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OVERVIEW DIAD is a one-day, hands-on workshop for business analysts, covering the breadth of Power BI capabilities. The course focuses on five practical Labs and at the end of the day, attendees will better understand how to: Connect and transform data from a variety of data sources. Define business rules and KPIs. Explore data with powerful interactive visuals. Build stunning reports. Share their dashboards with their team business partners and publish them to the web. The course content is managed by the Power BI engineering team at Microsoft. There is no exam associated with the course. COURSE BENEFITS: Learn how to clean, transform, and load data from various sources Create and manage a data model in Power BI consisting of multiple tables connected with relationships Build Measures and other calculations in the DAX language to plot in reports Manage and share report assets to the Power BI Service WHO IS THE COURSE FOR? Data Analysts and Management Consultants with little or no experience of Power BI who wish to upgrade their knowledge to include Business Intelligence Analysts looking for a quick introduction to Power BI who don’t have the time for the full three day PL-300 course Marketers in data-intensive organisations who need new tools to build visually appealing, dynamic charts for their stakeholders to use LAB OUTLINE Lab 1 Accessing & Preparing The Data Load data from Excel and CSV sources Manipulate the data to prepare it for reporting Prepare tables in Power Query and load them into the data model Lab 2 Data Modelling And Exploration Create a range of different charts Highlight and cross-filter Create new groups and hierarchies Add new measures to the model Lab 3 Data Visualization Add conditional formatting to a report Add logos to a filter Import a custom visual Apply a custom theme Add bookmarks to the report to tell a story Lab 4 Publishing A Report And Creating A Dashboard Create a Workspace in the Power BI Service Publish a report to the Service Create a Dashboard and pin visuals to it Generate and view insights Lab 5 Collaboration Share a Dashboard Access a Dashboard on a Mobile Device

Duration 3 Days 18 CPD hours This course is intended for This course is ideal for Professionals preparing to become CRISC certified. Risk practitioners Students or recent graduates Overview At course completions, students will understand the essential concepts in the 4 ISACA CRISC domains: Governance IT Risk Assessment Risk Response and Reporting Information Technology and Security This 3 Day CRISC course is geared towards preparing students to pass the ISACA Certified in Risk and Information Systems Control examination. The course covers all four of the CRISC domains, and each section corresponds directly to the CRISC job practice. CRISC validates your experience in building a well-defined, agile risk-management program, based on best practices to identify, analyze, evaluate, assess, prioritize and respond to risks. This enhances benefits realization and delivers optimal value to stakeholders. GOVERNANCE - a. Organizational Governance Organizational Strategy, Goals, and Objectives Organizational Structure, Roles, and Responsibilities Organizational Culture Policies and Standards Business Processes Organizational Assets GOVERNANCE - b. Risk Governance Enterprise Risk Management and Risk Management Framework Three Lines of Defense Risk Profile Risk Appetite and Risk Tolerance Legal, Regulatory, and Contractual Requirements Professional Ethics of Risk Management IT RISK ASSESSMENT - a. IT Risk Identification Risk Events (e.g., contributing conditions, loss result) Threat Modelling and Threat Landscape Vulnerability and Control Deficiency Analysis (e.g., root cause analysis) Risk Scenario Development IT RISK ASSESSMENT - b. IT Risk Analysis and Evaluation Risk Assessment Concepts, Standards, and Frameworks Risk Register Risk Analysis Methodologies Business Impact Analysis Inherent and Residual Risk RISK RESPONSE AND REPORTING - a. Risk Response Risk Treatment / Risk Response Options Risk and Control Ownership Third-Party Risk Management Issue, Finding, and Exception Management Management of Emerging Risk RISK RESPONSE AND REPORTING - b. Control Design and Implementation Control Types, Standards, and Frameworks Control Design, Selection, and Analysis Control Implementation Control Testing and Effectiveness Evaluation RISK RESPONSE AND REPORTING - c. Risk Monitoring and Reporting Risk Treatment Plans Data Collection, Aggregation, Analysis, and Validation Risk and Control Monitoring Techniques Risk and Control Reporting Techniques (heatmap, scorecards, dashboards) Key Performance Indicators Key Risk Indicators (KRIs) Key Control Indicators (KCIs) INFORMATION TECHNOLOGY AND SECURITY - a. Information Technology Principles Enterprise Architecture IT Operations Management (e.g., change management, IT assets, problems, incidents) Project Management Disaster Recovery Management (DRM) Data Lifecycle Management System Development Life Cycle (SDLC) Emerging Technologies INFORMATION TECHNOLOGY AND SECURITY - b. Information Security Principles Information Security Concepts, Frameworks, and Standards Information Security Awareness Training Business Continuity Management Data Privacy and Data Protection Principles

Data has become the most important resource for every organisation – but the insights gained from data analysis will only ever be truly valuable if they can be clearly expressed to other people.  This course is for anybody who works with data, and needs to communicate the meaning that's in the numbers to colleagues, customers, bosses or external stakeholders.  It will give you or your team the confidence and skills to translate raw data into compelling visual stories for your key audiences. The principles and skills covered apply to the simplest PowerPoint chart, to more complex interactive visualisations.  We’ll work with you before the course to ensure that we understand your organisation and what you’re hoping to achieve.  Sample learning content Session 1: What makes a great data-driven story The key elements of a successful infographic or presentation. Industry best practice, and discussion of good (and bad) examples. A simple framework for identifying the Audience, Story and Action. Session 2: Data in context How to balance function and aesthetic appeal. Identifying the right graph, chart, infographic or other visual. Framing the data and providing contextual information. Session 3: Designing for the human brain Using colours to add emphasis and meaning. Design and layout principles, and creating hierarchies of information. The principle of ‘self-sufficiency’, and removing clutter. Session 4: Navigation and narrative Tailoring visualisations for different types of communications. Structuring presentations and longer reports. Thinking in layers to create interactive dashboards. Delivery We deliver our courses over Zoom, to maximise flexibility. The training can be delivered in a single day, or across multiple sessions. All of our courses are live and interactive – every session includes a mix of formal tuition and hands-on exercises. To ensure this is possible, the number of attendees is capped at 16 people.  Tutor Alan Rutter is the founder of Fire Plus Algebra. He is a specialist in communicating complex subjects through data visualisation, writing and design. He teaches for General Assembly and runs in-house training for public sector clients including the Home Office, the Department of Transport, the Biotechnology and Biological Sciences Research Council, the Health Foundation, and numerous local government and emergency services teams. He previously worked with Guardian Masterclasses on curating and delivering new course strands, including developing and teaching their B2B data visualisation courses. He oversaw the iPad edition launches of Wired, GQ, Vanity Fair and Vogue in the UK, and has worked with Condé Nast International as product owner on a bespoke digital asset management system for their 11 global markets.
 Testimonial “I was familiar with Alan’s work as a Guardian Masterclass instructor on data visualisation and digital journalism, which made it easy for me to recommend him for onsite training at the Liverpool School of Tropical Medicine. We had a large group of people interested in honing their abilities to depict their research and stories in engaging ways. Alan’s course provided great insight about common communication pitfalls and how to avoid them, how to become better communicators by understanding the audience diversity, and it showcased some great online tools for creating infographics. This should be mandatory training for all students, academics, report writers and those involved with conveying research to the media as it will help increase the clarity and accessibility of our own research stories.”
 Dr Lee Haines | Liverpool School of Tropical Medicine

Duration 3 Days 18 CPD hours This course is intended for System administrators and operators who are operating in the AWS Cloud Informational technology workers who want to increase the system operations knowledge. Overview Identify the AWS services that support the different phases of Operational Excellence, an AWS Well-Architected Framework pillar Manage access to AWS resources using AWS accounts and organizations and AWS Identity and Access Management (IAM) Maintain an inventory of in-use AWS resources by using AWS services, such as AWS Systems Manager, AWS CloudTrail, and AWS Config Develop a resource deployment strategy using metadata tags, Amazon Machine Images (AMIs), and AWS Control Tower to deploy and maintain an AWS cloud environment Automate resource deployment by using AWS services, such as AWS CloudFormation and AWS Service Catalog Use AWS services to manage AWS resources through CloudOps lifecycle processes, such as deployments and patches Configure a highly available cloud environment that uses AWS services, such as Amazon Route 53 and Elastic Load Balancing, to route traffic for optimal latency and performance Configure AWS Auto Scaling and Amazon EC2 Auto Scaling to scale out your cloud environment based on demand Use Amazon CloudWatch and associated features, such as alarms, dashboards, and widgets, to monitor your cloud environment Manage permissions and track activity in your cloud environment by using AWS services, such as AWS CloudTrail and AWS Config Deploy your resources to an Amazon Virtual Private Cloud (Amazon VPC), establish necessary connectivity to your Amazon VPC, and protect your resources from disruptions of service State the purpose, benefits, and appropriate use cases for mountable storage in your AWS Cloud environment Explain the operational characteristics of object storage in the AWS Cloud, including Amazon Simple Storage Service (Amazon S3) and Amazon S3 Glacier Build a comprehensive cost model to help gather, optimize, and predict your cloud costs by using services such as AWS Cost Explorer and the AWS Cost & Usage Report This course teaches systems operators and anyone performing cloud operations functions how to manage and operate automatable and repeatable deployments of networks and systems on AWS. You will learn about cloud operations functions, such as installing, configuring, automating, monitoring, securing, maintaining, and troubleshooting these services, networks, and systems. The course also covers specific AWS features, tools, and best practices related to these functions. Prerequisites Successfully completed the AWS Technical Essentials course Background in either software development or systems administration Proficiency in maintaining operating systems at the command line, such as shell scripting in Linux environments or cmd/PowerShell in Windows Basic knowledge of networking protocols (TCP/IP, HTTP) 1 - Introduction to Cloud Operations on AWS What is Cloud Operations AWS Well-Architected Framework AWS Well-Architected Tool 2 - Access Management AWS Identity and Access Management (IAM) Resources, accounts, and AWS Organizations 3 - System Discovery Methods to interact with AWS services Tools for automating resource discovery Inventory with AWS Systems Manager and AWS Config Hands-On Lab: Auditing AWS Resources with AWS Systems Manager and AWS Config 4 - Deploy and Update Resources Cloud Operations in deployments Tagging strategies Deployment using Amazon Machine Images (AMIs) Deployment using AWS Control Tower 5 - Automate Resource Deployment Deployment using AWS CloudFormation Deployment using AWS Service Catalog Hands-On Lab: Infrastructure as Code 6 - Manage Resources AWS Systems Manager Hands-On Lab: Operations as Code 7 - Configure Highly Available Systems Distributing traffic with Elastic Load Balancing Amazon Route 53 8 - Automate Scaling Scaling with AWS Auto Scaling Scaling with Spot Instances Managing licenses with AWS License Manager 9 - Monitor and Maintain System Health Monitoring and maintaining healthy workloads Monitoring AWS infrastructure Monitoring applications Hands-On Lab: Monitor Applications and Infrastructure 10 - Data Security and System Auditing Maintaining a strong identity and access foundation Implementing detection mechanisms Automating incident remediation 11 - Operate Secure and Resilient Networks Building a secure Amazon Virtual Private Cloud (Amazon VPC) Networking beyond the VPC 12 - Mountable Storage Configuring Amazon Elastic Block Store (Amazon EBS) Sizing Amazon EBS volumes for performance Using Amazon EBS snapshots Using Amazon Data Lifecycle Manager to manage your AWS resources Creating backup and data recovery plans Configuring shared file system storage Hands-On Lab: Automating with AWS Backup for Archiving and Recovery 13 - Object Storage Deploying Amazon Simple Storage Service (Amazon S3) Managing storage lifecycles on Amazon S3 14 - Cost Reporting, Alerts, and Optimization Gaining AWS cost awareness Using control mechanisms for cost management Optimizing your AWS spend and usage Hands-On Lab: Capstone lab for CloudOps Additional course details: Nexus Humans Cloud Operations on AWS 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 Cloud Operations on AWS 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 The audience for this course are data professionals and business intelligence professionals who want to learn how to accurately perform data analysis using Power BI. This course is also targeted toward those individuals who develop reports that visualize data from the data platform technologies that exist on both in the cloud and on-premises. This course covers the various methods and best practices that are in line with business and technical requirements for modeling, visualizing, and analyzing data with Power BI. The course will show how to access and process data from a range of data sources including both relational and non-relational sources. Finally, this course will also discuss how to manage and deploy reports and dashboards for sharing and content distribution. Prerequisites Understanding core data concepts. Knowledge of working with relational data in the cloud. Knowledge of working with non-relational data in the cloud. Knowledge of data analysis and visualization concepts. DP-900T00 Microsoft Azure Data Fundamentals is recommended 1 - Discover data analysis Overview of data analysis Roles in data Tasks of a data analyst 2 - Get started building with Power BI Use Power BI Building blocks of Power BI Tour and use the Power BI service 3 - Get data in Power BI Get data from files Get data from relational data sources Create dynamic reports with parameters Get data from a NoSQL database Get data from online services Select a storage mode Get data from Azure Analysis Services Fix performance issues Resolve data import errors 4 - Clean, transform, and load data in Power BI Shape the initial data Simplify the data structure Evaluate and change column data types Combine multiple tables into a single table Profile data in Power BI Use Advanced Editor to modify M code 5 - Design a semantic model in Power BI Work with tables Create a date table Work with dimensions Define data granularity Work with relationships and cardinality Resolve modeling challenges 6 - Add measures to Power BI Desktop models Create simple measures Create compound measures Create quick measures Compare calculated columns with measures 7 - Add calculated tables and columns to Power BI Desktop models Create calculated columns Learn about row context Choose a technique to add a column 8 - Use DAX time intelligence functions in Power BI Desktop models Use DAX time intelligence functions Additional time intelligence calculations 9 - Optimize a model for performance in Power BI Review performance of measures, relationships, and visuals Use variables to improve performance and troubleshooting Reduce cardinality Optimize DirectQuery models with table level storage Create and manage aggregations 10 - Design Power BI reports Design the analytical report layout Design visually appealing reports Report objects Select report visuals Select report visuals to suit the report layout Format and configure visualizations Work with key performance indicators 11 - Configure Power BI report filters Apply filters to the report structure Apply filters with slicers Design reports with advanced filtering techniques Consumption-time filtering Select report filter techniques Case study - Configure report filters based on feedback 12 - Enhance Power BI report designs for the user experience Design reports to show details Design reports to highlight values Design reports that behave like apps Work with bookmarks Design reports for navigation Work with visual headers Design reports with built-in assistance Tune report performance Optimize reports for mobile use 13 - Perform analytics in Power BI Explore statistical summary Identify outliers with Power BI visuals Group and bin data for analysis Apply clustering techniques Conduct time series analysis Use the Analyze feature Create what-if parameters Use specialized visuals 14 - Create and manage workspaces in Power BI Distribute a report or dashboard Monitor usage and performance Recommend a development life cycle strategy Troubleshoot data by viewing its lineage Configure data protection 15 - Manage semantic models in Power BI Use a Power BI gateway to connect to on-premises data sources Configure a semantic model scheduled refresh Configure incremental refresh settings Manage and promote semantic models Troubleshoot service connectivity Boost performance with query caching (Premium) 16 - Create dashboards in Power BI Configure data alerts Explore data by asking questions Review Quick insights Add a dashboard theme Pin a live report page to a dashboard Configure a real-time dashboard Set mobile view 17 - Implement row-level security Configure row-level security with the static method Configure row-level security with the dynamic method Additional course details: Nexus Humans PL-300T00: Microsoft Power BI Data Analyst 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 PL-300T00: Microsoft Power BI Data Analyst 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 4 Days 24 CPD hours This course is intended for This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. Overview Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow. Prerequisites Creating cloud resources in Microsoft Azure. Using Python to explore and visualize data. Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow. Working with containers AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience. 1 - Design a data ingestion strategy for machine learning projects Identify your data source and format Choose how to serve data to machine learning workflows Design a data ingestion solution 2 - Design a machine learning model training solution Identify machine learning tasks Choose a service to train a machine learning model Decide between compute options 3 - Design a model deployment solution Understand how model will be consumed Decide on real-time or batch deployment 4 - Design a machine learning operations solution Explore an MLOps architecture Design for monitoring Design for retraining 5 - Explore Azure Machine Learning workspace resources and assets Create an Azure Machine Learning workspace Identify Azure Machine Learning resources Identify Azure Machine Learning assets Train models in the workspace 6 - Explore developer tools for workspace interaction Explore the studio Explore the Python SDK Explore the CLI 7 - Make data available in Azure Machine Learning Understand URIs Create a datastore Create a data asset 8 - Work with compute targets in Azure Machine Learning Choose the appropriate compute target Create and use a compute instance Create and use a compute cluster 9 - Work with environments in Azure Machine Learning Understand environments Explore and use curated environments Create and use custom environments 10 - Find the best classification model with Automated Machine Learning Preprocess data and configure featurization Run an Automated Machine Learning experiment Evaluate and compare models 11 - Track model training in Jupyter notebooks with MLflow Configure MLflow for model tracking in notebooks Train and track models in notebooks 12 - Run a training script as a command job in Azure Machine Learning Convert a notebook to a script Run a script as a command job Use parameters in a command job 13 - Track model training with MLflow in jobs Track metrics with MLflow View metrics and evaluate models 14 - Perform hyperparameter tuning with Azure Machine Learning Define a search space Configure a sampling method Configure early termination Use a sweep job for hyperparameter tuning 15 - Run pipelines in Azure Machine Learning Create components Create a pipeline Run a pipeline job 16 - Register an MLflow model in Azure Machine Learning Log models with MLflow Understand the MLflow model format Register an MLflow model 17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning Understand Responsible AI Create the Responsible AI dashboard Evaluate the Responsible AI dashboard 18 - Deploy a model to a managed online endpoint Explore managed online endpoints Deploy your MLflow model to a managed online endpoint Deploy a model to a managed online endpoint Test managed online endpoints 19 - Deploy a model to a batch endpoint Understand and create batch endpoints Deploy your MLflow model to a batch endpoint Deploy a custom model to a batch endpoint Invoke and troubleshoot batch endpoints

Advanced Kibana training course description This training course is aimed at users who already have some experience with Kibana, who are looking to further their knowledge. What will you learn Lens Timelion Maps Custom Visualisations with Vega Canvas Filters and Controls Drilldown and Dashboards KQSL and ElasticQueries Scripted and RunTime Fields Alerts and Alarms Advanced Kibana training course details Who will benefit: Users who already have some experience with Kibana, who are looking to further their knowledge. Prerequisites: None Duration 1 day Advanced Kibana training course contents Topics Lens Visualisation types (tables,bars,charts) Category breakdown Adding multiple metrics Using formulas in metrics Labels Adding reference layer Limitations Visualise Library Timeseries, Metrics Different types of aggregations Maps GeoMapping Heat Maps Using ES index as data source Visualisation, tool tips Custom Visualisations with Vega Introduction to vega scripting Canvas Widgets and Texts Elasticsearch SQL Canvas Expressions Filters and Controls Dropdown filters Ad-hoc filters Searchbar filters Drilldown Dashboards Linking one dashboard to another KQSL and ElasticQueries Bool Query AND/OR Phrase Part match vs keyword search Wildcard search Scripted and RunTime Fields Creating ad-hoc calculated fields using scripts Performance issues Alerts and Alarms Query Based Formatting output Connector types(email,index,teams etc)

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Duration 2 Days 12 CPD hours Overview What is JIRA Software? Managing agile projects using JIRA Software Managing project backlog Managing iterations / sprints Managing releases / versions Managing project components Managing security Managing fields and screens Managing custom issue types Viewing various burn-down/burn-up reports This course introduces students to JIRA Software which is one of the most popular agile project management tool. Agile methods help in accelerating the delivery of initial business value. Continuous planning and feedback ensures that value is maximized throughout the development process. JIRA Software lets you manage project backlog, plan and execute sprints, and manage releases. It also lets you view useful reports, such as, velocity, various burndown / burn-up charts. Navigating JIRA Connecting to JIRA Software JIRA Account System Dashboard Sidebars Global Sidebar Search Help Dashboards Projects Boards Issues Project Sidebar Summary Managing Projects What is a Project? What is a Project (Contd.)? Backlog Sprints Versions / Releases Issues What is Component? Project Name and Key Project Key Format Editing Project Key Caveats Editing Project Key Deleting Project Summary Managing Versions What is Version What is Version (Contd.)? Merging Versions Other Version Options Version Fields What is Version? Summary Managing Issues Issues What are Epics? Epics ? Types Creating a new Epic What is a Story? Creating a Story Story Estimation Tasks Sub-tasks Summary Managing Sprints Sprints What is typically done in Sprint Planning? Velocity Agile Board Sprint Naming Convention Sprint Execution Summary Search & Using JQL Search Search Types JQL JQL Examples Sharing search result Save Search and Reuse in a Board Summary Working with JIRA Dashboards and Reports What is a JIRA Dashboard? Creating a JIRA Dashboard Choosing a Dashboard Layout What is a Gadget? Gadgets Available Out-of-the-box Adding a Gadget to a Dashboard Adding a Gadget to a Dashboard (Example Calendar Gadget) Moving a Gadget Removing a Gadget from a Dashboard Viewing Dashboard as a Wallboard Deleting a Dashboard JIRA Reports Generating a JIRA Report Generating a JIRA Report (Example ? Burndown Chart) Viewing the Burndown Chart Report Categories Available Out-of-the-box Agile Reports Issue Analysis Reports Forecast & Management Reports For further details ? Summary Jira Agile Common Jira Software boards Scrum Agility Kanban Scrum vs. Kanban Scrumban History of Kanban Kanban for software teams Kanban boards Kanban boards (Contd.) Kanban cards The benefits of Kanban Planning flexibility Shortened time cycles Fewer bottlenecks Visual metrics Continuous Delivery Kanban ? Kanban backlog Summary Miscellaneous Issue Features Voting Watching an Issue Adding/Removing Labels Linking Issues Linking Issue (Contd.) Commenting on Issue Attaching a File to an Issue Attaching a File to an Issue (Contd.) Cloning (Copying) an Issue Cloning (Copying) an Issue (Contd.) Cloning (copying) an Issue (Contd.) Viewing an Issue?s Change History Viewing an Issue's Change History (Contd. Summary Managing Fields & Screens (OPTIONAL: TIME PERMITTING) Fields OOB Fields Custom Fields Field Type Screens Summary

Duration 2 Days 12 CPD hours Overview Identify and configure basic functions of Tableau. Connect to data sources, import data into Tableau, and save Tableau files Create views and customize data in visualizations. Manage, sort, and group data. Save and share data sources and workbooks. Filter data in views. Customize visualizations with annotations, highlights, and advanced features. Create and enhance dashboards in Tableau. Create and enhance stories in Tableau As technology progresses and becomes more interwoven with our businesses and lives, more and more data is collected about business and personal activities. This era of "big data" has exploded due to the rise of cloud computing, which provides an abundance of computational power and storage, allowing organizations of all sorts to capture and store data. Leveraging that data effectively can provide timely insights and competitive advantage. The creation of data-backed visualizations is a key way data scientists, or any professional, can explore, analyze, and report insights and trends from data. Tableau© software is designed for this purpose. Tableau was built to connect to a wide range of data sources and allows users to quickly create visualizations of connected data to gain insights, show trends, and create reports. Tableau's data connection capabilities and visualization features go far beyond those that can be found in spreadsheets, allowing users to create compelling and interactive worksheets, dashboards, and stories that bring data to life and turn data into thoughtful action. Prerequisites To ensure your success in this course, you should have experience managing data with Microsoft© Excel© or Google Sheets?. Lesson 1: Tableau Fundamentals Topic A: Overview of Tableau Topic B: Navigate and Configure Tableau Lesson 2: Connecting to and Preparing Data Topic A: Connect to Data Topic B: Build a Data Model Topic C: Save Workbook Files Topic D: Prepare Data for Analysis Lesson 3: Exploring Data Topic A: Create Views Topic B: Customize Data in Visualizations Lesson 4: Managing, Sorting, and Grouping Data Topic A: Adjust Fields Topic B: Sort Data Topic C: Group Data Lesson 5: Saving, Publishing, and Sharing Data Topic A: Save Data Sources Topic B: Publish Data Sources and Visualizations Topic C: Share Workbooks for Collaboration Lesson 6: Filtering Data Topic A: Configure Worksheet Filters Topic B: Apply Advanced Filter Options Topic C: Create Interactive Filters Lesson 7: Customizing Visualizations Topic A: Format and Annotate Views Topic B: Emphasize Data in Visualizations Topic C: Create Animated Workbooks Topic D: Best Practices for Visual Design Lesson 8: Creating Dashboards in Tableau Topic A: Create Dashboards Topic B: Enhance Dashboards with Actions Topic C: Create Mobile Dashboards Lesson 9: Creating Stories in Tableau Topic A: Create Stories Topic B: Enhance Stories with Tooltips