251 Pivot Table courses

Overview This Microsoft Excel: Master Power Query course will unlock your full potential and will show you how to excel in a career in Microsoft Excel: Master Power Query. So upskill now and reach your full potential. Everything you need to get started in Microsoft Excel: Master Power Query is available in this course. Learning and progressing are the hallmarks of personal development. This Microsoft Excel: Master Power Query will quickly teach you the must-have skills needed to start in the relevant industry. In This Microsoft Excel: Master Power Query Course, You Will: Learn strategies to boost your workplace efficiency. Hone your Microsoft Excel: Master Power Query skills to help you advance your career. Acquire a comprehensive understanding of various Microsoft Excel: Master Power Query topics and tips from industry experts. Learn in-demand Microsoft Excel: Master Power Query skills that are in high demand among UK employers, which will help you to kickstart your career. This Microsoft Excel: Master Power Query course covers everything you must know to stand against the tough competition in the Microsoft Excel: Master Power Query field.  The future is truly yours to seize with this Microsoft Excel: Master Power Query. Enrol today and complete the course to achieve a Microsoft Excel: Master Power Query certificate that can change your professional career forever. Additional Perks of Buying a Course From Institute of Mental Health Study online - whenever and wherever you want. One-to-one support from a dedicated tutor throughout your course. Certificate immediately upon course completion 100% Money back guarantee Exclusive discounts on your next course purchase from Institute of Mental Health Enrolling in the Microsoft Excel: Master Power Query course can assist you in getting into your desired career quicker than you ever imagined. So without further ado, start now. Process of Evaluation After studying the Microsoft Excel: Master Power Query course, your skills and knowledge will be tested with a MCQ exam or assignment. You must get a score of 60% to pass the test and get your certificate.  Certificate of Achievement Upon successfully completing the Microsoft Excel: Master Power Query course, you will get your CPD accredited digital certificate immediately. And you can also claim the hardcopy certificate completely free of charge. All you have to do is pay a shipping charge of just £3.99. Who Is This Course for? This Microsoft Excel: Master Power Query is suitable for anyone aspiring to start a career in Microsoft Excel: Master Power Query; even if you are new to this and have no prior knowledge on Microsoft Excel: Master Power Query, this course is going to be very easy for you to understand.  And if you are already working in the Microsoft Excel: Master Power Query field, this course will be a great source of knowledge for you to improve your existing skills and take them to the next level.  Taking this Microsoft Excel: Master Power Query course is a win-win for you in all aspects.  This course has been developed with maximum flexibility and accessibility, making it ideal for people who don't have the time to devote to traditional education. Requirements This Microsoft Excel: Master Power Query course has no prerequisite.  You don't need any educational qualification or experience to enrol in the Microsoft Excel: Master Power Query course. Do note: you must be at least 16 years old to enrol. Any internet-connected device, such as a computer, tablet, or smartphone, can access this online Microsoft Excel: Master Power Query course. Moreover, this course allows you to learn at your own pace while developing transferable and marketable skills. Course Curriculum Microsoft Excel: Master Power Query Power Query Intro and Excel version 00:03:00 Excel Power Query - Introduction 00:03:00 Excel Power Query - Query Editor Ribbon 00:09:00 Transform Data - Trim in Excel Power Query 00:05:00 Transform Data - Format Dates and Values in Excel Power Query 00:02:00 Transform Data - Parsing URLs in Excel Power Query 00:05:00 Transform Data - Split Text Fields in Excel Power Query 00:10:00 Transform Data - Group By in Excel Power Query 00:03:00 Transform Data - Unpivoting Columns in Excel Power Query 00:05:00 Transform Data - Pivoting Columns in Excel Power Query 00:02:00 Transform Data - Split Columns into Other Columns in Excel Power Query 00:04:00 Transform Data - Filtering Rows in Excel Power Query 00:05:00 Transform Data - Sorting Columns in Excel Power Query 00:02:00 Transform Data - Transform and Add Columns in Excel Power Query 00:07:00 From Folder - Import From Folder in Excel Power Query 00:07:00 From Folder - Doing Auto Cleanup in Excel Power Query 00:13:00 From Folder - Extract Data from Forms in Excel Power Query 00:13:00 From Workbook - Extract Multiple Criteria in Excel Power Query 00:05:00 From Workbook - Extract Multiple Worksheets in Excel Power Query 00:04:00 Joins - Intro to Joins 00:04:00 Joins - Merging 00:08:00 Joins - Full Outer Join 00:06:00 Joins - Right Anti Join 00:09:00 Power Query - Convert Reports into Pivot Tables 00:05:00 Modulo 00:06:00

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Duration 2 Days 12 CPD hours This course is intended for New users of IBM SPSS Statistics Users who want to refresh their knowledge about IBM SPSS Statistics Anyone who is considering purchasing IBM SPSS Statistics Overview Introduction to IBM SPSS Statistics Review basic concepts in IBM SPSS Statistics Identify the steps in the research process Review basic analyses Use Help Reading data and defining metadata Overview of data sources Read from text files Read data from Microsoft Excel Read data from databases Define variable properties Selecting cases for analyses Select cases for analyses Run analyses for groups Apply report authoring styles Transforming variables Compute variables Recode values of categorical and scale variables Create a numeric variable from a string variable Using functions to transform variables Use statistical functions Use logical functions Use missing value functions Use conversion functions Use system variables Use the Date and Time Wizard Setting the unit of analysis Remove duplicate cases Create aggregate datasets Restructure datasets Merging data files Add cases from one dataset to another Add variables from one dataset to another Enrich a dataset with aggregated information Summarizing individual variables Define levels of measurement Summarizing categorical variables Summarizing scale variables Describing the relationship between variables Choose the appropriate procedure Summarize the relationship between categorical variables Summarize the relationship between a scale and a categorical variable Creating presentation ready tables with Custom Tables Identify table layouts Create tables for variables with shared categories Create tables for multiple response questions Customizing pivot tables Perform Automated Output Modification Customize pivot tables Use table templates Export pivot tables to other applications Working with syntax Use syntax to automate analyses Create, edit, and run syntax Shortcuts in the Syntax Editor Controlling the IBM SPSS Statistics environment Set options for output Set options for variables display Set options for default working folders This course guides students through the fundamentals of using IBM SPSS Statistics for typical data analysis. Students will learn the basics of reading data, data definition, data modification, data analysis, and presentation of analytical results. In addition to the fundamentals, students will learn shortcuts that will help them save time. This course uses the IBM SPSS Statistics Base; one section presents an add-on module, IBM SPSS Custom Tables. Introduction to IBM SPSS Statistics Review basic concepts in IBM SPSS Statistics Identify the steps in the research process Review basic analyses Use Help Reading data and defining metadata Overview of data sources Read from text files Read data from Microsoft Excel Read data from databases Define variable properties Selecting cases for analyses Select cases for analyses Run analyses for groups Apply report authoring styles Transforming variables Compute variables Recode values of categorical and scale variables Create a numeric variable from a string variable Using functions to transform variables Use statistical functions Use logical functions Use missing value functions Use conversion functions Use system variables Use the Date and Time Wizard Setting the unit of analysis Remove duplicate cases Create aggregate datasets Restructure datasets Merging data files Add cases from one dataset to another Add variables from one dataset to another Enrich a dataset with aggregated information Summarizing individual variables Define levels of measurement Summarizing categorical variables Summarizing scale variables Describing the relationship between variables Choose the appropriate procedure Summarize the relationship between categorical variables Summarize the relationship between a scale and a categorical variable Creating presentation ready tables with Custom Tables Identify table layouts Create tables for variables with shared categories Create tables for multiple response questions Customizing pivot tables Perform Automated Output Modification Customize pivot tables Use table templates Export pivot tables to other applications Working with syntax Use syntax to automate analyses Create, edit, and run syntax Shortcuts in the Syntax Editor Controlling the IBM SPSS Statistics environment Set options for output Set options for variables display Set options for default working folders Additional course details: Nexus Humans 0G53BG IBM SPSS Statistics Essentials (V26) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the 0G53BG IBM SPSS Statistics Essentials (V26) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 3 Days 18 CPD hours This course is intended for Analyst Developer End User Implementer Overview Schedule and Burst Reports Perform Translations Create Reports Integrated With Oracle BI EE Administer BI Publisher Server Describe BI Publisher Technology and Architecture Create reports from OBI EE data sources Create and Modify Data Models Create RTF Templates by Using Template Builder Explore and Use the Form Field Method for Creating RTF Templates Create Layouts by Using the Layout Editor This Oracle BI Publisher 12c training will help you build a foundation of understanding how to best leverage this solution. Through Classroom Training or Live Virtual Class Training, you'll learn the ins and outs of how to use this solution. BI Publisher Technology and Architecture Functional Components Layout Templates Multitier Architecture Enterprise Server Architecture and Performance and Scalability Document Generation Process and Output Formats Supported Data Sources Bursting Overview Internationalization and Language Support Getting Started with BI Publisher Logging In, the Home Page, and Global Header, and Setting Account Preferences Viewing Reports Managing Repository Objects Managing Favorites Using Create Report wizard to Create Reports Selecting Data: Data Model, Spreadsheet, and BI Subject Area Configuring Report Properties Using the Data Model Editor Exploring the Schemas Used in the Course Exploring the Data Model Editor UI and the Supported Data Sources Creating a Private Data Source Creating a Simple Data Model based on a SQL Query Data Set Using Query Builder to Build a Query Viewing Data and Saving Sample Data Sets Adding Parameters and LOVs to the Query Configuring Parameter Settings and Viewing Reports with Parameters Working with Layout Editor Opening the Layout Editor and Navigating the Layout Editor UI Creating a Layout by Using a Basic Template Inserting a Layout Grid Adding a Table, Formatting Columns, Defining Sorts and Groups, and Applying Conditional Formats Inserting and Editing Charts, and Converting Charts to a Pivot Tables Adding Repeating Sections, Text Items, and Images Working with Lists, Gauges and Pivot Tables Creating Boilerplates Using Template Builder to Create RTF Templates Using the BI Publisher Menu Bar Creating an RTF Template from a Sample, Changing Field Properties, and Previewing Table Data Adding a Chart to an RTF Template Designing an RTF Template for a BI Publisher Report Creating a BI Publisher Report by Using Template Builder in Online Mode Exploring the Basic and Form Field Methods Exploring Advanced RTF Template Techniques Including Conditional Formats, Watermarks, Page-Level Calculations, Running Totals, Grouping, and Sorting BI Publisher Server: Administration and Security Describing the Administration Page Creating the JDBC Connections Setting, Viewing, and Updating Data Sources Describing the Security Model for BI Publisher and Oracle Fusion Middleware Describing Groups, Users, Roles, and Permissions Describing Delivery Options Including Print, Fax, Email, WebDav, HTTP Server, FTP, and CUPS Describing and Configuring BI Publisher Scheduler Integrating with Oracle BI Presentation Services and Oracle Endeca Server Scheduling and Bursting Reports Scheduling and Describing a Report Job and Related Options Managing and Viewing a Report Job Viewing Report Job History Scheduling a Report with Trigger Describing Bursting Adding a Bursting Definition to a Data Model Scheduling a Bursting Job Integrating BI Publisher with Oracle BI Enterprise Edition Configuring Presentation Services Integration Navigating Oracle BI EE Creating a Report based on OBI EE Subject Area Creating a Data Model and Report based on a BI Server SQL Query Creating a Data Model and Report based on an Oracle BI Analysis Adding a BI Publisher Report to an Oracle BI EE Dashboard Creating Data Models and BI Publisher Reports Based on Other Data Sources Configuring Presentation Services Integration Describing the Web Services Data Source Describing the HTTP (XML/RSS Feed) Data Source Explaining Proxy Setting for Web Services and HTTP Data Sources Creating a BI Publisher Report based on an External Web Service Creating a BI Publisher Report based on an HTTP Data Set Creating a BI Publisher Report Based on XML File Creating a BI Publisher Report Based on CSV Data source Performing Translations Describing Translation Types Translating by Using the Localized Template Option Translating by Using the XLIFF Option Managing XLIFF Translations on BI Publisher Server Describing the Overall Translation Process Describing Catalog Translation Exporting and Importing the XLIFF for a Catalog Folder Additional course details: Nexus Humans Oracle BI Publisher 12c R1: Fundamentals training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Oracle BI Publisher 12c R1: Fundamentals course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 1 Days 6 CPD hours This course is intended for To ensure success, students should have completed Excel Essentials and Excel Functions Including Pivot Tables and Lookups or have the equivalent knowledge and experience. Overview Upon successful completion of this course, students will understand the programming environment of Visual Basic for applications and know how to create custom procedures and functions that can expand their ability to use Excel more effectively. This course is intended for the experienced Excel users that want to gain knowledge of visual basic for applications (VBA). Introduction to Macros Recording Macros Absolute and Relative How to Run a Macro Basics of Code Writing The VBA Environment (the ?VBE? window) Essential Tips, Tricks, Shortcuts Understanding VBA and Best Practice Crucial VBA Objects and Structures Variables and Data Types Conditionals (If, Select Case) Loops (For, Do) Interaction Interact with Data, Sheets, the Excel Application Interact with User (Message Box, Input Box) Errors Run Editing Debuging Handeling Errors Additional course details: Nexus Humans Excel - VBA Bootcamp training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Excel - VBA Bootcamp course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm