Learn about research methods academic fields, medicine, industry, and more. Take these online research courses to learn how to do research today!
Data Analysis:
1. Data Science: R Basics- Harvard University
This course will introduce you to the basics of R programming and is first on the list of online research courses. You can better retain R when you learn it to solve a specific problem, so you’ll use a real-world dataset about crime in the United States. You will learn the R skills needed to answer essential questions about differences in crime across the different states. You’ll learn how to apply general programming features like “if-else,” and “for loop” commands, and how to wrangle, analyze and visualize data.
Rather than covering every R skill you might need, you’ll build a strong foundation to prepare you for the more in-depth courses later in the series, where we cover concepts like probability, inference, regression, and machine learning. The demand for skilled data science practitioners is rapidly growing, and this series prepares you to tackle real-world data analysis challenges.
Course Highlights:
- Basic R syntax
- Foundational R programming concepts such as data types, vectors arithmetic, and indexing
- How to perform operations in R including sorting, data wrangling using dplyr, and making plots
Course Duration: 8 weeks (1-2 hours/week)
2. Statistics and R- Harvard University
This course teaches the R programming language in the context of statistical data and statistical analysis in the life sciences. We will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R code. We provide R programming examples in a way that will help make the connection between concepts and implementation.
Problem sets requiring R programming will be used to test understanding and ability to implement basic data analyses. We will use visualization techniques to explore new data sets and determine the most appropriate approach. We will describe robust statistical techniques as alternatives when data do not fit the assumptions required by the standard approaches. By using R scripts to analyze data, you will learn the basics of conducting reproducible research.
Course Highlights:
- Random variables
- Distributions
- Inference: p-values and confidence intervals
- Exploratory Data Analysis
- Non-parametric statistics
Course Duration: 4 weeks (2-4 hours/week)
3. Data Science: Data Wrangling- Harvard University
This course covers several standard steps of the data wrangling process like importing data into R, tidying data, string processing, HTML parsing, working with dates and times, and text mining. Rarely are all these wrangling steps necessary in a single analysis, but a data scientist will likely face them all at some point. Very rarely is data easily accessible in a data science project. It’s more likely for the data to be in a file, a database, or extracted from documents such as web pages, tweets, or PDFs. In these cases, the first step is to import the data into R and tidy the data, using the tidyverse package. The steps that convert data from its raw form to the tidy form are called data wrangling.
Course Highlights:
- Importing data into R from different file formats
- Web scraping
- How to tidy data using the tidy verse to better facilitate analysis
- String processing with regular expressions (regex)
- Wrangling data using dplyr
- How to work with dates and times as file formats
- Text mining
Course Duration: 8 weeks (1-2 hours/week)
4. Data Science: Probability- Harvard University
In this course, part of our Professional Certificate Program in Data Science, you will learn valuable concepts in probability theory. The motivation for this course is the circumstances surrounding the financial crisis of 2007-2008. Part of what caused this financial crisis was that the risk of some securities sold by financial institutions was underestimated. To begin to understand this very complicated event, we need to understand the basics of probability.
Probability theory is the mathematical foundation of statistical inference which is indispensable for analyzing data affected by chance, and thus essential for data scientists.
Course Highlights:
- Important concepts in probability theory including random variables and independence
- How to perform a Monte Carlo simulation
- The meaning of expected values and standard errors and how to compute them in R
- The Importance of the Central Limit Theorem
Course Duration: 8 weeks (1-2 hours/week)
5. Data Science: Linear Regression- Harvard University
Linear regression is commonly used to quantify the relationship between two or more variables. It is also used to adjust for confounding. This course is fifth on the list of online research courses and covers how to implement linear regression and adjust for confounding in practice using R. In data science applications, it is very common to be interested in the relationship between two or more variables. We will also examine confounding, where extraneous variables affect the relationship between two or more other variables, leading to spurious associations. Linear regression is a powerful technique for removing confounders, but it is not a magical process. It is essential to understand when it is appropriate to use, and this course will teach you when to apply this technique.
Course Highlights:
- How linear regression was originally developed by Galton
- What is confounding and how to detect it
- How to examine the relationships between variables by implementing linear regression in R
Course Duration: 8 weeks (1-2 hours/week)
6. Data Science: Inference and Modelling- Harvard University
In this course, you will learn these key concepts through a motivating case study on election forecasting. This course will show you how inference and modeling can be applied to develop the statistical approaches that make polls an effective tool and we’ll show you how to do this using R. You will learn concepts necessary to define estimates and margins of errors and learn how you can use these to make predictions relatively well and also provide an estimate of the precision of your forecast.
Course Highlights:
- The concepts necessary to define estimates and margins of errors of populations, parameters, estimates and standard errors in order to make predictions about data
- How to use models to aggregate data from different sources
- The very basics of Bayesian statistics and predictive modelling
Course Duration: 8 weeks (1-2 hours/week)
7. Advanced Statistics for Data Science Specialization
Fundamental concepts in probability, statistics and linear models are primary building blocks for data science work. Learners aspiring to become biostatisticians and data scientists will benefit from the foundational knowledge being offered in this specialization. It will enable the learner to understand the behind-the-scenes mechanism of key modelling tools in data science, like least squares and linear regression.
This specialization starts with Mathematical Statistics boot camps, specifically concepts and methods used in biostatistics applications. These range from probability, distribution, and likelihood concepts to hypothesis testing and case-control sampling.
Course Highlights:
- Learn about probability, expectations, conditional probabilities, distributions, confidence intervals, bootstrapping, binomial proportions, and more.
- Understand the matrix algebra of linear regression models.
- Learn about canonical examples of linear models to relate them to techniques that you may already be using.
Course Duration: 2 hours/week
8. Introduction to Data Analysis Using Excel
The use of Excel is widespread in the industry. It is a very powerful data analysis tool and almost all big and small businesses use Excel in their day-to-day functioning. This is an introductory course in the use of Excel and is designed to give you a working knowledge of Excel with the aim of getting to use it for more advanced topics in Business Statistics later. The course is designed keeping in mind two kinds of learners – those who have very little functional knowledge of Excel and those who use Excel regularly but at a peripheral level and wish to enhance their skills.
The course takes you from basic operations such as reading data into Excel using various data formats, and organizing and manipulating data, to some of the more advanced functionality of Excel. All along, Excel functionality is introduced using easy-to-understand examples which are demonstrated in a way that learners can become comfortable in understanding and applying them.
Course Highlights:
Course Duration: 24 hours
9. Biostatistics in Public Health Specialization
This specialization is intended for public health and healthcare professionals, researchers, data analysts, social workers, and others who need a comprehensive concepts-centric biostatistics primer. Those who complete the specialization will be able to read and respond to the scientific literature, including the Methods and Results sections, in public health, medicine, biological science, and related fields. Successful learners will also be prepared to participate as part of a research team.
Course Highlights:
- Calculate summary statistics from public health and biomedical data
- Interpret written and visual presentations of statistical data
- Evaluate and interpret the results of various regression methods
Course Duration: 4 hours/week
Writing:
10. Writing in English at University
Acquiring good academic research and writing skills early on is essential for your success both at university and in your professional life. This course aims:- to give you an understanding of the conventions of academic writing in English and to teach you the components and benefits of what is called process writing. – to help you to put together your own “toolbox” of academic writing skills, as well as to give you a chance to test out these tools and to reflect on your own development as a writer. – to encourage reflection on discipline-specific conventions; although the course deals with generic skills, you will be able to apply these generic skills to meet the particular needs of your own discipline.
Course Highlights:
Course Duration: 24 hours
11. How to Write And Publish a Scientific Paper
In this project-based course, you will outline a complete scientific paper, choose an appropriate journal to which you’ll submit the finished paper for publication, and prepare a checklist that will allow you to independently judge whether your paper is ready to submit. If you just finished your graduate dissertation, just began your PhD, or are at a different stage of your academic journey or career and just want to publish your work, this course is for you.
Course Highlights:
- This course is designed for students who have previous experience with academic research
Course Duration: 13 hours
12. Writing in the Sciences
This course teaches scientists to become more effective writers, using practical examples and exercises and is twelfth on the list of online research courses. Topics include principles of good writing, tricks for writing faster and with less anxiety, the format of a scientific manuscript, peer review, grant writing, ethical issues in scientific publication, and writing for general audiences.
Course Highlights:
Course Duration: 30 hours
Study Designs:
13. Quantitative and Qualitative Research for Beginners
Quantitative and Qualitative Research for Beginners is an introductory course for research methodologies across academic disciplines. The module focuses on principles and techniques that are appropriate to the introductory level. It provides both theoretical and practical information for students and introduces basic principles and techniques for all stages of the research process.
Course Highlights:
- understand general principles of research design
- know why and how research is undertaken
- be able to identify the overall process of designing a research study from its inception to its report
- be able to identify a research problem stated in a study; be familiar with conducting a literature review for a research study
- know the primary characteristics of quantitative research, qualitative research, and mixed methods research
- know the steps involved in conducting research; be able to distinguish between the writing structure used for a quantitative study and one used for a qualitative study.
Course Duration: 8 weeks (1-2 hours/week)
14. Study Designs in Epidemiology
In this course, you will learn about the main epidemiological study designs, including cross-sectional and ecological studies, case-control and cohort studies, as well as the more complex nested case-control and case-cohort designs. The final module is dedicated to randomised controlled trials, which is often considered the optimal study design, especially in clinical research. You will also develop the skills to identify the strengths and limitations of the various study designs. By the end of this course, you will be able to choose the most suitable study design considering the research question, the available time, and resources.
Course Highlights:
- Compare and contrast different epidemiological study designs in order to describe their strengths and weaknesses.
Course Duration: 8 hours
15. Understanding Clinical Research: Behind the Statistics
If you are simply interested in properly understanding the published literature or if you are embarking on conducting your own research, this course is your first step. It offers an easy entry into interpreting common statistical concepts without getting into nitty-gritty mathematical formulae. To be able to interpret and understand these concepts is the best way to start your journey into the world of clinical literature. That’s where this course comes in – so let’s get started! The course is free to enroll and take. You will be offered the option of purchasing a certificate of completion which you become eligible for if you successfully complete the course requirements. This can be an excellent way of staying motivated! Financial Aid is also available.
Course Highlights:
Course Duration: 27 hours
16. Introduction to Systematic Review and Meta-Analysis
We will introduce methods to perform systematic reviews and meta-analyses of clinical trials. We will cover how to formulate an answerable research question, define inclusion and exclusion criteria, search for the evidence, extract data, assess the risk of bias in clinical trials, and perform a meta-analysis. Upon successfully completing this course, participants will be able to:- Describe the steps in conducting a systematic review- Develop an answerable question using the “Participants Interventions Comparisons Outcomes” (PICO) framework- Describe the process used to collect and extract data from reports of clinical trials- Describe methods to critically assess the risk of bias of clinical trials- Describe and interpret the results of meta-analyses
Communication skills:
17. Rhetoric: The Art of Persuasive Writing and Public Speaking
We are living in a contentious time in history. Fundamental disagreements on critical policy, economic, and political issues make it essential to learn how to compose an effective argument and to analyze the arguments of others. This ability will help you engage in civil discourse and make needed changes in society. Conveying a convincing message can benefit your personal, public, and professional lives. This course is an introduction to the theory and practice of rhetoric, the art of persuasive writing and speech. In it, you will learn to construct and defend compelling arguments, a crucial skill in many settings.
Course Highlights:
- When and how to employ a variety of rhetorical devices in writing and speaking
- How to differentiate between argument and rhetorical technique
- How to write a persuasive opinion editorial and short speech
- How to evaluate the strength of an argument
- How to identify logical fallacies in arguments
Course Duration: 8 weeks/1-2 hours
Grant writing:
18. Just Start Grant Writing
The course explains the primary elements most writers ignore which leads them to waste a lot of time and effort on grants that they have ABSOLUTELY no chance of winning. The course also walks you through 3 different winning grants, at different funding levels, and shares in-depth details of the tools used to win them. The key to successful grant writing is knowing how each of these elements works together to get you the funding you need to do MORE of the work you care about.
Course Highlights:
- The Primary Elements of Writing a winning grant proposal.
- Learn how grants are really scored.
- The top 10 reasons grants are rejected and/or underfunded.
- I break down what you NEED TO WRITE to get funding.
- How to get funding even if you’re NOT a non-profit organization.
- I will show you step by step how to turn a “No” into a “Yes!”
- Tips, tricks and strategies that I’ve learned over the last 30 years.
Course Duration: 43 mins
Project Management:
19. Project Management: The Basics for Success
This course combines the essential elements of Project Management and Team Leadership into one course. Through class engagement and reflection, you will acquire a further understanding of the responsibilities of leadership and become better prepared to apply this knowledge to the project environment.
Course Highlights:
- Understand the stages of the project cycle
- Monitor project activities and assess progress
- Communicate proficiently to report project status
- Develop and strengthen high-performance teams
Course Duration: 8 hours
