807 Modeling courses

OVERVIEW Prerequisites—DIAD training and Advanced Data Modeling and Shaping training or equivalent working experience This course has been designed specifically for experienced model developers and gives a more advanced treatment of DAX formulas than either DIAD or the PL-300 course. We recommend that attendees have prior experience working with Power BI Desktop to create data models. During this course you will review: Writing DAX formulas. Defining calculated tables and columns. Defining measures. Using DAX iterator functions. Modifying filter context. Using DAX time intelligence functions. After completing this training, the attendees should be able to work with Data Analysis Expressions (DAX), perform calculations and define common business calculations for use in reports, address performance and functionality concerns. COURSE BENEFITS: Understand Analytic queries in Power BI Create calculated tables, calculated columns and measures Use DAX functions and operators to build DAX formulas Use DAX iterator functions Create formulas that manipulate the filter context Use DAX time intelligence functions WHO IS THE COURSE FOR? Analysts with experience of Power BI wishing to develop more advanced formulas in DAX Power BI developers who wish to deepen their understanding of the process of calculating formulas so as to make development faster and more reliable LAB OUTLINE Lab 1 Setup Connect to data and understand the objectives of the future labs Lab 2 Write DAX Formulas For Power BI Create a measure Use variables in the measure definition Lab 3 Add Calculated Table And Columns Duplicate a table Create a hierarchy Create a date table Add calculated columns Lab 4 Add Measures To Power BI Desktop Models Add an implicit measure to a report Add an explicit measure Add a compound measure Add a quick measure Lab 5 Use DAX Iterator Functions In A Power BI Desktop Model Complex summarization Higher grain summarization Create ranking measure Lab 6 Modify DAX Filter Context In Power BI Desktop Models Apply Boolean expression filter Remove filters: use ALL Remove filters: use AllSelected Preserve filters: use KeepFilters HASONEVALUE ISINSCOPE Context transition Lab 7 Use DAX Time Intelligence Functions In Power BI Desktop Models TOTALYTD SAMEPERIODLASTYEAR Calculate new occurrences Snapshot calculations

This course covers the fundamental topics of statistical methodology, enabling you to understand the application and interpretation of linear and non-linear regression modeling.

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Learn the fundamentals of Blender software, explore techniques for creating detailed 3D models, and master the process of rendering your creations. Whether you're a beginner or an experienced designer, this course will equip you with the skills needed to bring your characters to life in the digital world.

Business Intelligence Business Intelligence (BI) refers to a set of technology-based techniques, applications, and practices used to aggregate, analyze, and present business data. BI practices provide historical and current views of vast amounts of data and generate predictions for business operations. The purpose of Business Intelligence is the support of better business decision making. This course provides an overview of the technology and application of BI and how it can be used to improve corporate performance. What you will Learn You will learn how to: Specify a data warehouse schema Identify the data and visualization to be used for data mining and Business Intelligence Design a Business Intelligence user interface Getting Started Introductions Agenda Expectations Foundation Concepts The challenge of decision making What is Business Intelligence? The Business Intelligence value proposition Business Intelligence taxonomy Business Intelligence management issues Sources of Business Intelligence Data warehousing Data and information Information architecture Defining the data warehouse and its relationships Facts and dimensions Modeling, meta-modeling, and schemas Alternate architectures Building the data warehouse Extracting Transforming Loading Setting up the data and relationships Dimensions and the Fact Table Implementing many-to-many relationships in data warehouse Data marts Online Analytical Processing (OLAP) What is OLAP? OLAP and OLTP OLAP functionality Multi-dimensions Thinking in more than two dimensions What are the possibilities? OLAP architecture Cubism Tools OLAP variations - MOLAP, ROLAP, HOLAP BI using SOA Applications of Business Intelligence Applying BI through OLAP Enterprise Resource Planning and CRM Business Intelligence and financial information Business Intelligence User Interfaces and Presentations Data access Push-pull data access Types of decision support systems Designing the front end Presentation formats Dashboards Types of dashboards Common dashboard features Briefing books and scorecards Querying and Reporting Reporting emphasis Retrofitting Talking back Key Performance Indicators Report Definition and Visualization Typical reporting environment Forms of visualization Unconstrained views Data mining What is in the mine? Applications for data mining Data mining architecture Cross Industry Standard Process for Data Mining (CISP-DM) Data mining techniques Validation The Business Intelligence User Experience The business analyst role Business analysis and data analysis Five-step approach Cultural impact Identifying questions Gathering information Understand the goals The strategic Business Intelligence cycle Focus of Business Intelligence Design for the user Iterate the access Iterative solution development process Review and validation questions Basic approaches Building ad-hoc queries Building on-demand self-service reports Closed loop Business Intelligence Coming attractions - future of Business Intelligence Best practices in Business Intelligence

Business Intelligence: In-House Training Business Intelligence (BI) refers to a set of technology-based techniques, applications, and practices used to aggregate, analyze, and present business data. BI practices provide historical and current views of vast amounts of data and generate predictions for business operations. The purpose of Business Intelligence is the support of better business decision making. This course provides an overview of the technology and application of BI and how it can be used to improve corporate performance. What you will Learn You will learn how to: Specify a data warehouse schema Identify the data and visualization to be used for data mining and Business Intelligence Design a Business Intelligence user interface Getting Started Introductions Agenda Expectations Foundation Concepts The challenge of decision making What is Business Intelligence? The Business Intelligence value proposition Business Intelligence taxonomy Business Intelligence management issues Sources of Business Intelligence Data warehousing Data and information Information architecture Defining the data warehouse and its relationships Facts and dimensions Modeling, meta-modeling, and schemas Alternate architectures Building the data warehouse Extracting Transforming Loading Setting up the data and relationships Dimensions and the Fact Table Implementing many-to-many relationships in data warehouse Data marts Online Analytical Processing (OLAP) What is OLAP? OLAP and OLTP OLAP functionality Multi-dimensions Thinking in more than two dimensions What are the possibilities? OLAP architecture Cubism Tools OLAP variations - MOLAP, ROLAP, HOLAP BI using SOA Applications of Business Intelligence Applying BI through OLAP Enterprise Resource Planning and CRM Business Intelligence and financial information Business Intelligence User Interfaces and Presentations Data access Push-pull data access Types of decision support systems Designing the front end Presentation formats Dashboards Types of dashboards Common dashboard features Briefing books and scorecards Querying and Reporting Reporting emphasis Retrofitting Talking back Key Performance Indicators Report Definition and Visualization Typical reporting environment Forms of visualization Unconstrained views Data mining What is in the mine? Applications for data mining Data mining architecture Cross Industry Standard Process for Data Mining (CISP-DM) Data mining techniques Validation The Business Intelligence User Experience The business analyst role Business analysis and data analysis Five-step approach Cultural impact Identifying questions Gathering information Understand the goals The strategic Business Intelligence cycle Focus of Business Intelligence Design for the user Iterate the access Iterative solution development process Review and validation questions Basic approaches Building ad-hoc queries Building on-demand self-service reports Closed loop Business Intelligence Coming attractions - future of Business Intelligence Best practices in Business Intelligence

Training duration: 10 hrs. Click here for more info Method: 1-2-1, Personalized attention, Tailored content, Flexible pace, Individual support. Schedule: Personalized training experience with our flexible 1-2-1 sessions. Tailor your own schedule by pre-booking a convenient hour of your choice, available from Monday to Saturday between 9 am and 7 pm. Discover the world of 3D computer graphics and animation with our Autodesk Maya Basic to Fundamentals Training Course. Whether you're a beginner or aspiring artist, this comprehensive program equips you with essential skills in modeling, animation, and rendering. Benefit from interactive learning, experienced instructors, and the option to choose in-person or live online sessions. Enroll now to gain industry-standard knowledge and unleash your creativity in the field of animation and visual effects. Maya Basic to Intermediate Course Course Duration: 10 hours Course Outline: I. Introduction to Maya (1 hour) - Gain an insightful overview of Maya and its diverse applications - Familiarize yourself with the Maya interface and essential tools - Master navigation and viewport controls with ease - Learn the art of creating and managing projects proficiently II. Creating 3D Models (2 hours) - Lay a strong foundation in polygon modeling basics - Craft and shape basic objects and shapes adeptly - Refine and modify objects with precision and creativity - Unlock the art of constructing complex objects using extrusions and bevels III. Texturing and Materials (1 hour) - Venture into the realm of texturing and its significance - Create and skillfully apply materials to enhance visual appeal - Master the art of texture mapping and UV unwrapping techniques - Seamlessly import and incorporate textures and images into your projects IV. Lighting and Rendering (1.5 hours) - Illuminate your creations with fundamental lighting techniques - Set up cameras and compose visually captivating scenes - Master the art of rendering still images and dynamic animations - Explore diverse output options and file formats for professional results V. Animation (2.5 hours) - Embark on an enthralling journey into the world of animation - Effectively utilize keyframe animation and animation curves - Create and edit animation clips for seamless and captivating motion - Dive into the intricacies of rigging and animating a simple character VI. Special Effects (1 hour) - Unleash the potential of particle systems and dynamics - Create and manipulate mesmerizing fluid and fire effects - Craft and refine awe-inspiring special effects such as explosions and smoke VII. Intermediate Modeling Techniques (1 hour) - Elevate your skills with NURBS modeling essentials - Create and modify curves and surfaces with finesse - Dive into the world of crafting organic shapes using NURBS techniques - Employ sculpting tools to create high-resolution and detailed models VIII. Conclusion and Next Steps (0.5 hours) - Recap the wealth of knowledge from the course content - Discover valuable tips and resources for further learning and growth - Engage in a dynamic Q&A session and provide valuable feedback

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Throughout this course, you will learn everything you need to know about linear and non-linear regression, regression modeling, and Stata. By the end of this course, you will be able to understand and be confident in interpreting complex types of data using Stata.

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