489 Intelligence courses in Liverpool delivered Live Online

Duration 2 Days 12 CPD hours This course is intended for This course is relevant to anyone who needs to work with and understand data including: Business Analysts, Data Analysts, Reporting and BI professionals Marketing and Digital Marketing professionals Digital, Web, e-Commerce, Social media and Mobile channel professionals Business managers who need to interpret analytical output to inform managerial decisions Overview This course will cover the basic theory of data visualization along with practical skills for creating compelling visualizations, reports and dashboards from data using Tableau. Outcome: After attending this course delegates will understand - How to move from business questions to great data visualizations and beyond How to apply the fundamentals of data visualization to create informative charts How to choose the right visualization type for the job at hand How to design and develop basic dashboards in Tableau that people will love to use by doing the following: Reading data sources into Tableau Setting up the roles and data types for your analysis Creating new data fields using a range of calculation types Creating the following types of charts - cross tabs, pie and bar charts, geographic maps, dual axis and combo charts, heat maps, highlight tables, tree maps and scatter plots Creating Dashboards that delight using the all of the features available in Tableau. The use of analytics, statistics and data science in business has grown massively in recent years. Harnessing the power of data is opening actionable insights in diverse industries from banking to tourism. From Business Questions to Data Visualisation and Beyond The first step in any data analysis project is to move from a business question to data analysis and then on to a complete solution. This section will examine this conversion emphasizing: The use of data visualization to address a business need The data analytics process ? from business questions to developed dashboards Introduction to Tableau ? Part 1 In this section, the main functionality of Tableau will be explained including: Selecting and loading your data Defining data item properties Create basic calculations including basic arithmetic calculations, custom aggregations and ratios, date math, and quick table calculations Creating basic visualizations Creating a basic dashboard Introduction to Tableau ? Part 2 In this section, the main functionality of Tableau will be explained including: Selecting and loading your data Defining data item properties Create basic calculations including basic arithmetic calculations, custom aggregations and ratios, date math, and quick table calculations Creating basic visualizations Creating a basic dashboard Key Components of Good Data Visualisation and The Visualisation Zoo In this section the following topics will be covered: Colour theory Graphical perception & communication Choosing the right chart for the right job Data Exploration with Tableau Exploring data to answer business questions is one of the key uses of applying good data visualization techniques within Tableau. In this section we will apply the data visualization theory from the previous section within Tableau to uncover trends within the data to answer specific business questions. The types of charts that will be covered are: Cross Tabs Pie and bar charts Geographic maps Dual axis and combo charts with different mark types Heat maps Highlight tables Tree maps Scatter plots Introduction to Building Dashboards with Tableau In this section, we will implement the full process from business question to final basic dashboard in Tableau: Introduction to good dashboard design Building dashboards in Tableau

Duration 2 Days 12 CPD hours This course is intended for This course is primarily for Application Consultants, Business Analysts, Business Process Owners/Team Leaders/Power Users, and Developer Consultants. Overview At course completion students will know- The basic functions and navigation options of BusinessObjects Analysis for Microsoft Office- The special functions and layout design options of BusinessObjects Analysis for Microsoft Office In this course, students learn the basic functions and navigation options of the Analysis edition for Microsoft Office. Students will also learn the special functions and layout design options of Analysis. Components and Data Sources for Analysis Using Analysis Components and Data Sources Customization for Workbook Data Analysis Using the Basic Components of Analysis Sorting and Filtering Workbook Members Filtering Selected Workbook Members by Measure for Enhanced Analysis Using Hierarchies for Data Analysis in Workbooks Using Inserted Components to Add Workbook Functionality Configuring Filter Components Using Formulas to Enhance the Workbook Layout Defining Conditional Formatting Options for Workbooks Using a Prompting Dialog in Workbook Queries Extending Workbook Display Options with Functions and Microsoft Excel VBA Using Styles to Customize Workbook Appearance Setting Preferences to Control Workbook Behavior Publishing Analysis Documents to the BI Platform Server Presentation of Workbook Analysis Data Presenting Analysis Data for Business Users Additional course details: Nexus Humans BOAN10 SAP BusinessObjects Analysis for Microsoft Office 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 BOAN10 SAP BusinessObjects Analysis for Microsoft Office 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 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 2 Days 12 CPD hours This course is intended for IBM SPSS Statistics users who want to familiarize themselves with the statistical capabilities of IBM SPSS StatisticsBase. Anyone who wants to refresh their knowledge and statistical experience. Overview Introduction to statistical analysis Describing individual variables Testing hypotheses Testing hypotheses on individual variables Testing on the relationship between categorical variables Testing on the difference between two group means Testing on differences between more than two group means Testing on the relationship between scale variables Predicting a scale variable: Regression Introduction to Bayesian statistics Overview of multivariate procedures This course provides an application-oriented introduction to the statistical component of IBM SPSS Statistics. Students will review several statistical techniques and discuss situations in which they would use each technique, how to set up the analysis, and how to interpret the results. This includes a broad range of techniques for exploring and summarizing data, as well as investigating and testing relationships. Students will gain an understanding of when and why to use these various techniques and how to apply them with confidence, interpret their output, and graphically display the results. Introduction to statistical analysis Identify the steps in the research process Identify measurement levels Describing individual variables Chart individual variables Summarize individual variables Identify the normal distributionIdentify standardized scores Testing hypotheses Principles of statistical testing One-sided versus two-sided testingType I, type II errors and power Testing hypotheses on individual variables Identify population parameters and sample statistics Examine the distribution of the sample mean Test a hypothesis on the population mean Construct confidence intervals Tests on a single variable Testing on the relationship between categorical variables Chart the relationship Describe the relationship Test the hypothesis of independence Assumptions Identify differences between the groups Measure the strength of the association Testing on the difference between two group meansChart the relationship Describe the relationship Test the hypothesis of two equal group means Assumptions Testing on differences between more than two group means Chart the relationship Describe the relationship Test the hypothesis of all group means being equal Assumptions Identify differences between the group means Testing on the relationship between scale variables Chart the relationship Describe the relationship Test the hypothesis of independence Assumptions Treatment of missing values Predicting a scale variable: Regression Explain linear regression Identify unstandardized and standardized coefficients Assess the fit Examine residuals Include 0-1 independent variables Include categorical independent variables Introduction to Bayesian statistics Bayesian statistics and classical test theory The Bayesian approach Evaluate a null hypothesis Overview of Bayesian procedures in IBM SPSS Statistics Overview of multivariate procedures Overview of supervised models Overview of models to create natural groupings

Duration 2 Days 12 CPD hours This course is intended for This course is suited to marketeers, business analysts, and researchers who are interested in increasing their statistical knowledge. Overview After attending this course, delegates will understand how statistics can be used to provide valuable insight into their business, and be able to apply statistical methods to solve business problems. On returning to work delegates will immediately be able to make a difference to the way that their organisations make decisions. This course covers the statistical methods that analysts need to move from simple reporting on business problems to extracting insight to solve business problems. Course Outline The course will explore the following topics through a series of lectures and workshops: Summary statistics for both continuous data and categorical data Using and reporting confidence intervals Using hypothesis tests to answer business questions Using correlations to explore data relationships Simple prediction models Analysing categorical data Additional course details: Nexus Humans Data-driven Business Using Statistical Analysis 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 Data-driven Business Using Statistical Analysis 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 This course is geared for Python experienced developers, analysts or others who are intending to learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web. Overview Working in a hands-on lab environment led by our expert instructor, attendees will Understand the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250 Clone Build a content-based engine to recommend movies based on real movie metadata Employ data-mining techniques used in building recommenders Build industry-standard collaborative filters using powerful algorithms Building Hybrid Recommenders that incorporate content based and collaborative filtering Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether its friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform.This course shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory?you will get started with building and learning about recommenders as quickly as possible. In this course, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You will also use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques.Students will learn to build industry-standard recommender systems, leveraging basic Python syntax skills. This is an applied course, so machine learning theory is only used to highlight how to build recommenders in this course.This skills-focused ccombines engaging lecture, demos, group activities and discussions with machine-based student labs and exercises.. Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern 'on-the-job' modern applied datascience, AI and machine learning experience into every classroom and hands-on project. Getting Started with Recommender Systems Technical requirements What is a recommender system? Types of recommender systems Manipulating Data with the Pandas Library Technical requirements Setting up the environment The Pandas library The Pandas DataFrame The Pandas Series Building an IMDB Top 250 Clone with Pandas Technical requirements The simple recommender The knowledge-based recommender Building Content-Based Recommenders Technical requirements Exporting the clean DataFrame Document vectors The cosine similarity score Plot description-based recommender Metadata-based recommender Suggestions for improvements Getting Started with Data Mining Techniques Problem statement Similarity measures Clustering Dimensionality reduction Supervised learning Evaluation metrics Building Collaborative Filters Technical requirements The framework User-based collaborative filtering Item-based collaborative filtering Model-based approaches Hybrid Recommenders Technical requirements Introduction Case study and final project ? Building a hybrid model Additional course details: Nexus Humans Applied AI: Building Recommendation Systems with Python (TTAI2360) 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 Applied AI: Building Recommendation Systems with Python (TTAI2360) 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 It is appropriate for Managers, Executives, Project Managers, Business Analysts, Business and IT stakeholders working with analysts, Quality and process engineers, technicians, managers; supervisors, team leaders, and process operators. Overview Describe business process improvement (BPI) business drivers.Plan, manage and close requirements for a Business Process Improvement project Understand the essential elements of a successful BPI initiative.Identify candidate business processes for improvement.Understand the essential elements of a successful BPI initiative.Identify candidate business processes for improvement.Apply a methodology to business process improvement projects. This 2-day course aims at introducing its attendees to the core values, principles, and practices of Business Process Improvement. Introduction - A Business Process Improvement (BPI) Overview Why are we here today? What is BPI? Benefits of BPI Specific challenges/obstacles and successes Process improvement examples: Industry specific examples Famous debacles to avoid and successes to emulate Your role in helping to identify problems Overview of the Joiner 7-Step Method What is the Joiner 7-Step Method? Walkthrough of the Joiner 7-Step Method Template: Introduce and review Process Improvement Template Case Study Exercise: Read and discuss introduction to the Case Study Step #1: Initiate the Project Types of business problems typically encountered at insurance companies and banks How to recognize a business-related problem Identifying the gaps (delta between current and future states) Ownership of the project and the business problem Defining measurable success criteria Case Study Exercise: Complete the Problem Statement section (Step #1) of the template Step #2: Define Current Situation What are symptoms of a problem? Looking for symptoms of the problem Performing Stakeholder Analysis Technique: View a RACI Matrix Defining the impacts caused by the problem Technique: Business Process Modeling (As-Is) Understand how to draw an As-Is Business Process Model Case Study Exercise: Complete the Define Current Situation section (Step #2) of the template Step #3: Identify Root Causes What are root causes? Performing Root Cause Analysis Technique: Fishbone Diagram using the cafeteria example Case Study Exercise: Discuss a Fishbone Diagram Technique: Pareto Chart (discuss and show example) Case Study Exercise: Complete the Identify Root Causes section (Step #3) of the template Step #4: Develop Solutions Identifying options for problem resolution Avoid jumping to conclusions Technique: Brainstorming Case Study Exercise: Conduct a Brainstorming Session Recognizing pros and cons for each option Technique: Kempner-Tregoe (?Must-Have? vs. ?Nice-to-Have?) Case Study Exercise: Determine best solution using a ?simple? Kempner-Tregoe model Case Study Exercise: Complete the Develop Solutions section (Step #4) of the template Step #5: Define Measurable Results Prototyping the solution Technique: Business Process Modeling (To-Be) Measuring results against the success criteria (Step #1) Case Study Exercise: Review changes to an As-Is Business Process Model Case Study Exercise: Complete the Define Measurable Results section (Step #5) of the template Step #6: Standardize Process Defining how the process will be documented Plan and understand organizational readiness Discuss how employees are empowered to identify and act upon their ideas Identifying follow-up needs (i.e., training) for the staff that will be impacted Technique: Communication Plan Case Study Exercise: Complete the Standardize Process section (Step #6) of the template Step #7: Determine Future Plans Monitoring the process for Continuous Process Improvement (The ?Plan-Do-Check-Act? Cycle) Understand how to sustain the improvements made by the Joiner 7-Step Method Technique: PDCA form Case Study Exercise: Complete the Determine Future Plans section (Step #7) of the template Going Forward with a Plan of Action Identifying process problems in your organization Individual Exercise: Name three (3) possible areas for improvement Prioritize and define the next steps Individual Exercise: Using a new template complete Step 2 & Step 3 for one possible area for improvement you have identified

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python-experienced attendees who wish to be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to: Understand how data analysts and scientists gather and analyze data Perform data analysis and data wrangling using Python Combine, group, and aggregate data from multiple sources Create data visualizations with pandas, matplotlib, and seaborn Apply machine learning (ML) algorithms to identify patterns and make predictions Use Python data science libraries to analyze real-world datasets Use pandas to solve common data representation and analysis problems Build Python scripts, modules, and packages for reusable analysis code Perform efficient data analysis and manipulation tasks using pandas Apply pandas to different real-world domains with the help of step-by-step demonstrations Get accustomed to using pandas as an effective data exploration tool. Data analysis has become a necessary skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. Geared for data team members with incoming Python scripting experience, Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will be able to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding lessons, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. Students will leave the course armed with the skills required to use pandas to ensure the veracity of their data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Introduction to Data Analysis Fundamentals of data analysis Statistical foundations Setting up a virtual environment Working with Pandas DataFrames Pandas data structures Bringing data into a pandas DataFrame Inspecting a DataFrame object Grabbing subsets of the data Adding and removing data Data Wrangling with Pandas What is data wrangling? Collecting temperature data Cleaning up the data Restructuring the data Handling duplicate, missing, or invalid data Aggregating Pandas DataFrames Database-style operations on DataFrames DataFrame operations Aggregations with pandas and numpy Time series Visualizing Data with Pandas and Matplotlib An introduction to matplotlib Plotting with pandas The pandas.plotting subpackage Plotting with Seaborn and Customization Techniques Utilizing seaborn for advanced plotting Formatting Customizing visualizations Financial Analysis - Bitcoin and the Stock Market Building a Python package Data extraction with pandas Exploratory data analysis Technical analysis of financial instruments Modeling performance Rule-Based Anomaly Detection Simulating login attempts Exploratory data analysis Rule-based anomaly detection Getting Started with Machine Learning in Python Learning the lingo Exploratory data analysis Preprocessing data Clustering Regression Classification Making Better Predictions - Optimizing Models Hyperparameter tuning with grid search Feature engineering Ensemble methods Inspecting classification prediction confidence Addressing class imbalance Regularization Machine Learning Anomaly Detection Exploring the data Unsupervised methods Supervised methods Online learning The Road Ahead Data resources Practicing working with data Python practice

Learn how to use this powerful tool to import and clean data and create some amazing visuals. Course overview Duration: 2 days (13 hours) Power BI Desktop is a powerful tool for working with your data. It enables you to import multiple data sources and create effective visualisations and reports. This course is an introduction to Power BI to get you started on creating a powerful reporting capability. You should have a good working knowledge of Excel and managing data before attending. Objectives  By the end of the course you will be able to: Import data from multiple data sources Edit and transform data before importing Create reports Create different visualisations Create data models Build data relationships Use the drill down features Create measures Use the Power BI Service Build dashboards Use the mobile app Content Essentials Importing Data Power BI Overview Data sources Importing data Transforming Your Data Editing your data Setting data types Removing columns/rows Choosing columns to keep Setting header rows Splitting columns Creating Reports Creating and saving reports Adding pages Renaming pages Interactivity Refreshing your data Adding Columns Columns from example Custom columns Conditional columns Append Queries Importing folders Setting up and using append queries Creating Chart Visualisations Adding chart elements Choosing chart types Setting properties Setting values, axis and legends Using tooltips Visual filters Setting page and report filters Creating Tables, Cards, Gauges and Maps Adding table elements Adding maps Working with cards Working with matrices KPIs and Gauges Conditional Formatting Setting rules Removing conditional formatting   Working with Data Models Merge Queries Setting up and using merge queries Merging in columns of data Creating a Data Model The data model Multiple data tables Connecting tables Building relationships Relationship types Building visuals from multiple tables Unpivoting Data Working with summary data Unpivoting data Using Hierarchies Using built in hierarchies Drill down Drill up See next level Expand a hierarchy Create a new hierarchy Grouping Grouping text fields Grouping date and number fields Creating Measures DAX functions DAX syntax Creating a new measure Using quick measures Using the PowerBI Service Shared workspaces My workspace Dashboards Reports Datasets Drill down in dashboards Focus mode Using Q&A Refreshing data Using Quick Insights Power BI Mobile App Using the Power BI Mobile App

Expand your Power BI knowledge and take your reports to the next level. Course overview Duration: 1 day (6.5 hours) This course is aimed at existing users who want to expand their skills to use advanced reporting techniques and use DAX to create calculated columns and measures. Participants should have either attended our Power BI – Introduction course or have equivalent knowledge. You should be able to import and transform data and create simple reports. Objectives  By the end of the course you will be able to: Import and connect data tables Create and use date calendars Create calculated columns Create and use measures Use drill down and drill through Create Tooltip pages Add and customise slicers Add action buttons Streamline your report for use in the Power BI Service Content Review of importing and loading data Importing data Transforming data Adding custom columns Creating data models Building visuals Creating date calendars Building date tables Creating Financial Year information Including Month and Day information Creating calculated columns Power Query custom columns vs DAX columns Creating DAX calculated columns Creating measures Implicit vs Explicit Measures Building measures Using DAX Common DAX functions Drill Down vs Drill Through Review of drill down Creating drill through pages Using drill through Creating ToolTips Pages Adding pages to use for Tooltips Linking ToolTip pages to visuals Using action buttons Adding images Adding buttons Setting actions Working with slicers Adding slicers Changing slicer settings Syncing slicers between pages Showing what has been sliced Setting slicer interactions Techniques in the Power BI Service Hiding the navigation bar Stopping users manually filtering