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Power Query and Power Pivot in Microsoft Excel:  A Quickstart Guide   Course Description   Copyright Ross Maynard 2022   Course Description Power Query and Power Pivot are two tools available in Microsoft Excel. If you are serious about data analysis, then you need to know how to use these tools. This Quickstart guide will give you the skills you need to get started with these powerful features. The Power Query tool allows you to import external data and then modify it to meet your needs, before loading it into Excel. These editing tasks are all things you can do manually in Excel, but Power Query greatly speeds things up when you have a large volume of data. The Power Pivot tool allows you to connect multiple data tables in an extended pivot table. Power Pivot also introduces new analysis and query tools to further enhance data analysis. If data analysis is your thing, then this course will help you get up to speed with these feature-filled tools. Through screen-capture video, the course provides a step-by-step guide to the foundation skills needed to use both Power Query and Power Pivot effectively. The course is designed to make your first steps into Power Query easy and to build your confidence to use them on your own. Both Power Query and Power Pivot are also available in Microsoft Power BI – the data visualisation suite. Once you have learned how to use Power Query and Power Pivot in Excel with this course, you’ll have a head start in getting to grips with Power BI Get up to speed with Power Query and Power Pivot in Microsoft Excel with this Quickstart guide.     Key Learning Points  On completion of the course, delegates will be able to: Use Power Query to load data from an external source into Microsoft Excel Clean and manipulate external data before loading it into Excel Consolidate separate files with Power Query Update Excel tables with new data in the same format Use Power Query to “unpivot” report tables to prepare the data for analysis Explain the core functionality of Power Pivot Link data tables together in the Power Pivot data model Create pivot tables that draw data from several source data tables Create performance measures and new analysis in the Power Pivot data model Use the most popular DAX query language functions in Power Pivot     Curriculum Lesson 1: Power Query Quickstart Lesson 2: Consolidating Files with Power Query Lesson 3: Unpivoting a Table Lesson 4: Power Pivot Quickstart Lesson 5: Power Pivot – Adding Data and Functionality Lesson 6: Key DAX Functions for Power Pivot Lesson 7: The DAX CALCULATE Function Lesson 8: Key Learning Points     Pre-Course Requirements There are no pre-course requirements     Additional Resources Starter spreadsheets for each lesson are provided so that learners can follow the steps presented.     Course Tutor Your tutor is Ross Maynard. Ross is a Fellow of the Chartered Institute of Management Accountants in the UK and has 30 years’ experience as a process improvement consultant specialising in business processes and organisation development. Ross is also a professional author of online training courses. Ross lives in Scotland with his wife, daughter and Cocker Spaniel     Questions What is the main feature of Power Query? The ability to link data tables together to create pivot tables that allow data to be drawn from any and all of the source tables The ability to clean and manipulate large volumes of external data efficiently and to load the result into Microsoft Excel The removal of Microsoft Excel’s restrictions on file sizes and the number of rows of data allowed The ability to visualise data in dashboards and management reports     What is the main feature of Power Pivot? The ability to link data tables together to create pivot tables that allow data to be drawn from any and all of the source tables The ability to clean and manipulate large volumes of external data efficiently and to load the result into Microsoft Excel The removal of Microsoft Excel’s restrictions on file sizes and the number of rows of data allowed The ability to visualise data in dashboards and management reports     Which of the following actions cannot be performed within Power Query? Split text into multiple columns Trimming text and removing spaces Creating calculated columns using DAX functions Make date based calculations   Which of the following statements is true? You can use Power Query to combine files that have the same format and open them all in the same spreadsheet You can use Power Query to link data tables and create pivot tables that pull data items from all the linked tables You can use Power Query to create interactive dashboard reports You can use Power Query to export data files to Microsoft Access and other databases     Which of the following provides a compelling case to use Power Pivot in Microsoft Excel? The data visualisation and presentation features of Power Pivot are much more powerful than those in Excel Power Pivot offers the ability to clean large files of data imported from other systems quickly Existing reports and tables can be “reverse engineered” (unpivoted) to create data tables for more advanced analysis Files sizes in Power Pivot are smaller than in Excel and there is no limit on the number of rows of data that can be stored. This makes it much faster and more efficient to use Power Pivot for large data sets     Power Pivot uses analysis functions from the DAX formula language. What does “DAX” stand for? Digital Architecture Extraction Data Arbitrage Extrapolation Display Analytics Extension Data Analysis Expressions Deeply Annoying Exercise   The DAX CALCULATE function is one of the most powerful and useful functions available in Power Pivot. Which statement best describes it? It performs row by row calculations defined by the user and adds the total of them all together for a specified table It performs row by row calculations defined by the user and displays the maximum result for a specified table It performs a defined calculation and then applies filters specified by the user It filters the data in a specified table according to criteria set by the user   As well as in Power Pivot in Microsoft Excel, in what application are DAX functions also available? Microsoft PowerPoint Microsoft Forms Microsoft Teams Microsoft Power BI   How do DAX functions differ from Microsoft Excel functions? DAX functions are designed specifically for use with SQL based data sources DAX functions are completely new and provide additional functionality within Microsoft Excel DAX functions never take a cell reference or a range as reference, but instead take a column or table as reference DAX date and time functions return a date as a serial number, whereas Excel date and time functions return a datetime data type  

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48-Hour Knowledge Knockdown! Prices Reduced Like Never Before. According to recent statistics, the healthcare industry is expected to grow exponentially in the coming years, which means that the demand for individuals with a background in human anatomy and physiology will be high. By enrolling in this Human Anatomy and Physiology course, you will be investing in your future and setting yourself up for success in a thriving industry. If you are interested in learning more about human anatomy and physiology? This Human Anatomy and Physiology course is the perfect opportunity to delve deeper into the inner workings of the human body. This Advanced Diploma in Human Anatomy and Physiology at QLS Level 7 course is endorsed by The Quality Licence Scheme and accredited by CPDQS (with 180 CPD points) to make your skill development & career progression more accessible than ever! Through this Human Anatomy and Physiology course, you will develop a strong understanding of the human body, its functions and how its systems work together. Upon completion, you will be able to identify the different human tissues and understand the roles they play in the body. You will also gain an understanding of the nervous system, cardiovascular system, respiratory system and other major body systems. Additionally, you will learn about the male and female reproductive systems and how they work together to produce offspring. This Human Anatomy and Physiology course is going to prepare you for: Understand the different human tissues and their functions. Identify and explain the major body systems. Analyze the function of the nervous and cardiovascular systems. Understand the reproductive systems and their role in reproduction. Discuss the importance of the respiratory and digestive systems. Explain the role of the urinary system in maintaining body balance. 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Take a step toward a brighter future! *** Course Curriculum *** Here is the curriculum breakdown of the Human Anatomy and Physiology at QLS Level 7 course: Module 1: The Chemistry of Cells Module 2: Cells Module 3: Cell Division Module 4: Replication and Metabolism Module 5: Human Tissues Module 6: Skin Module 7:Human Skeletal System Module 8: The Muscular System Module 9: Skeletal Muscle Fibre Module 10: Nervous System Module 11: The Brain Module 12: Nervous System-Spinal Cord and Spinal Nerves Module 13: Nervous System - Autonomic Nervous System and Cranial Nerves Module 14: Nervous System-The Eyes Module 15: Nervous System-The Ears, Hearing, and Equilibrium Module 16: Nervous System-Memory Module 17: Sensory Systems Module 18: Cardiovascular System Module 19: Blood Module 20: Lymphatics Module 21: Immune System Module 22: Digestive System Module 23: Respiratory System Module 24: Urinary System Module 25: Male Reproductive System Module 26: Female Reproductive System Assessment Process After completing an online module, you will be given immediate access to a specially designed MCQ test. The results will be immediately analysed, and the score will be shown for your review. The passing score for each test will be set at 60%. You will be entitled to claim a certificate endorsed by the Quality Licence Scheme after you have completed all of the Advanced Diploma in Human Anatomy and Physiology at QLS Level 7 exams. CPD 180 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This Human Anatomy and Physiology course is ideal for individuals who are interested in pursuing a career in healthcare or anyone who wants to expand their knowledge of the human body. It is also suitable for individuals who are currently working in healthcare and want to refresh their understanding of human anatomy and physiology. Requirements No prior background or expertise is required. Career path Medical Lab Technician - £23,000 - £30,000 Physician Assistant - £45,000 - £65,000 Biomedical Scientist - £24,000 - £40,000 Occupational Therapist - £25,000 - £40,000 Paramedic - £22,000 - £32,000 Healthcare Scientist - £20,000 - £40,000 Enroll in this Human Anatomy and Physiology course today and take the first step towards a rewarding career in healthcare. Certificates Advanced Diploma in Human Anatomy and Physiology at QLS Level 7 Hard copy certificate - Included Show off Your New Skills with a Certificate of Completion After successfully completing the Advanced Diploma in Human Anatomy and Physiology at QLS Level 7, you can order an original hardcopy certificate of achievement endorsed by the Quality Licence Scheme and also you can order CPDQSAccredited Certificate that is recognised all over the UK and also internationally. The certificates will be home-delivered, completely free of cost. CPDQS Accredited Certificate Digital certificate - Included

Are you interested in learning and deploying applications at scale using Google Cloud platform? Do you lack hands-on exposure when it comes to deploying applications and seeing them in action? Then this course is for you. You will also learn microservices and event-driven architectures with real-world use case implementations.

Duration 2 Days 12 CPD hours This course is intended for Business Analysts, Technical Managers, and Programmers Overview This intensive training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice. Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning. What is R ? What is R? ? Positioning of R in the Data Science Space ? The Legal Aspects ? Microsoft R Open ? R Integrated Development Environments ? Running R ? Running RStudio ? Getting Help ? General Notes on R Commands and Statements ? Assignment Operators ? R Core Data Structures ? Assignment Example ? R Objects and Workspace ? Printing Objects ? Arithmetic Operators ? Logical Operators ? System Date and Time ? Operations ? User-defined Functions ? Control Statements ? Conditional Execution ? Repetitive Execution ? Repetitive execution ? Built-in Functions ? Summary Introduction to Functional Programming with R ? What is Functional Programming (FP)? ? Terminology: Higher-Order Functions ? A Short List of Languages that Support FP ? Functional Programming in R ? Vector and Matrix Arithmetic ? Vector Arithmetic Example ? More Examples of FP in R ? Summary Managing Your Environment ? Getting and Setting the Working Directory ? Getting the List of Files in a Directory ? The R Home Directory ? Executing External R commands ? Loading External Scripts in RStudio ? Listing Objects in Workspace ? Removing Objects in Workspace ? Saving Your Workspace in R ? Saving Your Workspace in RStudio ? Saving Your Workspace in R GUI ? Loading Your Workspace ? Diverting Output to a File ? Batch (Unattended) Processing ? Controlling Global Options ? Summary R Type System and Structures ? The R Data Types ? System Date and Time ? Formatting Date and Time ? Using the mode() Function ? R Data Structures ? What is the Type of My Data Structure? ? Creating Vectors ? Logical Vectors ? Character Vectors ? Factorization ? Multi-Mode Vectors ? The Length of the Vector ? Getting Vector Elements ? Lists ? A List with Element Names ? Extracting List Elements ? Adding to a List ? Matrix Data Structure ? Creating Matrices ? Creating Matrices with cbind() and rbind() ? Working with Data Frames ? Matrices vs Data Frames ? A Data Frame Sample ? Creating a Data Frame ? Accessing Data Cells ? Getting Info About a Data Frame ? Selecting Columns in Data Frames ? Selecting Rows in Data Frames ? Getting a Subset of a Data Frame ? Sorting (ordering) Data in Data Frames by Attribute(s) ? Editing Data Frames ? The str() Function ? Type Conversion (Coercion) ? The summary() Function ? Checking an Object's Type ? Summary Extending R ? The Base R Packages ? Loading Packages ? What is the Difference between Package and Library? ? Extending R ? The CRAN Web Site ? Extending R in R GUI ? Extending R in RStudio ? Installing and Removing Packages from Command-Line ? Summary Read-Write and Import-Export Operations in R ? Reading Data from a File into a Vector ? Example of Reading Data from a File into A Vector ? Writing Data to a File ? Example of Writing Data to a File ? Reading Data into A Data Frame ? Writing CSV Files ? Importing Data into R ? Exporting Data from R ? Summary Statistical Computing Features in R ? Statistical Computing Features ? Descriptive Statistics ? Basic Statistical Functions ? Examples of Using Basic Statistical Functions ? Non-uniformity of a Probability Distribution ? Writing Your Own skew and kurtosis Functions ? Generating Normally Distributed Random Numbers ? Generating Uniformly Distributed Random Numbers ? Using the summary() Function ? Math Functions Used in Data Analysis ? Examples of Using Math Functions ? Correlations ? Correlation Example ? Testing Correlation Coefficient for Significance ? The cor.test() Function ? The cor.test() Example ? Regression Analysis ? Types of Regression ? Simple Linear Regression Model ? Least-Squares Method (LSM) ? LSM Assumptions ? Fitting Linear Regression Models in R ? Example of Using lm() ? Confidence Intervals for Model Parameters ? Example of Using lm() with a Data Frame ? Regression Models in Excel ? Multiple Regression Analysis ? Summary Data Manipulation and Transformation in R ? Applying Functions to Matrices and Data Frames ? The apply() Function ? Using apply() ? Using apply() with a User-Defined Function ? apply() Variants ? Using tapply() ? Adding a Column to a Data Frame ? Dropping A Column in a Data Frame ? The attach() and detach() Functions ? Sampling ? Using sample() for Generating Labels ? Set Operations ? Example of Using Set Operations ? The dplyr Package ? Object Masking (Shadowing) Considerations ? Getting More Information on dplyr in RStudio ? The search() or searchpaths() Functions ? Handling Large Data Sets in R with the data.table Package ? The fread() and fwrite() functions from the data.table Package ? Using the Data Table Structure ? Summary Data Visualization in R ? Data Visualization ? Data Visualization in R ? The ggplot2 Data Visualization Package ? Creating Bar Plots in R ? Creating Horizontal Bar Plots ? Using barplot() with Matrices ? Using barplot() with Matrices Example ? Customizing Plots ? Histograms in R ? Building Histograms with hist() ? Example of using hist() ? Pie Charts in R ? Examples of using pie() ? Generic X-Y Plotting ? Examples of the plot() function ? Dot Plots in R ? Saving Your Work ? Supported Export Options ? Plots in RStudio ? Saving a Plot as an Image ? Summary Using R Efficiently ? Object Memory Allocation Considerations ? Garbage Collection ? Finding Out About Loaded Packages ? Using the conflicts() Function ? Getting Information About the Object Source Package with the pryr Package ? Using the where() Function from the pryr Package ? Timing Your Code ? Timing Your Code with system.time() ? Timing Your Code with System.time() ? Sleeping a Program ? Handling Large Data Sets in R with the data.table Package ? Passing System-Level Parameters to R ? Summary Lab Exercises Lab 1 - Getting Started with R Lab 2 - Learning the R Type System and Structures Lab 3 - Read and Write Operations in R Lab 4 - Data Import and Export in R Lab 5 - k-Nearest Neighbors Algorithm Lab 6 - Creating Your Own Statistical Functions Lab 7 - Simple Linear Regression Lab 8 - Monte-Carlo Simulation (Method) Lab 9 - Data Processing with R Lab 10 - Using R Graphics Package Lab 11 - Using R Efficiently

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