Data-Informed Decision Making in Projects: On-Demand Project management professionals constantly need to make project decisions that could be decisive for the outcome of their projects but often do not have sufficient information available to confidently make decisions. As a result, projects are increasingly falling short of delivering on their promises, requiring, more than ever, a data-informed approach to decision-making in the area of project delivery and management. The rapid growth of data comes with various challenges though, which consequently needs consideration of various critical factors for a successful implementation of a data-informed decision-making process in organizations and projects. What You Will Learn At the end of this program, you will be able to: Describe and understand the relevant methods and techniques to identify, acquire, and analyze relevant data points for decision making in projects Articulate analytical questions to focus on the real problems Identify potential shortfalls and gaps in project decision-making and apply actions to mitigate them Introduction to Data-Informed Decision Making The different types of decisions in projects Data-informed decision-making framework Shortcomings with traditional decision-making models Understanding the value of data for project delivery Issues in project management and how data can help solve them The DIKW Pyramid (Data, information, knowledge, wisdom) Types of data in projects Applying Data Analytics Understanding Data Analytics Levels of Data Analytics Data-Informed vs. Data-Driven Challenges and How to Address Them Project data availability and collection Data quality Behavioral blockers and bias Skills and Techniques Data literacy and data fluency Communicating for informed decision-making Monitoring and evaluating project decisions Implementing Data-Informed Decision Making Decision-making strategy and governance Project data culture Continuously improving decision quality Future Outlook for Decision-Making in Projects Data and AI Digital Decisioning
Data-Informed Decision Making in Projects: On-Demand Project management professionals constantly need to make project decisions that could be decisive for the outcome of their projects but often do not have sufficient information available to confidently make decisions. As a result, projects are increasingly falling short of delivering on their promises, requiring, more than ever, a data-informed approach to decision-making in the area of project delivery and management. The rapid growth of data comes with various challenges though, which consequently needs consideration of various critical factors for a successful implementation of a data-informed decision-making process in organizations and projects. What You Will Learn At the end of this program, you will be able to: Describe and understand the relevant methods and techniques to identify, acquire, and analyze relevant data points for decision making in projects Articulate analytical questions to focus on the real problems Identify potential shortfalls and gaps in project decision-making and apply actions to mitigate them Introduction to Data-Informed Decision Making The different types of decisions in projects Data-informed decision-making framework Shortcomings with traditional decision-making models Understanding the value of data for project delivery Issues in project management and how data can help solve them The DIKW Pyramid (Data, information, knowledge, wisdom) Types of data in projects Applying Data Analytics Understanding Data Analytics Levels of Data Analytics Data-Informed vs. Data-Driven Challenges and How to Address Them Project data availability and collection Data quality Behavioral blockers and bias Skills and Techniques Data literacy and data fluency Communicating for informed decision-making Monitoring and evaluating project decisions Implementing Data-Informed Decision Making Decision-making strategy and governance Project data culture Continuously improving decision quality Future Outlook for Decision-Making in Projects Data and AI Digital Decisioning
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.
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Certificates CPD Accredited e-Certificate Digital certificate - Included CPD Accredited Framed (Hardcopy) Certificate Hard copy certificate - Included Enrolment Letter Digital certificate - Included QLS Endorsed Hard Copy Certificate Hard copy certificate - Included Student ID Card Digital certificate - Included
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