In this 2-hour long project-based course, you will understand how to use SQL joins like INNER JOIN, LEFT JOIN, and RIGHT JOIN to get a desired result set. In addition, you will learn how to use SQL Joins with the WHERE clause and with aggregate functions. By extension, you will learn how to join more than two tables in the database. Note: You do not need to be a data administrator or data analyst expert to be successful in this guided project, just you have to be familiar with querying databases using SQL SELECT statement to get the most of this project. If you are not familiar with SQL and want to learn the basics, start with my previous guided projects titled “Performing Data definition and Manipulation in SQL", “Querying Databases using SQL SELECT statement” and “Performing Data Aggregation using SQL Aggregate Functions”

This is a self-paced lab that takes place in the Google Cloud console. In this lab you will create a Kubernetes cluster and deploy a simple application to that cluster. Then, connect the application to the supplied Cloud SQL for PostgreSQL database instance and confirm that it is able to write to and read from it.

In this 2-hour long project-based course, you will learn the basics of using Power BI Desktop software. We will do this by analyzing data on credit card defaults with Power BI Desktop. Power BI Desktop is a free Business Intelligence application from Microsoft that lets you load, transform, and visualize data. You can create interactive reports and dashboards quite easily, and quickly. We will learn some of the basics of Power BI by importing, transforming, and visualizing the data. This course is aimed at learners who are looking to get started with the Power BI Desktop software. There are no hard prerequisites and any competent computer user should be able to complete the project successfully. 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.

Neurohacking describes how to use the R programming language (https://cran.r-project.org/) and its associated package to perform manipulation, processing, and analysis of neuroimaging data. We focus on publicly-available structural magnetic resonance imaging (MRI). We discuss concepts such as inhomogeneity correction, image registration, and image visualization. By the end of this course, you will be able to: Read/write images of the brain in the NIfTI (Neuroimaging Informatics Technology Initiative) format Visualize and explore these images Perform inhomogeneity correction, brain extraction, and image registration (within a subject and to a template).

Conduct exploratory data analysis with a systematic approach to investigate different aspects of your data: comparisons, relationships, compositions, and distributions. This guided project gives you a framework so you can conduct your own exploratory data analysis and make your work more professional and organized. The language is Python and the libraries used are seaborn, pandas, and matplotlib.

In this structured series of hands-on guided projects, we will master the fundamentals of data analysis and manipulation with Pandas and Python. Pandas is a super powerful, fast, flexible and easy to use open-source data analysis and manipulation tool. This guided project is the second of a series of multiple guided projects (learning path) that is designed for anyone who wants to master data analysis with pandas. 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.

Welcome to Fundamentals of Big Data, the fourth course of the Key Technologies of Data Analytics specialization. By enrolling in this course, you are taking the next step in your career in data analytics. This course is the fourth of a series that aims to prepare you for a role working in data analytics. In this course, you will be introduced to many of the core concepts of big data. You will learn about the primary systems used in big data. We’ll go through phases of a common big data life cycle. This course covers a wide variety of topics that are critical for understanding big data and are designed to give you an introduction and overview as you begin to build relevant knowledge and skills.

In this fourth of our five courses, I will go deeper into the training and education leadership skills that are helpful for nursing informatics leaders. I will also guide you through the process of preparing a course document or syllabus for the nursing informatics specialty both in academic settings and in practice or industry. Following are the course objectives: 1. Describe relevant nursing informatics course development in clinical and academic settings to understand similarities and differences in informatics teaching and education across settings. 2. Describe informatics education and training needs for diverse participants with various experience levels to enable development of appropriate training and education materials. 3. Develop a prototype course syllabus and introductory recorded message to apply learning in a simulated setting. 4. Describe the benefits of formal and informal mentoring for nursing informaticians to advance career opportunities and support the nursing informatics specialty.

This guided project is about book genre classification using logistic regression in Julia. It is ideal for beginners who do not know what logistic regression is because this project explains these concepts in simple terms. While you are watching me code, you will get a cloud desktop with all the required software pre-installed. This will allow you to code along with me. After all, we learn best with active, hands-on learning. Special features: 1) Simple explanations of important concepts. 2) Use of images to aid in explanation. 3) Use a real world dataset. Note: This project works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

By the end of this course, learners are prepared to identify and test the best solutions for improving performance and integrating concepts from operational excellence methodologies for optimum data-driven decision making. The course begins with a focus on deciphering the root cause of problems through a variety of tools before determining and assessing best-fit solutions. Learners discover how to apply ISO, Lean and Six Sigma in the pursuit of aligning organizational operations data with performance standards. Hospitality, manufacturing and e-commerce case studies help illustrate how to build data literacy while ensuring privacy and data ethics measures are in place. Material features online lectures, videos, demos, project work, readings and discussions. This course is ideal for individuals keen on developing a data-driven mindset that derives powerful insights useful for improving a company’s bottom line. It is helpful if learners have some familiarity with reading reports, gathering and using data, and interpreting visualizations. It is the third course in the Data-Driven Decision Making (DDDM) specialization. To learn more about the specialization, check out a video overview at https://www.youtube.com/watch?v=Oi4mmeSWcVc&list=PLQvThJe-IglyYljMrdqwfsDzk56ncfoLx&index=11.