWare.

Further Reading

Two texts were originally required for the course:

Gordis, Leon. (2004). Epidemiology. Third edition. Philadelphia: Elsevier Saunders.
Pagano, Marcello and Kimberlee Gauvreau. (2000). Principles of Biostatistics. Belmont, CA: Wadsworth.

Additional Resources

Health Informatics Terminologies: An Introduction – HIBBs 1888

This 2011 HIBBs course module provides an introductory look at health informatics technologies. Specifically, the module will allow users who complete it to:

define what health informatics is and discuss its applications with different information communication terminologies.
understand the different types of common health Informatics terminologies.
identify the defined sub-domains of health informatics and applications for each sub-domain accordingly.

Course Organization

This represents a learning module and not so much a full course. The user may choose how they wish to view this material: audio-only, slides-only, slides with audio, and/or transcript of the audio.

Recommended Reading

No recommended reading is outlined for this learning module.

Health Information Systems to Improve Quality of Care in Resource-Poor Settings – MIT HST184 and S14

This 2011–2012 MIT OpenCourseWare course provides guidance on the development of information system innovations for developing countries that will:

* translate into improvement in health outcomes.

* strengthen the existing organizational infrastructure.

* create a collaborative ecosystem to maximize the value of these innovations.

Teaching students the science of improvement and scale is a strategy for capacity-building that has not been fully explored by current vertical programs that have focused on providing clinical skills to community health workers (CHWs).

Learning Objectives

The learning objectives for this course are:

1. Understand healthcare gaps and inefficiencies in developing countries and resource-poor settings.

2. Learn about strategies for improving the quality of care using information systems in resource-poor settings.

3. Project management and coordination skills on a multidisciplinary, cross-continental team.

4. Learn value-chain analysis, process re-engineering, design learning systems and quality improvement in the context of eHealth projects.

Course Organization

The course combines material from the Spring 2011 offering (course number HST.184) and Spring 2012 offering (course number HST.S14), sequenced in a way the instructors feel makes sense. The 2011 class emphasized lectures by guest speakers, whereas the 2012 class focused on case studies and mentored projects. The lecture videos are all from Spring 2011. No lecture videos were recorded for the Spring 2012 course.

The general layout is as follows:

* Module I: Setting the Stage for eHealth – Sessions 1–4

* Module II: Designing Health Information Systems – Sessions 5–11

* Module III: Creating a Culture of Quality in Health Care – Sessions 12–21

The course also included a four-stage project proposal and final paper:

* Initial Proposal – Session 6

* Interim Pitch & Outline – Session 12

* Final Presentation – Session 20

* Final Paper – After Session 21

Further Reading

Health Information Technology Standards and Systems Interoperability – JHSPH 315.708.81

This 2011 JHSPH OpenCourseWare graduate course provides health professionals with an understanding of the existing health information technology (HIT) standards and HIT standardization processes. The goal of this course is to provide students with methods and tools for participation as users in the HIT standardization activities for the design and evaluation of integrated health data systems at the local, state, regional, national, or international levels. The intended audience comprises public health and medical professionals responsible, or advocating, for information systems used in providing services; developing, implementing, and evaluating policies; and performing research.

Learning Objectives

Over the course of this educational session you’ll:

understand health information exchanges (HIEs) between clinical and public health/population health data systems.
understand the main categories of HIT standards.
understand the HIT standardization process.
learn the HIT standardization entities.
understand the role of users in HIT standardization.
learn how to participate in the design of information systems in public health.

At the end of the course, students should be able to:

understand the HIT standardization processes and entities.
participate as users in the HIT standardization activities.
develop a functional requirements specification document (functional standards) for the information system for a specific public health problem/domain.

Course Syllabus

This is the full original syllabus for this course: JHSPH 315.708.81 course syllabus

Course Organization

This course constitutes 14 lectures, a session 0 introduction, a session 14 student presentation, and a final assignment due a few weeks after the final lecture. PDFs of lecture notes and audio recordings are available for all of the lectures. At the end of each session one or more discussion questions are assigned for students to respond to, due by the next session.

Further Reading

Each session contains a mix of required and recommended reading. Consult each session for a listing of these readings.

Other Requirements

No additional requirements are associated with this course.

History of Public Health – JHSPH 550.605.81

This 2005 JHSPH OpenCourseWare graduate course examines the historical experience of health and illness from a population perspective. This material seeks to reveal how the organization of societies facilitates or mitigates the production and transmission of disease. It also investigates how populations and groups of individuals go about securing their health. One key theme is the medical management of space in one form or another – from the public space of the environment, through institutional spaces such as schools and workplaces, to personal/individual body space.

Learning Objectives

Upon completion of this course, you will be able to examine public health through its historical context and use this information in the evaluation of current public health issues.

Course Organization

The content of this course is divided into eight separate lectures held over a period of eight weeks. The lecture sections are presented sequentially and should be listened to in that order.

There will be two types of discussion sessions in this course: LiveTalks and Bulletin Board discussions. Both of these discussion sessions involved all the students and the original professor Dr. Mooney. The four Bulletin Board sessions revolved around discussion questions posed by Dr. Mooney.

MP3 files of the lectures are available; however, JHSPH did not make any lecture slides available to go along with the audio material.

Further Reading

Two texts were originally required for the course:

Ibsen, Henrik (1964). “A Public Enemy” in Ghosts and Other Plays, trans. Peter Watts, London: Penguin Books.
Porter, Dorothy (1999). Health, Civilization, and the State: A History of Public Health from Ancient to Modern Times.

Recommended reading was also assigned for every session. Consult the Recommended Reading associated with each session.

Additional Course Information

This document contains a description of each lecture, general reading for the course, and recommended reading for each session: History of Public Health – Additional Course Information

How to Create a New Course on LiMSForum.com

Progress:

You can create your own training courses on LiMSForum.com!

In this course, you will learn how to create your own full-featured LIMS courses. Specifically, you will learn how to do the following:

Create a course page with introduction content.
Develop lessons and topics with on-page content, embedded media, and intermediate quizzes.
Enhance your course with special features.

Take each of the following lessons to get the most from LiMSforum lab courses:

Information and Entropy – MIT 6.050J and 2.110J

This 2008 MIT OpenCourseWare course provides guidance on the ultimate limits to communication and computation, with an emphasis on the physical nature of information and information processing.

Learning Objectives

The learning objectives for this course are not clearly stated on the MIT OpenCourseWare site.

The site only mentions covered topics, including information and computation, digital signals, codes and compression, applications such as biological representations of information, logic circuits, computer architectures, algorithmic information, noise, probability, error correction, reversible and irreversible operations, physics of computation, and quantum computation.

Course Organization

Due to technical difficulties at MIT, not all the lecture videos for this course are available.

The course is based on 13 lectures, each associated with one or more chapters of the professor’s self-organized textbook. The course also includes problem sets assigned with each lecture, the ones associated with this course originating from 2003. Finally, a quiz during session nine and a final exam during session 14 are included.

Further Reading

The recommended reading for the course is the professor’s self-organized textbook, found here. Additional recommended reading is available from session to session and is included in each session section.

Other Requirements

No other requirements are stated for this course.

Information Exploration: Becoming a Savvy Scholar – MIT 3.093

This 2006 MIT OpenCourseWare course provides guidance on the scientific publishing cycle, finding and evaluating information, citing sources, and other aspects of research. The course is designed to introduce first year students to the scientific research process and provide them with the skill necessary to find, evaluate and use information successfully throughout their educational careers.

Learning Objectives

The learning objectives for this course include:

* gaining a better understanding of the research environment.

* becoming a more effective researcher.

* developing a scientific communication foundation.

Course Organization

The course is based on 13 lectures, each associated with an aspect of information retrieval, use, and citation. Six organized assignments are given out during the course, and research logs are maintained and graded by the professor as part of the assignments. Additionally, students are requested to review tutorial modules and answer questions associated with them.

The resources provided for this MIT OpenCourseWare course don’t align clearly with the schedule posted on the MIT OCW site. Therefore, the ordering of assignments, tutorial modules, and research logs and their due dates on this site may not align exactly as intended by the original professor.

No videos and audio recordings are associated with this course. This leaves some of the sessions bereft of value. An attempt was made to add a “Recommended Reading” session to each session to add value to those sessions.

Information about research logs

Research logs were associated with each assignment for this course. The purpose of these logs was to make you think about your search process as you record your steps, and for the instructors to get a sense of how the search process was improving over the semester. Research logs were to be e-mailed to the instructor before class began on the date the assignment was due.

The following elements were encouraged in each research log, using any format you wish (text, screen shots, charts, tables, etc.):

* the name(s) of the database(s), Web site(s), book(s), or other source(s) you used to find information for the assignment

* a description of why you specifically chose those sources for your information search, including justification

* a description of your search process, including the steps you took, the keywords you searched, and how you went about searching

* an explaination of the results you got from each source: did you find what the information you expected to find; were your results scholarly/academic in nature; and which results did you select and why?

* the amount of time you spent searching each source

* a description of your emotional response to the experience: were you frustrated, confused, confident, worried about time, etc.?

Sample research logs:

Sample 1 (PDF)
Sample 2 (PDF)

Information about tutorial module reviews

Module Review assignments were an important part of this course. The course instructors expected honest feedback on the modules, with intent to revise them for future students.

Each module review needed to include the following elements:

* a description of what you learned from each module: did you learn new information or did you already know that content?

* an explanation of if the module met your expectations of what you needed to learn, including why or why not

* an explanation of how you would ideally like to learn the content presented in the module: via an online module, via a lecture, or via another method (e.g. read it on a Web page).

* a description of how you liked the delivery: did the module move too slowly or too quickly; were the graphics interesting; and was the module interactive enough?

* feedback on the quiz at the end of the module: were the questions challenging enough, and were there too many or too few questions?

* answers to the following: were there things discussed in class that were not included in the module, and do you think it should be included in the module? If so, how?

* any additional feedback you have about the content, presentation, or delivery of the modules

Further Reading

Aside from the assignments and tutorial reviews, no recommended reading was originally associated with these sessions. Any recommended reading attached to sessions is additional to the original MIT OCW content.

Information Retrieval: Access to Knowledge-Based Resources – HIBBs 1806

This 2010 HIBBs course module provides an overview of information retrieval (IR) as applied to knowledge‐based resources in the biomedical and health domain. Specifically, the module will allow users who complete it to:

define the key terms of information retrieval.
understand the purposes and techniques used for indexing and retrieval.
be able to access bibliographic, full‐text, annotated, and aggregated resources of biomedical and health content.

Course Organization

This represents a learning module and not so much a full course. The user may choose how they wish to view this material: audio-only, slides-only, slides with audio, documents, and/or transcript of the audio.

Recommended Reading

No recommended reading is outlined for this learning module.

Information Theory – USU ECE7680

This 2006 Utah State University OpenCourseWare graduate course explores the fundamental limits of the representation and transmission of information. It focuses on the definition and implications of (information) entropy, the source coding theorem, and the channel coding theorem. These concepts provide a vital background for researchers in the areas of data compression, signal processing, controls, and pattern recognition.

This class is highly mathematical. The direct applications are, in a sense, only recently emerging, despite the nearly 50 year history. A firm determination and a fair degree of mathematical maturity will be required by students hoping to do well in the class. After all, how many engineering classes have you had which have the word “theory” in the title?

Course Organization

This course constitutes 14 lectures, originally offered twice per week. HTML and PDF versions of lecture notes are available for all of the lectures. No audio or video is associated with this course. Some sessions have a homework assignment, including reading, associated with them. The homework actually comes from the Spring 2000 class and not the 2006 class. Since the 2000 class had a different lecture layout, best judgement was made placing the homework assignments into the 2006 layout seen here.

A final class project is also associated with this course and described in the last session.

Further Reading

The following materials are listed as textbooks and supplementary reading for the course. The OCW often but not always makes it clear what chapters from which books were assigned for each session. In cases where it’s not clear, use your own judgement in selecting chapters/sections for each session.

Textbooks

Cover, Thomas M. and Thomas, Joy A. Elements of Information Theory. 2nd edition. John Wiley & Sons, 2012.

Mackay, David J.C. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2004.

Supplementary reading

Lucky, Robert W. Silicon Dreams: Information, Man, and Machine. St. Martin’s Press, 1991.

Moon, Todd K. and Stirling, Wynn C. Mathematical Methods and Algorithms for Signal Processing. Prentice Hall, 2000.

Proakis, John G. and Salehi, Masoud. Communications Systems Engineering. Prentice Hall, 1994.

Introduction to Bioinformatics – HIBBs 1900

This 2011 HIBBs course module provides an introductory look at bioinformatics. Specifically, the module will allow users who complete it to:

understand what the field of bioinformatics encompasses and why the field is important.
give examples of bioinformatics applications.
understand sequencing and the uses of sequencing.

Course Organization

This represents a learning module and not so much a full course. The user may choose how they wish to view this material: audio-only, slides-only, slides with video, streaming video, and/or downloadable video.

Recommended Reading

No recommended reading is outlined for this learning module.

Introduction to Biology – CMU 03-121

—–

This 2012 Carnegie Mellon University Open Learning Initiative course introduces biology and its relationship to other sciences. It examines the overarching theories of life from biological research and also explores the fundamental concepts and principles of the study of living organisms and their interaction with the environment. Students will examine how life is organized into hierarchical levels; how living organisms use and produce energy; how life grows, develops, and reproduces; how life responds to the environment to maintain internal stability; and how life evolves and adapts to the environment.

Learning Objectives

This course is a part of CMU OLI’s Community College (CC-OLI) series. Courses in this series are particularly well-suited to the needs of introductory community college courses, but are open for use by any instructor or student. By the end of the course, students should be able to explain fundamental concepts and principles of the study of living organisms and their interaction with the environment.

Course Organization

Information in this course is organized into 10 units and an appendix. Each unit begins with an introduction that orients you toward the major themes you’ll explore in that unit. The unit introduction will also show you how the content fits into the course as a whole. Each unit consists of several sessions. When you start a new session, you will see the list of learning outcomes you will achieve after completing that section of the course. The course organization is as such:

Unit 1: Biology: The Science of Life – Sessions 1–4
Unit 2: Introduction to Chemistry – Sessions 5–9
Unit 3: Biological Macromolecules – Sessions 10–15
Unit 4: The Cell – Sessions 16–19
Unit 5: Metabolism – Sessions 20–23
Unit 6: Cell Division – Sessions 24–27
Unit 7: Classical Genetics – Sessions 28–31
Unit 8: Molecular Genetics – Sessions 32–36
Unit 9: Evolution – Sessions 37–39
Unit 10: Ecology – Sessions 40–44
Appendix

If you wish to save your progress through the course at OLI, create an account and log in each time you wish to continue the course.

Further Reading

No textbook is associated with this course. However, the free online ChemPRIME wiki and Biology wiki may be useful in helping you further understand chemical and biological concepts within a broad range of contexts that relate to other disciplines and everyday life.

Other Requirements

Introduction to Clinical Laboratory Informatics – LII 006

This course provides an introduction to laboratory informatics in the clinical sphere, sometimes referred to as clinical informatics. The emphasis of this course will primarily be on learning the basics of how a clinical laboratory functions and how software is utilized to assist with those functions.

Topics include types of clinical labs, types of staff, regulation in the lab, operations, standards, equipment, and system requirements. Note that this course focuses on the basics characteristics of clinical labs and their technological needs and does not intend to be thorough in scope. In later courses, many of these topics will be elaborated on.

Learning Objectives

Upon completion of this course, you will be able to:

explain what separates a laboratory in the clinical setting from labs in other settings.
give examples of clinical labs and describe their staffing needs.
explain some of the regulations and standards that affect clinical laboratory operations.
describe the operations of a typical clinical laboratory.
detail the equipment and electronic systems used in a clinical laboratory.
explain the functional requirements for software operating in the clinical laboratory.

Course Organization

This course constitutes nine sections or sessions, each covering an important aspect of the clinical laboratory.

Additionally, a tenth session is included as a review of all the previously discussed content.

Further Reading

The following reading material is recommended for this course:

Gardner, Reed M.; Overhage J. Mark; Steen, Elaine B.; Holmes, John H.; Munger, Benson S.; Williamson, Jeffrey J.; Detmer, Don E. (2009). “Core content for the subspecialty of clinical informatics”. Journal of the American Medical Informatics Association 16 (2): 153–7.

Sittig, Dean F.; Ash, Joan S. (2009). “Chapter 1: Computer-Based Provider Order Entry: Placing the Application Into Perspective“. Clinical Information Systems: Overcoming Adverse Consequences. Sudbury, MD: Jones & Bartlett Learning.

Other Requirements

Students will best utilize this course via the desktop or laptop environment. While every effort has been made to make this course compatible with mobile platforms, they often provide challenges with embedded browser content like Adobe Flash videos and PDFs. This seems especially true in the Android environment.

Authors and Contributors

The authors of and contributors to this course are Shawn Douglas, Rebecca A. Fein, and Jason Smith.

Introduction to Information Studies – UMich SI110

This 2009 Open.Michigan course provides the foundational knowledge necessary to begin addressing the key issues associated with the Information Revolution. Issues will range from:

* the theoretical: e.g. What is information and how do humans construct it?

* the cultural: e.g. Is life on the screen a qualitatively different phenomenon from experiences with earlier distance-shrinking and knowledge-building technologies such as telephones?

* the practical: e.g. What are the basic architectures of computing and networks?

Learning Objectives

Successful completion of this “gateway” course will give you, the student, the conceptual tools necessary to understand the politics, economics, and culture of the Information Age, providing a foundation for later study in Information or any number of more traditional disciplines.

Course Organization

The course is based on 13 lectures, a mid-term review and exam (session eight), and a final exam (session fifteen). Recommended reading is assigned with most every lecture. Audio recordings of the lecture (MP3 and FLAC) and lecture slides (PDF and PPT) are also available for each session.

Course Syllabus

This is the original syllabus associated with this course. Alternatively, you can find it on iTunes.

Further Reading

Recommended reading was assigned for every session but eight and fifteen. Consult the Recommended Reading for each session. No course text was assigned.

Introduction to OpenEMR – LII 005

This LII course uses a variety of strategies and resources to assist learners with their desire to become more familiar with free open-source OpenEMR. This course is best utilized in conjunction with an accessible installation of OpenEMR. The online demo may be utilized to practice some of the tasks in this course, though it’s not clear if all activities can be conducted on the demo site.

Topics

Topics covered in this course include:

Getting started
Main screen & navigation
Setting up your clinic
Adding a new patient
Using the calendar
New encounters and coding
Issues & immunizations
Patient notes & transactions
Basic billing
Accounting and receivables
Reporting

Format

Each session has resources and activities that may be repeated until one feels comfortable moving on to the next session. However, even if no assignment is listed, log in to your copy of OpenEMR and practice working with the functions described. This will assist you with your learning objectives for the course.

Authors and Contributors

The author of this course is Rebecca A. Fein.

Introduction to Routine Health Information Systems – HIBBs 1889

This 2011 HIBBs course module provides an introduction to the concepts and methods of routine health information systems. Specifically, the module will allow users who complete it to:

explain the roles of routine health information systems (RHIS) in health service management.
examine strategies used to improve routine health information systems.
understand how to carry out the process of improving RHIS performance.
discuss three categories of determinants that influence RHIS.

Course Organization

This represents a learning module and not so much a full course. The user may choose how they wish to view this material: audio-only, slides-only, and/or slides with voiceover.

Recommended Reading

No recommended reading is outlined for this learning module.

Logic and Proofs – CMU 80-210

—–

This 2012 Carnegie Mellon University Open Learning Initiative course introduces the discipline of logic. It is deeply tied to mathematics and philosophy, as correctness of argumentation is particularly crucial for these abstract disciplines. Logic systematizes and analyzes steps in reasoning: correct steps guarantee the truth of their conclusion given the truth of their premise(s); incorrect steps allow the formulation of counterexamples, i.e., of situations in which the premises are true, but the conclusion is false.

Recognizing (and having conceptual tools for recognizing) the correctness or incorrectness of steps is crucial in order to critically evaluate arguments, not just in philosophy and mathematics, but also in ordinary life. This skill is honed by working in two virtual labs. In the ProofLab you learn to construct complex arguments in a strategically guided way, whereas in the TruthLab the emphasis is on finding counterexamples systematically.

This is an introductory course designed for students from a broad range of disciplines, from mathematics and computer science to drama and creative writing. The highly interactive presentation makes it possible for any student to master the material. Concise multimedia lectures introduce each chapter; they discuss, in detail, the central notions and techniques presented in the text, but also articulate and motivate the learning objectives for each chapter.

Course Organization

This course is made up of eight sessions, Each session features both review materials and homework assignments, including quizzes and lab problems. The end-of-chapter quizzes and practice questions provide fully automated feedback to the student; the ample practice lab problems offer tutoring, while the problems in the chapter’s lab assignment do not, providing students with the opportunity to demonstrate mastery of the skills developed in completing the practice problems.

The course also includes a Resources section containing further reading, settings check tools, and the ProofLab User’s Guide.

If you wish to save your progress through the course at OLI, create an account and log in each time you wish to continue the course.

Further Reading

No textbook is associated with this course. However, numerous free online wiki-based logic books may be useful in helping you further understand concepts of logic within a broad range of contexts that relate to other disciplines and everyday life.

Other Requirements

Class Resources

CMU includes several class resources for students at the bottom of its main course page. It includes…

Further Reading
Settings Check
ProofLab User’s Guide Quick Links

Management of Electronic Records – UMich SI655

This 2009 Open.Michigan graduate course examines the ways in which new information technologies challenge organizations’ capacities to define, identify, control, manage, and preserve electronic records.

Students learn how different organizational, technological, regulatory, and cultural factors affect the strategies, practices, and tools that organizations can employ to manage electronic records. Problems of long-term preservation and continuing access to electronic records are analyzed and addressed. The course also covers electronic records management issues in a wide variety of settings, including archives and manuscript repositories.

Learning Objectives

This course will provide students:

* familiarity with the role of electronic records in accountability and sensitivity to what can go wrong if recordkeeping systems are inadequate or fail.

* knowledge of the legal, administrative, and financial issues related to electronic records and recordkeeping.

* awareness of standards and best practices for creation, retention, authenticity, security, and accessibility of electronic records.

* familiarity with systems, technologies, and tools that support electronic records management, and knowledge of criteria to evaluate their effectiveness.

* an understanding of the institutional variables (e.g., corporate culture, business activities, and information technology environments) that affect the implementation of recordkeeping and accountability requirements.

* skills in evaluating information systems for compliance with recordkeeping and accountability requirements.

Course Organization

The course is based on 13 lectures. Recommended reading is assigned with most every lecture. Lecture slides (PDF and PPT) are available for each session; however, no videos or audio is associated with the sessions. Several assignments were also given out during the course of the semester.

Original Syllabys

This is the original syllabus associated with this course: Original syllabus for SI655

Further Reading

Recommended reading was assigned for every session. Consult the Recommended Reading for each session. No course text was assigned.

Mathematics for Computer Science – MIT 6.042J and 18.062J

This 2010 MIT OpenCourseWare course covers elementary discrete mathematics for computer science and engineering.

Mathematics for Computer Science emphasizes mathematical definitions and proofs as well as applicable methods. Topics include formal logic notation, proof methods; induction, well-ordering; sets, relations; elementary graph theory; integer congruences; asymptotic notation and growth of functions; permutations and combinations, counting principles; discrete probability. Further selected topics may also be covered, such as recursive definition and structural induction; state machines and invariants; recurrences; and generating functions.

Learning Objectives

Students finishing this course should be able to apply concepts like number theory, graph theory, recurrences, probabilities, and expectations to their studies in computer science and computer engineering.

Course Organization

The course is based on 25 sessions, a midterm exam (session 15), and a final exam (session 27). A video and readings are associated with almost all the lectures.

In addition to lectures, this course also had recitations; each recitation had its own set of notes, problems, and their solutions. The course also includes 12 problem sets assigned throughout various sessions, typically on every other session.

Further Reading

The textbook associated with this course:

Lehman, Eric; Leighton, F. Thomson; Meyer, Albert R. Mathematics for Computer Science. Massachusetts Institute of Technology, September 8, 2010. (PDF)

Other Requirements

The prerequisite for this course is Single Variable Calculus – MIT 18.01.

MediaWiki Basics – LII 001

This LIMSwiki-based course provides an introduction to MediaWiki software. The emphasis is on how to quickly learn the editing and formatting tools — the wiki markup — of the software so you can quickly get started on making meaningful contributions to the wiki.

Learning Objectives

Upon completion of this course, you will be able to:

explain how wikis in general and MediaWiki in particular can be used by individuals and organizations.
create MediaWiki pages with links and other basic text formatting.
utilize the User and other namespaces in a MediaWiki installation.
add tables and multimedia to a MediaWiki page.
use templates, citations, and categories in a MediaWiki page.

Course Organization

The course provides an introduction to MediaWiki and wikis in general, followed by beginner, intermediate, and advanced editing concepts. A brief review of all the covered topics is also included, for a total of five sessions.

Editing activities are included with each session, thus requiring an account on a MediaWiki installation. If you don’t have a LIMSwiki account and wish to contribute to the site, you can request an account. However, the activities in this course could easily be applied on your own wiki or even on Wikipedia, as the activities all take place in a user sandbox you create.

The activities are included both at the bottom of each linked LIMSwiki page and as a separate activity file. If you’re not using LIMSwiki to complete the activities, ignore the final step of leaving a comment on the indicated user talk page. Instead, find a friend knowledgeable with MediaWiki to verify you’ve completed the activity properly.

Further Reading

No recommended reading material is assigned for this course.

Other Requirements

Methods in Biostatistics I – JHSPH 140.651.01

This 2006 JHSPH OpenCourseWare graduate course is the first half of a course presenting fundamental concepts in applied probability, exploratory data analysis, and statistical inference, focusing on probability and analysis of one and two samples.

Topics include discrete and continuous probability models; expectation and variance; central limit theorem; inference, including hypothesis testing and confidence for means, proportions, and counts; maximum likelihood estimation; sample size determinations; elementary non-parametric methods; graphical displays; and data transformations.

Learning Objectives

Over the course of this educational session you’ll:

1) reacquaint yourself with the mathematical, computational, statistical, and probability background needed to complete the course.
2) be introduced to the display and communication of statistical data. This will include graphical and exploratory data analysis using tools like scatterplots, boxplots, and the display of multivariate data. In this objective, students will be required to write extensively.
3) learn the distinctions between the fundamental paradigms underlying statistical methodology.
4) learn the basics of maximum likelihood.
5) learn the basics of frequentist methods: hypothesis testing, confidence intervals.
6) learn basic Bayesian techniques, interpretation and prior specification.
7) learn the creation and interpretation of P values.
8) learn estimation, testing, and interpretation for single group summaries such as means, medians, variances, correlations, and rates.
9) learn estimation, testing, and interpretation for two group comparisons such as odds ratios, relative risks, and risk differences.
10) learn the basic concepts of ANOVA.

Course Organization

This course constitutes 14 lectures. PDFs of lecture notes are available for all of the lectures. However, no homework, video, or audio files are associated. A final exam was likely given for this course, but it is not available through OCW.

Further Reading

The following texts are listed as required or recommended for the course:

Required

Rosner, Bernard. Fundamentals of Biostatistics. 6th edition, 2006.

This is a practical introduction to the methods, techniques, and computation of statistics with human subjects. It prepares students for their future courses and careers by introducing the statistical methods most often used in medical literature. Rosner minimizes the amount of mathematical formulation (algebra-based) while still giving complete explanations of all the important concepts. As in previous editions, a major strength of this book is that every new concept is developed systematically through completely worked out examples from current medical research problems. (Chapters 1–3 found here as a PDF.)

The Rosner text is given as assigned reading with most lectures. See each session for what was assigned.

Recommended

Rice, John A. Mathematical Statistics and Data Analysis. 2nd edition, 1995. (PDF)

This is the first text in a generation to re-examine the purpose of the mathematical statistics course. The book’s approach interweaves traditional topics with data analysis and reflects the use of the computer with close ties to the practice of statistics. The author stresses analysis of data, examines real problems with real data, and motivates the theory. The book’s descriptive statistics, graphical displays, and realistic applications stand in strong contrast to traditional texts which are set in abstract settings.

Venables, WN; Ripley, BD. Modern Applied Statistics with S. 4th edition, 2002. (PDF)

This book is a guide to using S-PLUS to perform statistical analyses and provides both an introduction to the use of S-PLUS and a course in modern statistical methods. S-PLUS is available for both Windows and UNIX workstations, and both versions are covered in depth. The aim of the book is to show how to use S-PLUS as a powerful and graphical data analysis system. Readers are assumed to have a basic grounding in statistics; as such the book in intended for would-be users of S-PLUS and both students and researchers using statistics. Throughout, the emphasis is on presenting practical problems and full analyses of real data sets.

McDavid, James C. Program Evaluation and Performance Measurement: An Introduction to Practice. Thousand Oaks, CA: SAGE Publications, 2005.
Marshall, Elliot. Flawed Statistics in Murder Trial May Cost Expert His Medical License. Science 2005; 309:543.
Wainer, Howard. How to Display Data Badly. The American Statistician 1984; 38:137-147. (PDF)

Other Requirements

Calculus, linear algebra, and a moderate level of mathematical literacy are prerequisites for this class. Note that simply having the prerequisites for this class does not necessarily mean that it is the correct class for you.

Additional Course Resources

The following resources may be useful to you as you progress throughout the course:

» R for Windows and Mac: R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. To download R, please choose your preferred CRAN mirror.

» WinEdt: WinEdt (shareware) is a ASCII editor and shell for MS Windows with a strong predisposition towards the creation of [La]TeX documents.

» John Verzani’s Notes: simpleR – Using R for Introductory Statistics (PDF)

» J.H. Maindonald’s Notes: Using R for Data Analysis and Graphics: Introduction, Code and Commentary (PDF)

» Julian J. Faraway’s Notes: Practical Regression and ANOVA using R (PDF)

» Patrick Burns’ Notes: A Guide for the Unwilling S User (PDF)

» Jonathan Baron’s R Reference Card (PDF)

» Tom Short’s R Reference Card (PDF)

» Karl Broman’s R Site (Archived)

Methods in Biostatistics II – JHSPH 140.652.01

This 2006 JHSPH OpenCourseWare graduate course is the second half of a course presenting fundamental concepts in applied probability, exploratory data analysis, and statistical inference, focusing on probability and analysis of one and two samples.

Learning Objectives

Over the course of this educational session you’ll:

Course Organization

This course constitutes 14 lectures. Note the lecture numbers continue from the first course as lectures 15–28. PDFs of lecture notes are available for all of the lectures. Additional handouts for a few lectures are also available. However, no homework, video, or audio files are associated. A final exam was likely given for this course, but it is not available through OCW.

Further Reading

The following texts are listed as required or recommended for the course:

Required

Rosner, Bernard. Fundamentals of Biostatistics. 6th edition, 2006.

The Rosner text is given as assigned reading with most lectures. See each session for what was assigned.

Recommended

Rice, John A. Mathematical Statistics and Data Analysis. 2nd edition, 1995. (PDF)

Venables, WN; Ripley, BD. Modern Applied Statistics with S. 4th edition, 2002. (PDF)

McDavid, James C. Program Evaluation and Performance Measurement: An Introduction to Practice. Thousand Oaks, CA: SAGE Publications, 2005.
Marshall, Elliot. Flawed Statistics in Murder Trial May Cost Expert His Medical License. Science 2005; 309:543.
Wainer, Howard. How to Display Data Badly. The American Statistician 1984; 38:137-147. (PDF)

Other Requirements

Additional Course Resources

The following resources may be useful to you as you progress throughout the course:

» WinEdt: WinEdt (shareware) is a ASCII editor and shell for MS Windows with a strong predisposition towards the creation of [La]TeX documents.

» John Verzani’s Notes: simpleR – Using R for Introductory Statistics (PDF)

» J.H. Maindonald’s Notes: Using R for Data Analysis and Graphics: Introduction, Code and Commentary (PDF)

» Julian J. Faraway’s Notes: Practical Regression and ANOVA using R (PDF)

» Patrick Burns’ Notes: A Guide for the Unwilling S User (PDF)

» Jonathan Baron’s R Reference Card (PDF)

» Tom Short’s R Reference Card (PDF)

» Karl Broman’s R Site (Archived)

Principles of Chemical Science – MIT 5.111

This 2008 MIT OpenCourseWare course provides an introduction to the chemistry of biological, inorganic, and organic molecules. The emphasis is on basic principles of atomic and molecular electronic structure, thermodynamics, acid-base and redox equilibria, chemical kinetics, and catalysis.

In an effort to illuminate connections between chemistry and biology and spark students’ excitement for chemistry, MIT incorporated frequent biology-related examples into the lectures. A list of the biology-, medicine-, and MIT research-related examples used in 5.111 is provided in the course resources as “Biology supplement”. Not all lectures incorporated supplemental biology material.

The textbook associated with this course:

* Atkins, Peter, and Loretta Jones. Chemical Principles: The Quest for Insight. 4th ed. New York, NY: W.H. Freeman and Company, 2007. ISBN: 9781429209656.

Principles of Computing – CMU 15-110

—–

This 2012 Carnegie Mellon University Open Learning Initiative course introduces elementary principles of computing, including iteration, recursion, and binary representation of data. Additional topics on cellular automata, encryption, and the limits of computation are also introduced. The goal of this course is to introduce some of the techniques used in computer science to solve complex problems, with or without a computer. This course does not include a programming component, although the principles that are taught can be used in a programming context.

Learning Objectives

The main goal of this course is to teach the fundamental principles used in computer science to a general audience so that they understand how computer scientists use these principles to solve complex problems to improve their daily lives. This OLI course does not include programming although it can be used to supplement an introductory course in programming. In that situation, this course will show students that there is much more to computer science than learning to write code.

Course Organization

Information in this course is organized into 10 sessions and an introduction. The first three topics are traditionally some of the harder topics to comprehend for students who tend to shy away from technical courses, especially in computing. The next three topics were chosen because they illustrate some deeper discoveries in computing that will show how computational thinking affects our lives. The final four topics cover more of the nuts and bolts of practical computing.

If you wish to save your progress through the course at OLI, create an account and log in each time you wish to continue the course.

Note: The course introduction gives an overview of the the course. The OLI course description mentions six sessions; however, it has been updated to 10 sessions. The course descriptions have not been updated to recognize that.

Further Reading

No textbook is associated with this course. However, the free online Introduction to Computer Science wiki and Introduction to Computers wiki may be useful in helping you further understand computing concepts within a broad range of contexts that relate to other disciplines and everyday life.

Other Requirements

Probability & Statistics – CMU 36-201

—–

This 2011 Carnegie Mellon University Open Learning Initiative course introduces students to the basic concepts and logic of statistical reasoning and gives the students introductory-level practical ability to choose, generate, and properly interpret appropriate descriptive and inferential methods. In addition, the course helps students gain an appreciation for the diverse applications of statistics and its relevance to their lives and fields of study. The course does not assume any prior knowledge in statistics and its only prerequisite is basic algebra.

The course includes a classical treatment of probability and includes basic probability principles, conditional probability, discrete random variables (including the Binomial distribution) and continuous random variables (with emphasis on the normal distribution). The probability section culminates in a discussion of sampling distributions that is grounded in simulation.

Learning Objectives

The course is built around a series of carefully devised learning objectives that are independently assessed. Most of the interactive tutors are tagged by learning objective and skill, and so student work can be tracked by the system and reported to the instructor via the Learning Dashboard. These give the instructor insight into mastery of learning objectives and skills, both for the class as a whole and for individual students.

The course was designed to be used as a stand alone (with no instruction in the background) however studies have shown that it is best and most effectively used in the hybrid mode together with face to face instruction.

Course Organization

The structure of this entire course is based on 14 sessions spread out over four units; one for each of the steps in “the big picture.” As the figure below shows, we will start this course with EDA (even though it is second in the process of statistics), continue to discuss producing data, then go on to probability, so that at the end we’ll be able to discuss inference. The following figure summarizes the structure of the course:

If you wish to save your progress through the course at OLI, create an account and log in each time you wish to continue the course.

Further Reading

No textbook is associated with this course. However, it seems much of the material is based off the following text, which may be useful in expanding on the existing course material:

DeGroot, Morris H. and Schervish, Mark J. Probability and Statistics. 4th edition. Addison-Wesley, 2010.

Class Resources

CMU the original course overview and details about the course are available at the main course page.

Other Requirements

To do the activities, you will need your own copy of Microsoft Excel, Minitab, the open source R software (free), TI calculator, or StatCrunch. You will also need to have Flash, Java, and mathml installed; these programs are free. More detailed information is provided when you enter the course, found under “Test and Configure Your System.”

Single Variable Calculus – MIT 18.01

This 2006 MIT OpenCourseWare course covers differentiation and integration of functions of one variable, with applications.

The basic objective of calculus is to relate small-scale (differential) quantities to large-scale (integrated) quantities. This is accomplished by means of the Fundamental Theorem of Calculus. Students taking this course should gain an understanding of the integral as a cumulative sum, of the derivative as a rate of change, and of the inverse relationship between integration and differentiation.

Learning Objectives

Students finishing this course should be able to complete the following learning objectives:

Use both the definition of derivative as a limit and the rules of differentiation to differentiate functions.
Sketch the graph of a function using asymptotes, critical points, and the derivative test for increasing/decreasing and concavity properties.
Set up max/min problems and use differentiation to solve them.
Set up related rates problems and use differentiation to solve them.
Evaluate integrals by using the Fundamental Theorem of Calculus.
Apply integration to compute areas and volumes by slicing, volumes of revolution, arclength, and surface areas of revolution.
Evaluate integrals using techniques of integration, such as substitution, inverse substitution, partial fractions and integration by parts.
Set up and solve first order differential equations using separation of variables.
Use L’Hospital’s rule.
Determine convergence/divergence of improper integrals, and evaluate convergent improper integrals.
Estimate and compare series and integrals to determine convergence.
Find the Taylor series expansion of a function near a point, with emphasis on the first two or three terms.

Course Organization

The course is based on 40 sessions spread out over four units. A video and lecture notes are associated with almost all the lectures.

The course also includes eight problem sets assigned throughout various sessions. Problem sets have two parts: I and II. Part I consists of exercises given in the course reader and solved in section S of the course reader. Part II consists of problems for which solutions are not given. Some of these problems are longer multi-part exercises posed here because they do not fit conveniently into an exam or short-answer format.

Finally, four exams are included, typically at the end of each unit, with the fourth exam (session 34) being the exception. A final exam is given in session 40.

The general layout is as follows:

Unit 1: Derivatives – Sessions 1–8
Unit 2: Applications of Differentiation – Sessions 9–17
Unit 3: Integration – Sessions 18–26
Unit 4: Techniques of Integration – Sessions 27–40

Further Reading

The textbook associated with this course:

Simmons, George F. Calculus with Analytic Geometry. 2nd ed. New York, NY: McGraw-Hill, October 1, 1996. ISBN: 9780070576421.

In addition to the textbook, a “course reader” is included in the required reading, based on Supplementary Notes, Exercises and Solutions from Prof. David Jerison and Prof. Arthur Mattuck’s Calculus 1 course. Links to the PDFs will be included in each associated session.

Other Requirements

The prerequisites for this course are high school algebra and trigonometry.

Additionally, MIT lists a lengthy set of resources for use before and during the course. At a minimum “Tips for Success in Undergraduate Math Courses” and “Common Errors in Undergraduate Mathematics” are recommended reading before taking this course.

Statistical Methods for Sample Surveys – JHSPH 140.640.01

This 2009 JHSPH OpenCourseWare graduate course presents construction of sampling frames, area sampling, methods of estimation, stratified sampling, subsampling, and sampling methods for surveys of human populations. Students use STATA or another comparable package to implement designs and analyses of survey data.

Learning Objectives

Upon completion of this course, you will be able to:

design and implement surveys with the following sampling designs: simple random, systematic, stratified, cluster, and multistage.
estimate sample size for different sampling designs in order to estimate population level point estimates and testing null hypothesis.
explain and apply intraclass correlation and design-effects (DEFF) for complex surveys.
estimate design weights and adjust for non-response.

Course Organization

This course constitutes seven lectures and a project review during the eighth session. PDFs of lecture notes are available for all of the lectures. Labs were originally associated with this course, but no information exists about them for this OpenCourseWare course. No video or audio files are associated. A final project was given for this course, but specific details were limited.

Further Reading

The following texts are listed as required or recommended for the course:

Required:

United Nations Department of Economic and Social Affairs. Designing Household Survey Samples: Practical Guidelines. United Nations, 2005.

Recommended:

Levy, Paul S. and Stanley Lemeshow. Sampling of Populations: Methods and Applications. 4th edition. John Wiley & Sons, 2013.
Lohr, Sharon L. Sampling: Design and Analysis. 2nd edition. Cengage Learning, 2010.

No particular chapters or sections were linked with each session. Use your best judgement in matching content with sessions.

Other Requirements

Methods in Biostatistics II was originally a requirement for taking this class. STATA or another comparable package is required to implement designs and analyses of survey data.

Statistical Reasoning in Public Health I – JHSPH 140.611.81

This 2009 JHSPH OpenCourseWare graduate course is the first half of a course providing a broad overview of biostatistical methods and concepts used in the public health sciences, emphasizing interpretation and concepts rather than calculations or mathematical details. It develops ability to read the scientific literature, to critically evaluate study designs and methods of data analysis, and it introduces basic concepts of statistical inference, including hypothesis testing, p-values, and confidence intervals.

Topics include comparisons of means and proportions; the normal distribution; regression and correlation; confounding; concepts of study design, including randomization, sample size, and power considerations; logistic regression; and an overview of some methods in survival analysis. The course draws examples of the use and abuse of statistical methods from the current biomedical literature.

Learning Objectives

Upon completion of this course, you will be able to:

understand and give examples of different types of data arising in public health studies.
interpret differences in data distributions via visual displays.
calculate standard normal scores and resulting probabilities.
calculate and interpret confidence intervals for population means and proportions.
interpret and explain a p-value.
perform a two-sample t-test and interpret the results; calculate a 95% confidence interval for the difference in population means.
use Stata to perform two sample comparisons of means and create confidence intervals for the population mean differences.
select an appropriate test for comparing two populations on a continuous measure, when the two sample t-test is not appropriate.
understand and interpret results from Analysis of Variance (ANOVA), a technique used to compare means amongst more than two independent populations.
choose an appropriate method for comparing proportions between two groups; construct a 95% confidence interval for the difference in population proportions.
use Stata to compare proportions amongst two independent populations.
understand and interpret relative risks and odds ratios when comparing two populations.
understand why survival (timed to event) data requires its own type of analysis techniques.
construct a Kaplan-Meier estimate of the survival function that describes the “survival experience” of a cohort of subjects.
interpret the result of a log-rank test in the context of comparing the “survival experience” of multiple cohorts.
interpret output from the statistical software package Stata related to the various estimation and hypothesis testing procedures covered in the course.

Course Organization

The content of this first of two courses is divided into eight lectures spread out over four “modules.” The four modules are:

Module 1: Describing Data – Sessions 1 and 2
Module 2: Confidence Intervals – Session 3
Module 3: Comparing Two Groups and Hypothesis Testing – Sessions 4–7
Module 4: Introduction to Survival Analysis – Session 8

PDF slides and audio recordings from lectures are available for each session. Additionally, practice problems and homework assignments were fortunately included with the associated OpenCourseWare course. A final exam was given for this course, but it is not available through OCW.

Further Reading

No text was originally required for this course. However, several books were listed as “useful, but optional”:

* Altman, D. G. (1990). Practical Statistics for Medical Research. Boca Raton, Florida: CRC Press.

* Freedman, D., R. Pisani, and R. Purves. (2007). Statistics. 4th Edition. New York: W. W. Norton & Company, Inc.

* Moore, D., G. McCabe, and B. Craig. (2012). Introduction to the Practice of Statistics. 7th Edition. New York: W. W. Norton & Company, Inc.

Recommended reading was assigned with most sessions, always from Practical Statistics for Medical Research. Consult the Recommended Reading associated with each session.

Other Requirements

Students are also required to have access to Small Stata, a version of Stata that is less powerful (in terms of the amount of data it can store and process, not in terms of functionality) than regular Intercooled Stata, and costs significantly less. Small Stata carries a one-year users license. However, if you intend to further your study of statistics beyond this course, you may wish to purchase a copy of Intercooled Stata 8.

Additional Course Resources

This flowchart should aid in choosing a correct statistical procedure during this course: Choosing a Correct Statistical Procedure

This document provides more information on performing a paired t-test in Stata: Paired T-test in Stata

Statistical Reasoning in Public Health II – JHSPH 140.612.81

This 2009 JHSPH OpenCourseWare graduate course is the second half of a course providing a broad overview of biostatistical methods and concepts used in the public health sciences, emphasizing interpretation and concepts rather than calculations or mathematical details. It develops ability to read the scientific literature, to critically evaluate study designs and methods of data analysis, and it introduces basic concepts of statistical inference, including hypothesis testing, p-values, and confidence intervals.

Learning Objectives

Upon completion of this course, you will be able to:

recognize different study designs and understand the pros and cons of each.
learn methods for randomly assigning subjects to two groups.
understand the concepts of confounding and statistical interaction; know how to recognize each.
explain the relationship between power and sample size; use Stata to perform sample size calculations.
create a scatterplot to visually assess the nature of an association between two continuous variables.
interpret the calculated values of the correlation coefficient and the coefficient of determination, and understand the relationship between these two measures of association.
perform a simple linear regression using Stata, and use the results to assess the magnitude and significance of the relationship between a continuous outcome variable and a continuous predictor variable, and for predicting values of the outcome variable.
Understand why multiple regression techniques allow for the analysis of the relationship between an outcome and a predictor in the presence of confounding variables.
perform a multiple linear regression using Stata, and use the results to assess the magnitude and significance of the relationship between a continuous outcome variable and multiple continuous and categorical predictor variables, and for predicting values of the outcome variable.
perform a multiple logistic regression using Stata, and use the results to assess the magnitude and significance of the relationship between a dichotomous outcome variable and multiple continuous and categorical predictor variables.
interpret the results from a proportional hazards regression model.

Course Organization

The content of this second of two courses is divided into eleven lectures spread out over four “modules.” The four modules are:

Module 1: Issues in Design Study – Sessions 1–3
Module 2: Linear Regression – Sessions 4–6
Module 3: Logistic Regression – Sessions 7–9
Module 4: Survival Analysis – Sessions 10 and 11

Further Reading

No text was originally required for this course. However, several books were listed as “useful, but optional”:

* Altman, D. G. (1990). Practical Statistics for Medical Research. Boca Raton, Florida: CRC Press.

* Freedman, D., R. Pisani, and R. Purves. (2007). Statistics. 4th Edition. New York: W. W. Norton & Company, Inc.

* Moore, D., G. McCabe, and B. Craig. (2012). Introduction to the Practice of Statistics. 7th Edition. New York: W. W. Norton & Company, Inc.

Recommended reading was assigned with some sessions, in all cases from Practical Statistics for Medical Research. Consult the Recommended Reading associated with each session.

Other Requirements

Additional Course Resources

This flowchart should aid in choosing a correct statistical procedure during this course: Choosing a Correct Statistical Procedure

This document provides more information on performing a paired t-test in Stata: Paired T-test in Stata

Statistics for Laboratory Scientists I – JHSPH 140.615

This 2006 JHSPH OpenCourseWare graduate course:

introduces the basic concepts and methods of statistics with applications in the experimental biological sciences.
demonstrates methods of exploring, organizing, and presenting data, and it introduces the fundamentals of probability.
presents the foundations of statistical inference, including the concepts of parameters and estimates and the use of the likelihood function, confidence intervals, and hypothesis tests.
introduces and employs the freely-available statistical software, R, to explore and analyze data.

Topics include experimental design, linear regression, the analysis of two-way tables, sample size and power calculations, and a selection of the following: permutation tests, the bootstrap, survival analysis, longitudinal data analysis, nonlinear regression, and logistic regression.

Learning Objectives

Upon completion of this course, you will be able to:

create graphical displays of data.
understand basic experimental design.
understand basic probability.
implement confidence intervals and tests of hypotheses.

Course Organization

This course constitutes 23 lectures and a final exam during the 24th session. PDFs of lecture notes are available for many but not all of the lectures. Additionally, homework assignments were fortunately included with many of the lectures associated with this OpenCourseWare course. However, no video or audio files are associated. A final exam was given for this course, but it is not available through OCW.

Further Reading

The following texts are listed as required or recommended for the course:

Required:

Samuels, Myra L., Jeffrey A. Witmer, and Andrew A. Schaffner. (2012). Statistics for the Life Sciences. 4th revised edition, Pearson Education.

Recommended:

Gonick, Larry. (1993). Cartoon Guide to Statistics. HarperCollins.
Dalgaard, Peter. (2008). Introductory Statistics with R. Springer.

No particular chapters were linked with each session. Use your best judgement in matching chapters with sessions.

Other Requirements

Students are also required to have access to a scientific calculator with the ability to handle logarithms, exponents, trigonometric functions, simple memory and recall, and factorials. Additionally, the freely available statistical software, R, will be necessary. Consult the following URLs and files associated with them:

This leads to the R Project for Statistical Computing, where you can download and learn about R: R for Windows

This PDF contains class notes on how to use R effectively: Notes on Using R

Statistics for Laboratory Scientists II – JHSPH 140.616

This second of two 2006 JHSPH OpenCourseWare graduate courses:

introduces the basic concepts and methods of statistics with applications in the experimental biological sciences.
demonstrates methods of exploring, organizing, and presenting data, and it introduces the fundamentals of probability.
presents the foundations of statistical inference, including the concepts of parameters and estimates and the use of the likelihood function, confidence intervals, and hypothesis tests.
introduces and employs the freely-available statistical software, R, to explore and analyze data.

Learning Objectives

Upon completion of this course, you will be able to:

utilize tests for goodness of fit.
understand contingency tables.
analyze for variance.
understand advanced concepts of multiple comparisons.
understand linear regression.
utilize advanced methods of experimental design.

Course Organization

This course constitutes 21 lectures. PDFs of lecture notes are available for many but not all of the lectures. Additionally, a few homework assignments were included for sessions 1–4 and session 6. However, no video or audio files are associated. A final project was originally assigned, assumably due after session 21.

Further Reading

The following texts are listed as required or recommended for the course:

Required:

Samuels, Myra L., Jeffrey A. Witmer, and Andrew A. Schaffner. (2012). Statistics for the Life Sciences. 4th revised edition, Pearson Education.

Recommended:

Gonick, Larry. (1993). Cartoon Guide to Statistics. HarperCollins.
Dalgaard, Peter. (2008). Introductory Statistics with R. Springer.

No particular chapters were linked with each session. Use your best judgement in matching chapters with sessions.

Other Requirements

This leads to the R Project for Statistical Computing, where you can download and learn about R: R for Windows

This PDF contains class notes on how to use R effectively: Notes on Using R

Understanding Records and Archives: Principles and Practices – UMich SI580

This 2009 Open.Michigan graduate course presents cornerstone terminology, concepts, and practices used in records management and archival administration. It also examines the evolution of methods and technologies used to create, store, organize, and preserve records, It also addresses the ways in which organizations and individuals use archives and records for ongoing operations, accountability, research, litigation, and organizational memory.

Learning Objectives

This course will train students to:

* understand why societies, cultures, organizations, and individuals create and keep records and archives.

* become familiar with the evolution of methods and technologies used to create, store, organize, and preserve records and archives.

* become conversant in the terminology and concepts used in archives and records administration.

* be aware of the ways that organizations and individuals use records and archives for research, ongoing operations, accountability, litigation, and organizational memory.

* understand the basic components of programs including inventory, classification, appraisal, disposition, acquisition, arrangement, description, preservation, reference, access, use, outreach, and public programming.

* understand the relationships between these program elements.

* be aware of the various environments where archives and records are created, managed, and used.

* understand how archival and recordkeeping practices differ from and relate to other information management practices.

* be aware of the legal, policy, and ethical issues surrounding archives and records administration.

* become familiar with the structure, organization, literature, and current issues in the archives and records professions.

Course Organization

This course combines lecture, discussion, demonstrations, and problem solving. It requires independent research and writing. Critical reading of course materials is essential to stimulate active participation and learning of the material.

The course is based on 13 lectures, a final project, and a final exam. Recommended reading is assigned with most every lecture. Lecture slides (PDF and PPT) are available for each session; however, no videos or audio is associated with the sessions. Several assignments were also given out during the course of the semester.

Original Syllabus

This is the original syllabus associated with this course: Original course syllabus for SI580

Further Reading

Two texts were originally required for the course:

O’Toole, James M. and Richard J. Cox. Understanding Archives and Manuscripts. (Chicago: Society of American Archivists, 2006).
Kurtz, Michael J. Managing Archival and Manuscript Repositories. (Chicago: Society of American Archivists, 2004).

Recommended reading was also assigned for every session. Consult the Recommended Reading associated with each session.

The Society of American Archivists has posted an online glossary that will also prove useful to you during this course.