2007 Analytics courses

This course is very much a discussion, so be prepared to present and critically analyse your own and class mates work. You will also need to bring a few examples of work you have done in the past. Learning and applying best practice visualisation principles will improve effective discussions amongst decision makers throughout your organisation. As a result more end-users of your dashboards will be able to make better decisions, more quickly. This 2 Day training course is aimed at analysts with good working knowledge of BI tools (we use Tableau to present, but attendees can use their own software such as Power BI or Qlik Sense). It is a great preparation for taking advanced certifications, such as Tableau Certified Professional. Contact us to discuss the Visual Analytics Best Practice course Email us if you are interested in an on-site course, or would be interested in different dates and locations This Tableau Desktop training intermediate course is designed for the professional who has a solid foundation with Tableau and is looking to take it to the next level. Attendees should have a good understanding of the fundamental concepts of building Tableau worksheets and dashboards typically achieved from having attended our Tableau Desktop Foundation Course. At the end of this course you will be able to communicate insights more effectively, enabling your organisation to make better decisions, quickly. The Tableau Desktop Analyst training course is aimed at people who are used to working with MS Excel or other Business Intelligence tools and who have preferably been using Tableau already for basic reporting. The course includes the following topics: WHAT IS VISUAL ANALYSIS? Visual Analytics Visual Analytics Process Advantages of Visual Analysis Exercise: Interpreting Visualisations HOW DO WE PROCESS VISUAL INFORMATION? Memory and Processing Types Exercise: Identifying Types of Processing Cognitive Load Exercise: Analysing Cognitive Load Focus and Guide the Viewer Remove Visual Distractions Organise Information into Chunks Design for Proximity Exercise: Reducing Cognitive Load SENSORY MEMORY Pre-attentive Attributes Quantitatively-Perceived Attributes Categorically-Perceived Attributes Exercise: Analysing Pre-attentive Attributes Form & Attributes Exercise: Using Form Effectively Colour & Attributes Exercise: Using Colour Effectively Position & Attributes Exercise: Using Position Effectively ENSURING VISUAL INTEGRITY Informing without Misleading Gestalt Principles Visual Area Axis & Scale Colour Detail Exercise: Informing without Misleading CHOOSING THE RIGHT VISUALISATION Comparing and Ranking Categories Comparing Measures Comparing Parts to Whole Viewing Data Over Time Charts Types for Mapping Viewing Correlation Viewing Distributions Viewing Specific Values DASHBOARDS AND STORIES Exercise: Picking the Chart Type Exercise: Brainstorming Visual Best Practice Development Process for Dashboards and Stories Plan the Visualisation Create the Visualisation Test the Visualisation Exercise: Designing Dashboards and Stories This training course includes over 20 hands-on exercises to help participants “learn by doing” and to assist group discussions around real-life use cases. Each attendee receives an extensive training manual which covers the theory, practical applications and use cases, exercises and solutions together with a USB with all the materials required for the training. The course starts at 09:30 on the first day and ends at 17:00. On the second day the course starts at 09:00 and ends at 17:00. Students must bring their own laptop with an active version of Tableau Desktop 10.5 (or later) pre-installed. What People Are Saying About This Course "Steve was willing to address questions arising from his content in a full and understandable way"Lisa L. "Really enjoyed the course and feel the subject and the way it was taught was very close to my needs"James G. "The course tutor Steve was incredibly helpful and taught the information very well while making the two days very enjoyable."Bradd P. "The host and his courses will give you the tools and confidence that you need to be comfortable with Tableau."Jack S. "Steve was fantastic with his knowledge and knowhow about the product. Where possible he made sure you could put demonstrations in to working practice, to give the audience a clear understanding."Tim H. "This was a very interesting and helpful course, which will definitely help me produce smarter, cleaner visualisations that will deliver more data-driven insights within our business."Richard A. "Steve is very open to questions and will go out of his way to answer any query. Thank you"Wasif N. "Steve was willing to address questions arising from his content in a full and understandable way"Lisa L. "Really enjoyed the course and feel the subject and the way it was taught was very close to my needs"James G.

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The goal of this course is to teach you the fundamentals of Alteryx and teach you a more efficient way to clean and prepare your data. You'll learn to work with data in new ways other than Tableau and PowerBI, using Alteryx for all your data analysis needs.

Duration 1 Days 6 CPD hours This course is intended for This course is intended for data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines. Completed either AWS Technical Essentials or Architecting on AWS Completed Building Data Lakes on AWS Overview In this course, you will learn to: Compare the features and benefits of data warehouses, data lakes, and modern data architectures Design and implement a data warehouse analytics solution Identify and apply appropriate techniques, including compression, to optimize data storage Select and deploy appropriate options to ingest, transform, and store data Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights Secure data at rest and in transit Monitor analytics workloads to identify and remediate problems Apply cost management best practices In this course, you will build a data analytics solution using Amazon Redshift, a cloud data warehouse service. The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will learn to integrate Amazon Redshift with a data lake to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon Redshift. Module A: Overview of Data Analytics and the Data Pipeline Data analytics use cases Using the data pipeline for analytics Module 1: Using Amazon Redshift in the Data Analytics Pipeline Why Amazon Redshift for data warehousing? Overview of Amazon Redshift Module 2: Introduction to Amazon Redshift Amazon Redshift architecture Interactive Demo 1: Touring the Amazon Redshift console Amazon Redshift features Practice Lab 1: Load and query data in an Amazon Redshift cluster Module 3: Ingestion and Storage Ingestion Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API Data distribution and storage Interactive Demo 3: Analyzing semi-structured data using the SUPER data type Querying data in Amazon Redshift Practice Lab 2: Data analytics using Amazon Redshift Spectrum Module 4: Processing and Optimizing Data Data transformation Advanced querying Practice Lab 3: Data transformation and querying in Amazon Redshift Resource management Interactive Demo 4: Applying mixed workload management on Amazon Redshift Automation and optimization Interactive demo 5: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus cluster Module 5: Security and Monitoring of Amazon Redshift Clusters Securing the Amazon Redshift cluster Monitoring and troubleshooting Amazon Redshift clusters Module 6: Designing Data Warehouse Analytics Solutions Data warehouse use case review Activity: Designing a data warehouse analytics workflow Module B: Developing Modern Data Architectures on AWS Modern data architectures