Power and Sample Size for Longitudinal and Multilevel Study Designs, a five-week, fully online course covers innovative, research-based power and sample size methods, and software for multilevel and longitudinal studies. The power and sample size methods and software taught in this course can be used for any health-related, or more generally, social science-related (e.g., educational research) application. All examples in the course videos are from real-world studies on behavioral and social science employing multilevel and longitudinal designs. The course philosophy is to focus on the conceptual knowledge to conduct power and sample size methods. The goal of the course is to teach and disseminate methods for accurate sample size choice, and ultimately, the creation of a power/sample size analysis for a relevant research study in your professional context. Power and sample size selection is one of the most important ethical questions researchers face. Interventional studies that are too large expose human volunteer research participants to possible, and needless, harm from research. Interventional studies that are too small will fail to reach their scientific objective, again bringing possible harm to research participants, without the possibility of concomitant gain from the increase in knowledge. For observational studies in which there are no possible harms to the participants, such as observational studies, proper power ensures good stewardship of both time and money. Most National Institutes of Health (NIH) study sections will only fund a grant if the grantee has written a compelling and accurate power and sample size analysis. The Institute of Education Sciences (IES), the statistics, research, and evaluation arm of the U.S. Department of Education, also offers competitive grants requiring a compelling and accurate power and sample size analysis (Goal 3: Efficacy and Replication and Goal 4: Effectiveness/Scale-Up). At the end of the online course, learners will be able to: • Use a framework and strategy for study planning • Write study aims as testable hypotheses • Describe a longitudinal and multilevel study design • Write a statistical analysis plan • Plan a sampling design for subgroups, e.g. racial and ethnic • Demonstrate the feasibility of recruitment • Describe expected missing data and dropout • Write a power and sample size analysis that is aligned with the planned statistical analysis This is a five-week intensive and interactive online course. We will use a mix of instructional videos, software demonstration videos, online readings, quizzes, and exercise assignments. The final course project is a peer-reviewed research study you design for future power or sample size analysis.

In this one-hour project, you will understand how Azure Text Analytics works and how you can use the power of Natual Language Processing, NLP, and Machine Learning to extract information and explore insights from text. You will learn how to use Azure Text Analytics to extract entities' sentiments, key phrases, and other elements from text like product reviews, understand how the results are organized, manipulate the data and generate a report to explore the insights. Azure Text Analytics is a fully managed service, and it is one of the most powerful Natural Language Processing engines in the market, so you can get up and running quickly, without having to train models from scratch. Once you're done with this project, you will be able to use Azure Text Analytics to extract, analyze and explore insights in your documents in just a few steps.

Functional Magnetic Resonance Imaging (fMRI) is the most widely used technique for investigating the living, functioning human brain as people perform tasks and experience mental states. It is a convergence point for multidisciplinary work from many disciplines. Psychologists, statisticians, physicists, computer scientists, neuroscientists, medical researchers, behavioral scientists, engineers, public health researchers, biologists, and others are coming together to advance our understanding of the human mind and brain. This course covers the analysis of Functional Magnetic Resonance Imaging (fMRI) data. It is a continuation of the course “Principles of fMRI, Part 1”.

This is the first course of a six part specialization.  You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This first course in the IBM AI Enterprise Workflow Certification specialization introduces you to the scope of the specialization and prerequisites.  Specifically, the courses in this specialization are meant for practicing data scientists who are knowledgeable about probability, statistics, linear algebra, and Python tooling for data science and machine learning.  A hypothetical streaming media company will be introduced as your new client.  You will be introduced to the concept of design thinking, IBMs framework for organizing large enterprise AI projects.  You will also be introduced to the basics of scientific thinking, because the quality that distinguishes a seasoned data scientist from a beginner is creative, scientific thinking.  Finally you will start your work for the hypothetical media company by understanding the data they have, and by building a data ingestion pipeline using Python and Jupyter notebooks.   By the end of this course you should be able to: 1.  Know the advantages of carrying out data science using a structured process 2.  Describe how the stages of design thinking correspond to the AI enterprise workflow 3.  Discuss several strategies used to prioritize business opportunities 4.  Explain where data science and data engineering have the most overlap in the AI workflow 5.  Explain the purpose of testing in data ingestion  6.  Describe the use case for sparse matrices as a target destination for data ingestion  7.  Know the initial steps that can be taken towards automation of data ingestion pipelines   Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses.   What skills should you have? It is assumed you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.

Welcome to the “Deploy Bridgerton NLP SMS Text Generator” guided project. In this project, we will deploy an NLP text generator model that sends text messages of generated words to a phone number via SMS through a python Streamlit app. The model has been trained on quotes from Netflix's popular tv show "Bridgerton". This project is an intermediate python project for anyone interested in learning about how to productionize natural language text generator models as a Streamlit app on Heroku and leveraging python modules to send SMS texts. It requires preliminary knowledge on how to build and train NLP text generator models (as we will not be building or training models), how to utilize Git, and how to leverage multiple Python modules like the email and smtp modules. Learners would also need a Heroku account and some familiarity with the Python Streamlit module. At the end of this project, learners will have a publicly available Streamlit web app that leverages natural language processing text generation to send generated Bridgerton quotes via SMS to a phone number.

By the end of this project, you will learn how to use conditional and custom formatting in an Excel Spreadsheet by using a free version of Microsoft Office Excel. Excel is a spreadsheet that is similar in layout as accounting spreadsheets. It consists of individual cells that can be used to build functions, formulas, tables, and graphs that easily organize and analyze large amounts of information and data. Conditional formatting is a convenient tool for data analysis and visual representation of results. Knowing how to use this tool will save you a lot of time and effort. A fleet glance at the document will be enough to obtain the necessary information.

Welcome to Data Analytics Foundations for Accountancy I! You’re joining thousands of learners currently enrolled in the course. I'm excited to have you in the class and look forward to your contributions to the learning community. To begin, I recommend taking a few minutes to explore the course site. Review the material we’ll cover each week, and preview the assignments you’ll need to complete to pass the course. Click Discussions to see forums where you can discuss the course material with fellow students taking the class. If you have questions about course content, please post them in the forums to get help from others in the course community. For technical problems with the Coursera platform, visit the Learner Help Center. Good luck as you get started, and I hope you enjoy the course!

Learning Python gives the programmer a wide variety of career paths to choose from. Python is an open-source (free) programming language that is used in web programming, data science, artificial intelligence, and many scientific applications. Learning Python allows the programmer to focus on solving problems, rather than focusing on syntax. Its relative size and simplified syntax give it an edge over languages like Java and C++, yet the abundance of libraries gives it the power needed to accomplish great things. In this tutorial you will create a guessing game application that pits the computer against the user. You will create variables, decision constructs, and loops in python to create the game. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Machine learning systems used in Clinical Decision Support Systems (CDSS) require further external validation, calibration analysis, assessment of bias and fairness. In this course, the main concepts of machine learning evaluation adopted in CDSS will be explained. Furthermore, decision curve analysis along with human-centred CDSS that need to be explainable will be discussed. Finally, privacy concerns of deep learning models and potential adversarial attacks will be presented along with the vision for a new generation of explainable and privacy-preserved CDSS.

By the end of this project, you will create a C# application using MongoDB to access Employee data perform CRUD operations on the MongoDB database. Many Applications use a MongoDB database on the backend, and nearly every programming language has a driver for it. Since MongoDB is a No-SQL database, it works quite well for storing C# objects. Conversely, reading MongoDB documents into C# objects is quite seamless, especially when compared to reading Relational database data into objects. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.