1708 Analytics courses delivered Online

What You'll Learn An introduction to reactive expressions - how they differ from base R functionality and why they are needed within Shiny. Simple reactive expressions - creating dynamic data expressions Reacting to user-initiated actions - how to get your dashboard to adapt dynamically to user inputs Nested reactions - more complex relationships where multiple actions need to bee accounted for About The Trainer Jeremy has been using R since the very first day of his career in 2005, when it was better known as a mere letter of the alphabet! As an early adopter of the language, he’s seen it evolve – from its ugly initial versions to more elegant analytics solutions underpinned by the tidyverse, on which this course is focused. He is a regular speaker on UK R circuit and co-organises a number of R-community groups, including LondonR, ManchesterR & BrightonR, and the infamous EARL conference! About Us Datacove are a data and analytics consultancy specialising in customer analytics, marketing analytics and process automation. With over 20 years of combined experience in the industry we answer the questions that sit at the heart of every business, from who your customers are, to where and how you find more of them and how much you need to spend to do so. We also automate your most painful business processes, removing the 20+ hours you spend on them per week, forever! Extra Information Our courses are delivered during core office hours, ensuring they fit seamlessly into your work schedule. Once your booking is confirmed, we will send you a Microsoft Teams link, allowing you to join the session with ease.

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

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Duration 3 Days 18 CPD hours This course is intended for This is an introductory-level course designed to teach experienced systems administrators how to install, maintain, monitor, troubleshoot, optimize, and secure Hadoop. Previous Hadoop experience is not required. Overview Working within in an engaging, hands-on learning environment, guided by our expert team, attendees will learn to: Understand the benefits of distributed computing Understand the Hadoop architecture (including HDFS and MapReduce) Define administrator participation in Big Data projects Plan, implement, and maintain Hadoop clusters Deploy and maintain additional Big Data tools (Pig, Hive, Flume, etc.) Plan, deploy and maintain HBase on a Hadoop cluster Monitor and maintain hundreds of servers Pinpoint performance bottlenecks and fix them Apache Hadoop is an open source framework for creating reliable and distributable compute clusters. Hadoop provides an excellent platform (with other related frameworks) to process large unstructured or semi-structured data sets from multiple sources to dissect, classify, learn from and make suggestions for business analytics, decision support, and other advanced forms of machine intelligence. This is an introductory-level, hands-on lab-intensive course geared for the administrator (new to Hadoop) who is charged with maintaining a Hadoop cluster and its related components. You will learn how to install, maintain, monitor, troubleshoot, optimize, and secure Hadoop. Introduction Hadoop history and concepts Ecosystem Distributions High level architecture Hadoop myths Hadoop challenges (hardware / software) Planning and installation Selecting software and Hadoop distributions Sizing the cluster and planning for growth Selecting hardware and network Rack topology Installation Multi-tenancy Directory structure and logs Benchmarking HDFS operations Concepts (horizontal scaling, replication, data locality, rack awareness) Nodes and daemons (NameNode, Secondary NameNode, HA Standby NameNode, DataNode) Health monitoring Command-line and browser-based administration Adding storage and replacing defective drives MapReduce operations Parallel computing before MapReduce: compare HPC versus Hadoop administration MapReduce cluster loads Nodes and Daemons (JobTracker, TaskTracker) MapReduce UI walk through MapReduce configuration Job config Job schedulers Administrator view of MapReduce best practices Optimizing MapReduce Fool proofing MR: what to tell your programmers YARN: architecture and use Advanced topics Hardware monitoring System software monitoring Hadoop cluster monitoring Adding and removing servers and upgrading Hadoop Backup, recovery, and business continuity planning Cluster configuration tweaks Hardware maintenance schedule Oozie scheduling for administrators Securing your cluster with Kerberos The future of Hadoop

What You'll Learn Static vs dynamic displays - why interactive plots and tables are useful in shiny and how they can contribute towards the overall usability of the application Introduction to highcharter - how to create rendered, interactive plots using a similar syntax to the tidyverse (ggplot2) Developing dynamic charts within high charts - creating visualisations that respond to user-initiated actions within the application Introduction to DT - a package for creating data tables that users can interact with, e.g. by searching for specific pieces of data Developing dynamic tables within DT - developing sortable and searchable interactive tables for use in the application About The Trainer Jeremy has been using R since the very first day of his career in 2005, when it was better known as a mere letter of the alphabet! As an early adopter of the language, he’s seen it evolve – from its ugly initial versions to more elegant analytics solutions underpinned by the tidyverse, on which this course is focused. He is a regular speaker on UK R circuit and co-organises a number of R-community groups, including LondonR, ManchesterR & BrightonR, and the infamous EARL conference! About Us Datacove are a data and analytics consultancy specialising in customer analytics, marketing analytics and process automation. With over 20 years of combined experience in the industry we answer the questions that sit at the heart of every business, from who your customers are, to where and how you find more of them and how much you need to spend to do so. We also automate your most painful business processes, removing the 20+ hours you spend on them per week, forever! Extra Information Our courses are delivered during core office hours, ensuring they fit seamlessly into your work schedule. Once your booking is confirmed, we will send you a Microsoft Teams link, allowing you to join the session with ease.

What You'll Learn Overview of the Shiny interface - the difference between a UI and a server and how they interlink Shiny folder structures - hows to store and use static datasets, imagery and other assets for your application. Introduction to Shiny servers - how to create and deploy your first application and how to manage deployments within Shiny apps Basic Shiny UI inputs - include filters/drop downs, date sliders, text and buttons About The Trainer Jeremy has been using R since the very first day of his career in 2005, when it was better known as a mere letter of the alphabet! As an early adopter of the language, he’s seen it evolve – from its ugly initial versions to more elegant analytics solutions underpinned by the tidyverse, on which this course is focused. He is a regular speaker on UK R circuit and co-organises a number of R-community groups, including LondonR, ManchesterR & BrightonR, and the infamous EARL conference! About Us Datacove are a data and analytics consultancy specialising in customer analytics, marketing analytics and process automation. With over 20 years of combined experience in the industry we answer the questions that sit at the heart of every business, from who your customers are, to where and how you find more of them and how much you need to spend to do so. We also automate your most painful business processes, removing the 20+ hours you spend on them per week, forever! Extra Information Our courses are delivered during core office hours, ensuring they fit seamlessly into your work schedule. Once your booking is confirmed, we will send you a Microsoft Teams link, allowing you to join the session with ease.

Digital marketing has become exceptionally valuable as it has the potential to grow your business from scratch and make a profit in the process. The Digital Marketing Diploma Level 3 course includes all the most up-to-date digital marketing strategies and helps you to promote your products or business using various digital platforms. Through this Digital Marketing Diploma Level 3 course, you will equip yourself with a solid foundation in the core concepts of digital marketing. It ensures you learn all the essential skills and knowledge needed to exceed as a digital marketing professional. Broaden your knowledge of the basics of digital marketing, including the elemental characteristics, and its various strategies that empower you to develop and implement digital marketing strategies and drive sales eventually. Along with Facebook, Twitter, Pinterest, Instagram, and Google+ marketing, you will also enhance your understanding of website promoting, email marketing, Youtube marketing, copywriting, podcasting, Quora, and Google Analytics. From social media marketing to search marketing, this Digital Marketing Diploma Level 3 course teaches you everything to sparkle in the digital marketing arena. Learning Objectives Learn the fundamental skills you require to be an expert Explore different techniques used by professionals Find out the relevant job skills & knowledge to excel in this profession Get a clear understanding of the job market and current demand Update your skills and fill any knowledge gap to compete in the relevant industry CPD accreditation for proof of acquired skills and knowledge Who is this Course for? Whether you are a beginner or an existing practitioner, our CPD accredited Digital Marketing Diploma Level 3 is perfect for you to gain extensive knowledge about different aspects of the relevant industry to hone your skill further. It is also great for working professionals who have acquired practical experience but require theoretical knowledge with a credential to support their skill, as we offer CPD accredited certification to boost up your resume and promotion prospects. Entry Requirement Anyone interested in learning more about this subject should take this Digital Marketing Diploma Level 3. This course will help you grasp the basic concepts as well as develop a thorough understanding of the subject. The course is open to students from any academic background, as there is no prerequisites to enrol on this course. The course materials are accessible from an internet enabled device at anytime of the day. CPD Certificate from Course Gate At the successful completion of the course, you can obtain your CPD certificate from us. You can order the PDF certificate for £9 and the hard copy for £15. Also, you can order both PDF and hardcopy certificates for £22. Career path The Digital Marketing Diploma Level 3 will help you to enhance your knowledge and skill in this sector. After accomplishing this course, you will enrich and improve yourself and brighten up your career in the relevant job market.

Duration 5 Days 30 CPD hours This course is intended for This class is designed for email digital marketers who manage the email channel for their organization. Whether you are new to Marketing Cloud or looking for tips on how to improve your existing customer journeys, this class is a great place to start. No prior knowledge of Marketing Cloud is needed. Overview When you complete this course, you will be able to: Explain compliance concepts to ensure optimal deliverability. Use tools within Marketing Cloud to uphold deliverability standards. Utilize Email Design best practices to ensure the best customer experience. Develop effective, relevant messages using Content Builder. Design and test different delivery methods and options when sending an email message. Design and execute customer journeys using automation tools within Marketing Cloud. Differentiate the use cases for different automation activities in Automation Studio and Journey Builder. Define fundamental data management and structure terminology. Use data segmentation tools to create targeted emails. Apply a simple data model concept to a real-world scenario. Define subscriber statuses, unsubscribe methods, and preferences. Analyze marketing campaigns using common KPIs. Solve a common marketing problem using troubleshooting guidance. Prioritize testing methods and tools to ensure quality control. Explain fundamental account and sending administration. Know where to go for more information, guidance, and support. Describe capabilities across the platform. Start your journey to becoming a Marketing Cloud Specialist. In this 5-day, expert-led class, you will learn how to build customer journeys within Marketing Cloud. Our team of Marketing Cloud pros will walk you through best practices related to executing, monitoring, and analyzing your journeys, arming you with the tools and know-how to design personalized journeys and engage with your customers in a whole new way. Course Outline Introduction to Salesforce Marketing Cloud Administration Subscriber and Data Management Email Message Design and Creation Message Testing, Delivery, and Email Marketing Best Practices Marketing Automation Analytics and Troubleshooting Summary

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Use Cases for Business Analysis: Virtual In-House Training The use case is a method for documenting the interactions between the user of a system and the system itself. Use cases have been in the software development lexicon for over twenty years, ever since it was introduced by Ivar Jacobson in the late 1980s. They were originally intended as aids to software design in object-oriented approaches. However, the method is now used throughout the Solution Development Life Cycle from elicitation through to specifying test cases, and is even applied to software development that is not object oriented. This course identifies how business analysts can apply use cases to the processes of defining the problem domain through elicitation, analyzing the problem, defining the solution, and confirming the validity and usability of the solution. What you will Learn You'll learn how to: Apply the use case method to define the problem domain and discover the conditions that need improvement in a business process Employ use cases in the analysis of requirements and information to create a solution to the business problem Translate use cases into requirements Getting Started Introductions Course structure Course goals and objectives Foundation Concepts Overview of use case modeling What is a use case model? The 'how and why' of use cases When to perform use case modeling Where use cases fit into the solution life cycle Use cases in the problem domain Use cases in the solution domain Use case strengths and weaknesses Use case variations Use case driven development Use case lexicon Use cases Actors and roles Associations Goals Boundaries Use cases though the life cycle Use cases in the life cycle Managing requirements with use cases The life cycle is use case driven Elicitation with Use Cases Overview of the basic mechanics and vocabulary of use cases Apply methods of use case elicitation to define the problem domain, or 'as is' process Use case diagrams Why diagram? Partitioning the domain Use case diagramming guidelines How to employ use case diagrams in elicitation Guidelines for use case elicitation sessions Eliciting the problem domain Use case descriptions Use case generic description template Alternative templates Elements Pre and post conditions Main Success Scenario The conversation Alternate paths Exception paths Writing good use case descriptions Eliciting the detailed workflow with use case descriptions Additional information about use cases Analyzing Requirements with Use Cases Use case analysis on existing requirements Confirming and validating requirements with use cases Confirming and validating information with use cases Defining the actors and use cases in a set of requirements Creating the scenarios Essential (requirements) use case Use case level of detail Use Case Analysis Techniques Generalization and Specialization When to use generalization or specialization Generalization and specialization of actors Generalization and specialization of use cases Examples Associating generalizations Subtleties and guidelines Use Case Extensions The <> association The <> association Applying the extensions Incorporating extension points into use case descriptions Why use these extensions? Extensions or separate use cases Guidelines for extensions Applying use case extensions Patterns and anomalies o Redundant actors Linking hierarchies Granularity issues Non-user interface use cases Quality considerations Use case modeling errors to avoid Evaluating use case descriptions Use case quality checklist Relationship between Use Cases and Business Requirements Creating a Requirements Specification from Use Cases Flowing the conversation into requirements Mapping to functional specifications Adding non-functional requirements Relating use cases to other artifacts Wire diagrams and user interface specifications Tying use cases to test cases and scenarios Project plans and project schedules Relationship between Use Cases and Functional Specifications System use cases Reviewing business use cases Balancing use cases Use case realizations Expanding and explaining complexity Activity diagrams State Machine diagrams Sequence diagrams Activity Diagrams Applying what we know Extension points Use case chaining Identifying decision points Use Case Good Practices The documentation trail for use cases Use case re-use Use case checklist Summary What did we learn, and how can we implement this in our work environment?