The top 4 Research Data courses you need to take

1. Data Science for Health Research

In Data Science for Health Research, learn to organize and visualize health data using statistical analysis in programs like R. Explore how to translate data, interpret statistical models, and predict outcomes to help make data-informed decisions within the public health field...

2. How to Visualize Research Data in Tableau

Publishing research often requires the preparation of visual elements like charts, tables, and graphs to better explain the text in a research report. Creating these elements can be done easily and effectively in Tableau. Using Tableau, large and small data sets can be visualized with precision, creativity, interactivity, and options in Tableau. After taking this course learners will know how to create a table, a geovisualization, and a pie chart. Three of the most common research visualizations available. The learners will also learn how to upload data, how to export these tables, and how to incorporate charts and graphs in research reports. Researchers from students to professionals will benefit from learning how to create visualizations based on surveys, observations, experiments, and other types of research methods. Knowledge of research is useful but not required for this project...

3. SPSS Data Analysis for Beginning Researchers

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Thank you for checking in SPSS Data Analysis for Beginning Researchers. Who is this course for?As the title implies, this course is for people working on their very first research projects (i. e. beginners / newbies), including but not limited to: Students working on their research papers or dissertationsBeginning researchers with a non-technical backgroundAnyone curious about data analysisWhat is so difficult about data analysis?Many people find data analysis difficult, and with good reasons. Data analysis is difficult because it is not a single discipline. It is multi-disciplinary, which means that it requires integrated knowledge from different fields in order to do it right. Specifically, to conduct data analysis for your research you need: Knowledge in the data analysis software (e. g. SPSS, Excel, R, etc.)Knowledge in statistics conceptsKnowledge in research methodsExperience and skills working with dataWhat you need is not only knowledge in separate fields, but also experience and skills integrating these knowledge together to deal with real life data. However, beginning researchers, by definition, have very little of these knowledge, experience, and skills. For example: You may know how to use the data analysis software, but you don't know what method of analysis to use because you are not familiar with the statistics concepts. You may know some statistics, but you may not know how to calculate the statistics on the computer. You may have knowledge in both statistics and data analysis software, but you are not sure what analysis to conduct in order to fulfill the research needs. You may have knowledge in statistics, software, and research, but you may not have the experience in actually handling data, and you are stuck dealing with practical issues here and there (such as missing and invalid data). There are plenty of textbooks in these different disciplines, but few of them could teach you all these knowledge and skills. The problem is not lack of information. Quite the contrary, the problem is overwhelmingly rich information, so rich that you may not know where to start and how to select, so rich that you may not know how to put them into practice to fulfill your specific needs. How may this course help?This course is designed to be concise and practical. I am not attempting to tell you everything about statistics, SPSS, data analysis, and research - that would make your learning journey unnecessarily difficult. Instead, I am going to guide you, in an structured and practical way, through the minimal set of knowledge and skills you would need to analyze your own data. This course will not make you an expert in statistics, SPSS, data analysis, and research, but it will help you finish your own data analysis. This is to be achieved by the following: Background knowledge. Each section of the course begins with a brief introduction to the minimal set of necessary statistics concepts you need. Practical demonstrations. All the videos are example-based. In each video, I show you how to conduct a practical data analysis task. These tasks are carefully selected from a list of most common analyses that you are likely to conduct. Experience sharing. In addition to statistics and SPSS, I also share a lot of my own experience doing research and data analysis, including how to deal with the most common issues while working with data, avoid the common mistakes and misunderstandings, and work around some annoying bugs in SPSS. Key points. Key points are highlighted throughout the video and also recapped at the end of the videos. Exercise. There is an exercise at the end of each section. This helps you apply what you have learned in the previous videos. There are also questions that prompt you to think deeper about what you are doing. The exercise problems have been used for a few years in my own offline classes so they are proven to be helpful to students. While appropriate, a separate video is dedicated to demonstrating the answers to these exercise problems. References. For those who would like to dig deeper into the statistics concepts, I have included links to useful references for your pursue. So, how may I learn effectively in this course?You may do the following for each section: Watch the videos. Take notes while necessary. Complete the exercise on your own. Knowing is not enough. We must apply! If you forget some of the details, refer back to the previous videos. After attempting the exercise, watch the next video for answers. Watch my steps carefully and compare with yours. In case of any difference, ask yourself which way is better, and why. Last but not least, apply the techniques to your own data. That's all for the introduction. Happy learning!...