If you don't use Data Analysis Expressions (DAX) Language, you will miss out on 95% of Power BI's potential as a fantastic analytical tool, and the journey to becoming a DAX master starts with the right step. This project-based course, "Using DAX throughout Power BI to create robust data scenarios," is intended for novice data analysts willing to advance their knowledge and skills. This 2-hour project-based course will teach you how to create columns, measures, and tables using DAX codes while understanding the importance of context in DAX formulas. Finally, we'll round off the course by introducing time-intelligence functions and show you how to use Quick Measures to create complex DAX code. This course is structured in a systematic way and very practical, where you get an option to practice as you progress. This project-based course is a beginner-level course in Power BI. Therefore, you should be familiar with the Power BI interface to get the most out of this project. Please join me on this beautiful ride! Let's take the first step in your DAX mastery journey!

Bayesian Statistics: Mixture Models introduces you to an important class of statistical models. The course is organized in five modules, each of which contains lecture videos, short quizzes, background reading, discussion prompts, and one or more peer-reviewed assignments. Statistics is best learned by doing it, not just watching a video, so the course is structured to help you learn through application. Some exercises require the use of R, a freely-available statistical software package. A brief tutorial is provided, but we encourage you to take advantage of the many other resources online for learning R if you are interested. This is an intermediate-level course, and it was designed to be the third in UC Santa Cruz's series on Bayesian statistics, after Herbie Lee's "Bayesian Statistics: From Concept to Data Analysis" and Matthew Heiner's "Bayesian Statistics: Techniques and Models." To succeed in the course, you should have some knowledge of and comfort with calculus-based probability, principles of maximum-likelihood estimation, and Bayesian estimation.

Digital health is rapidly being realised as the future of healthcare. While this is placing emphasis on the input of quality health data in digital records and systems, the delivery of safe and quality healthcare relies not only on the input of data, but also the ability to access and derive meaning from data to generate evidence, inform decision making and drive better health outcomes. This course provides insight into the use of healthcare data, including an overview of best practices and the practical realities of obtaining useful information from digital health systems via the understanding of the fundamental concepts of health data analytics. Learners will understand why data quality is essential in modern healthcare, as they are guided through various stages of the data life cycle, starting with the generation of quality health data, through to discovering patterns and extracting knowledge from health data using common methodologies and tools in the basic analysis, visualisation and communication of health data. In doing so, learners explore current healthcare delivery contexts, and future and emerging digital health data systems and applications that are rapidly becoming tomorrow’s reality. On completion of this course, you will be able to: 1. Identify digital health technologies, health data sources, and the evolving roles of health workforce in digital health environments 2. Understand key health data concepts and terminology, including the significance of data integrity and stakeholder roles in the data life cycle 3. Use health data and basic data analysis to inform and improve decision making and practice. 4. Apply effective methods of communication of health data to facilitate safe and quality care. During this course, you will interact with learning content contributed by: • Digital Health Cooperative Research Centre • Australian Digital Health Agency • eHealth NSW • Sydney Local Health District • The NSW Ministry of Health • Health Education and Training Institute • Clinical Excellence Commission • Chris O’Brien Lifehouse • Monash Partners / Australian Health Research Alliance • Australian Research Data Commons • Justice Health & Forensic Mental Health Network • South Eastern Sydney Local Health District • Western Sydney Local Health District • Westmead Breast Cancer Institute • Agency for Clinical Innovation • Western NSW Local Health District • Sydney Children’s Hospital Network This course is a collaborative venture between NSW Health, the University of Sydney and the Digital Health Cooperative Research Centre, including dedicated resources from eHealth NSW, Health Education and Training Institute, and the Research in Implementation Science & eHealth group. While many learning resources and case examples are drawn from the NSW Health service context, this course has relevance for all existing and future health workforce, regardless of role or work context. Note: Materials used are for learning purposes and content may not reflect your organisation’s policies. When working with data, make sure you act within the guidelines and policies of your organisation.

In this course, you will build on the skills learned in Introduction to Image Processing to work through common complications such as noise. You’ll use spatial filters to deal with different types of artifacts. You’ll learn new approaches to segmentation such as edge detection and clustering. You’ll also analyze regions of interest and calculate properties such as size, orientation, and location. By the end of this course, you’ll be able to separate and analyze regions in your own images. You’ll apply your skills to segment an MRI image of a brain to separate different tissues. You will use MATLAB throughout this course. MATLAB is the go-to choice for millions of people working in engineering and science, and provides the capabilities you need to accomplish your image processing tasks. You will be provided with free access to MATLAB for the duration of the course to complete your work. To be successful in this course you should have a background in basic math and some exposure to MATLAB. If you want to familiarize yourself with MATLAB check out the free, two-hour MATLAB Onramp. Experience with image processing is not required.

Welcome to this project-based course, Introduction to Regular Expressions in SQL. In this project, you will learn how to use SQL regular expressions extensively for pattern matching to query tables in a database. By the end of this 2-and-a-half-hour-long project, you will be able to use POSIX regular expressions together with meta (special) characters in the WHERE clause and the SELECT clause to retrieve the desired result from a database. In this project, we will move systematically by first revising the use of the LIKE and NOT LIKE operators in the WHERE clause. Then, we will use different regular expression metacharacters together with POSIX operators in the WHERE clause. Also, we will use regular expressions to work on tweets from Twitter data. Be assured that you will get your hands really dirty in this project because you will get to work on a lot of exercises to reinforce your knowledge of the concepts. Also, for this hands-on project, we will use PostgreSQL as our preferred database management system (DBMS). Therefore, to complete this project, it is required that you have prior experience with using PostgreSQL. Similarly, this project is an intermediate SQL concept; so, a good foundation for writing SQL queries is vital to complete this project. If you are not familiar with writing queries in SQL and want to learn these concepts, start with my previous guided projects titled “Querying Databases using SQL SELECT statement." I taught this guided project using PostgreSQL. So, taking these projects will give the needed requisite to complete this Introduction to Regular Expressions in SQL project. However, if you are comfortable writing queries in PostgreSQL, please join me on this wonderful ride! Let’s get our hands dirty!

This is a self-paced lab that takes place in the Google Cloud console. This hands-on lab demonstrates how to use Binary Authorization to secure your GKE cluster by requiring all containers to be verified and signed by trusted attestors as a part of the build/deploy process.

By the end of this project, you will understand use cases for conducting forecasts in your workplace and be able to confidently conduct a trend forecast in any spreadsheet software. You will also understand when it is necessary to refine a model to improve the accuracy of forecasted trends. There are many times when having a crystal ball might be useful and it’s natural to leverage trusted predictions of future outcomes to prepare and drive best results. Predictions come our way in the form of the forecasted data we consume regularly in our personal and business lives. This data covers everything from the weather to projected investment returns. At work we use forecasted data for a multitude of purposes including developing strategies, budgets, to provide the right amount of resources to meet demand, and to create the best customer experience possible. In this course, you will build baseline prediction skills with statistical forecasting by designing, creating, and interpreting a sales trend forecast. You will do this as we work side-by-side in the free-to-use software Google Sheets. 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.

Sometimes the visual understanding of a mathematical problem is quite helpful in solving it fast and correctly. Play with graph is an attempt to ease the minds of all mathematics lovers and engineering aspirants who wish to solve the tricky and knotty mathematics problem involving functional approach. This project will help you make intuitive connections between the graphs of the function in the problem with the exact solution of the problem. The project starts with plotting basic functions ,labelling & fillings and later takes up the topic such as curve filling 'below' and 'between' the two curves ,plotting of sample points where the curve changes quickly, ranges where function becomes non real, plotting curve where there are discontinuities and finally labelling and legending of illustrative curves. It is highly recommended to all those who sincerely desire to master problem solving in mathematics.

Effective planning isn’t just an annual top-down strategic planning and budgeting exercise. To adapt in the turbulent global economy, successful organizations plan in real-time, across the organization, at all times. By leveraging a Connected Planning approach and technology, organizations around the world are finding ways to not only survive, but thrive. In this course, you’ll explore examples of how Connected Planning transforms the way organizations do business. Using real-life case studies from the Finance, Sales, Supply Chain, and Human Resources functions, you’ll see a wide range of Connected Planning examples and benefits. Most significantly, Connected Planning is cross-functional, which generates even greater impact for an organization. By the end of this course, you’ll be able to: • Explain how Connected Planning provides the link between strategic planning and operational execution • Describe a range of ways that organizations apply Connected Planning within and across functions • Articulate the benefits of Connected Planning This course is presented by Anaplan, provider of a leading technology platform that is purpose-built for Connected Planning.

The R programming language is purpose-built for data analysis. R is the key that opens the door between the problems that you want to solve with data and the answers you need to meet your objectives. This course starts with a question and then walks you through the process of answering it through data. You will first learn important techniques for preparing (or wrangling) your data for analysis. You will then learn how to gain a better understanding of your data through exploratory data analysis, helping you to summarize your data and identify relevant relationships between variables that can lead to insights. Once your data is ready to analyze, you will learn how to develop your model and evaluate and tune its performance. By following this process, you can be sure that your data analysis performs to the standards that you have set, and you can have confidence in the results. You will build hands-on experience by playing the role of a data analyst who is analyzing airline departure and arrival data to predict flight delays. Using an Airline Reporting Carrier On-Time Performance Dataset, you will practice reading data files, preprocessing data, creating models, improving models, and evaluating them to ultimately choose the best model. Watch the videos, work through the labs, and add to your portfolio. Good luck! Note: The pre-requisite for this course is basic R programming skills. For example, ensure that you have completed a course like Introduction to R Programming for Data Science from IBM.