284 Courses in Cardiff delivered Live Online

Attend our world class Kanban University accredited Certified Kanban System Design training course and learn to implement Kanban in your company

Attend our world class Kanban University accredited Certified Kanban System Design training course and learn to implement Kanban in your company

Attend our world class Kanban University accredited Certified Kanban System Design training course and learn to implement Kanban in your company

Join Patrick Garton for a pair of connected session about effective classroom behaviour. This is for all teachers who want to refresh and deepen their skills and understanding in this vital area.

Duration 2 Days 12 CPD hours This course is intended for Business Analysts, Technical Managers, and Programmers Overview This intensive training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice. Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning. What is R ? What is R? ? Positioning of R in the Data Science Space ? The Legal Aspects ? Microsoft R Open ? R Integrated Development Environments ? Running R ? Running RStudio ? Getting Help ? General Notes on R Commands and Statements ? Assignment Operators ? R Core Data Structures ? Assignment Example ? R Objects and Workspace ? Printing Objects ? Arithmetic Operators ? Logical Operators ? System Date and Time ? Operations ? User-defined Functions ? Control Statements ? Conditional Execution ? Repetitive Execution ? Repetitive execution ? Built-in Functions ? Summary Introduction to Functional Programming with R ? What is Functional Programming (FP)? ? Terminology: Higher-Order Functions ? A Short List of Languages that Support FP ? Functional Programming in R ? Vector and Matrix Arithmetic ? Vector Arithmetic Example ? More Examples of FP in R ? Summary Managing Your Environment ? Getting and Setting the Working Directory ? Getting the List of Files in a Directory ? The R Home Directory ? Executing External R commands ? Loading External Scripts in RStudio ? Listing Objects in Workspace ? Removing Objects in Workspace ? Saving Your Workspace in R ? Saving Your Workspace in RStudio ? Saving Your Workspace in R GUI ? Loading Your Workspace ? Diverting Output to a File ? Batch (Unattended) Processing ? Controlling Global Options ? Summary R Type System and Structures ? The R Data Types ? System Date and Time ? Formatting Date and Time ? Using the mode() Function ? R Data Structures ? What is the Type of My Data Structure? ? Creating Vectors ? Logical Vectors ? Character Vectors ? Factorization ? Multi-Mode Vectors ? The Length of the Vector ? Getting Vector Elements ? Lists ? A List with Element Names ? Extracting List Elements ? Adding to a List ? Matrix Data Structure ? Creating Matrices ? Creating Matrices with cbind() and rbind() ? Working with Data Frames ? Matrices vs Data Frames ? A Data Frame Sample ? Creating a Data Frame ? Accessing Data Cells ? Getting Info About a Data Frame ? Selecting Columns in Data Frames ? Selecting Rows in Data Frames ? Getting a Subset of a Data Frame ? Sorting (ordering) Data in Data Frames by Attribute(s) ? Editing Data Frames ? The str() Function ? Type Conversion (Coercion) ? The summary() Function ? Checking an Object's Type ? Summary Extending R ? The Base R Packages ? Loading Packages ? What is the Difference between Package and Library? ? Extending R ? The CRAN Web Site ? Extending R in R GUI ? Extending R in RStudio ? Installing and Removing Packages from Command-Line ? Summary Read-Write and Import-Export Operations in R ? Reading Data from a File into a Vector ? Example of Reading Data from a File into A Vector ? Writing Data to a File ? Example of Writing Data to a File ? Reading Data into A Data Frame ? Writing CSV Files ? Importing Data into R ? Exporting Data from R ? Summary Statistical Computing Features in R ? Statistical Computing Features ? Descriptive Statistics ? Basic Statistical Functions ? Examples of Using Basic Statistical Functions ? Non-uniformity of a Probability Distribution ? Writing Your Own skew and kurtosis Functions ? Generating Normally Distributed Random Numbers ? Generating Uniformly Distributed Random Numbers ? Using the summary() Function ? Math Functions Used in Data Analysis ? Examples of Using Math Functions ? Correlations ? Correlation Example ? Testing Correlation Coefficient for Significance ? The cor.test() Function ? The cor.test() Example ? Regression Analysis ? Types of Regression ? Simple Linear Regression Model ? Least-Squares Method (LSM) ? LSM Assumptions ? Fitting Linear Regression Models in R ? Example of Using lm() ? Confidence Intervals for Model Parameters ? Example of Using lm() with a Data Frame ? Regression Models in Excel ? Multiple Regression Analysis ? Summary Data Manipulation and Transformation in R ? Applying Functions to Matrices and Data Frames ? The apply() Function ? Using apply() ? Using apply() with a User-Defined Function ? apply() Variants ? Using tapply() ? Adding a Column to a Data Frame ? Dropping A Column in a Data Frame ? The attach() and detach() Functions ? Sampling ? Using sample() for Generating Labels ? Set Operations ? Example of Using Set Operations ? The dplyr Package ? Object Masking (Shadowing) Considerations ? Getting More Information on dplyr in RStudio ? The search() or searchpaths() Functions ? Handling Large Data Sets in R with the data.table Package ? The fread() and fwrite() functions from the data.table Package ? Using the Data Table Structure ? Summary Data Visualization in R ? Data Visualization ? Data Visualization in R ? The ggplot2 Data Visualization Package ? Creating Bar Plots in R ? Creating Horizontal Bar Plots ? Using barplot() with Matrices ? Using barplot() with Matrices Example ? Customizing Plots ? Histograms in R ? Building Histograms with hist() ? Example of using hist() ? Pie Charts in R ? Examples of using pie() ? Generic X-Y Plotting ? Examples of the plot() function ? Dot Plots in R ? Saving Your Work ? Supported Export Options ? Plots in RStudio ? Saving a Plot as an Image ? Summary Using R Efficiently ? Object Memory Allocation Considerations ? Garbage Collection ? Finding Out About Loaded Packages ? Using the conflicts() Function ? Getting Information About the Object Source Package with the pryr Package ? Using the where() Function from the pryr Package ? Timing Your Code ? Timing Your Code with system.time() ? Timing Your Code with System.time() ? Sleeping a Program ? Handling Large Data Sets in R with the data.table Package ? Passing System-Level Parameters to R ? Summary Lab Exercises Lab 1 - Getting Started with R Lab 2 - Learning the R Type System and Structures Lab 3 - Read and Write Operations in R Lab 4 - Data Import and Export in R Lab 5 - k-Nearest Neighbors Algorithm Lab 6 - Creating Your Own Statistical Functions Lab 7 - Simple Linear Regression Lab 8 - Monte-Carlo Simulation (Method) Lab 9 - Data Processing with R Lab 10 - Using R Graphics Package Lab 11 - Using R Efficiently

weave together the enchantment of tarot cards with the magic of altars, candles, and incantations, creating a tapestry of transformation

Build your confidence, fluency & accuracy with an individual ESL Book Club in English using a simplified, shortened and adapted novel. Highlights Join this individual ESL book club course to quickly improve your English confidence, fluency & accuracy 3 hours of live 1:1 English classes to help you develop your confidence in speaking in English Read a section of the book at home and then discuss what you have read Learn English 1:1 & be corrected by your private English teacher to remove errors and mistakes Build your confidence, skills and accuracy in this individual ESL book club in English course. Choose one of the books from the selection below. These books are shortened, simplified and adapted for learners of English as a foreign language. There are also language learning exercises and a short glossary of new words. Each week you read about 20-30 pages, write a summary of what you have read, and then discuss what you have read with your teacher Geoff. Personalised feedback improves your grammar, vocabulary and pronunciation. The Book Club book Individual ESL Book Clubs are flexible and can be taken on the following simplified and shortened "graded reader" books. Contact Geoff to agree a day and time, then enrol on the course. Intermediate (B1) Austen, Jane - Emma (Oxford Bookworms) Austen, Jane - Persuasion (Oxford Bookworms) Austen, Jane - Sense and Sensibility (Penguin Readers) Brontë, Emily - Wuthering Heights (Penguin Readers) Christie, Agatha - Death on the Nile (Collins English Readers) Christie, Agatha - The Body in the Library (Collins English Readers) Conan Doyle, Arthur - Sherlock Holmes: The Hound of the Baskervilles (Oxford Bookworms) Dickens, Charles - A Tale of Two Cities (Penguin Readers) Dickens, Charles - David Copperfield (Penguin Readers) Dickens, Charles - Great Expectations (Penguin Readers) Dickens, Charles - Oliver Twist (Penguin Readers) Hardy, Thomas - Far From The Madding Crowd (Penguin Readers) Hardy, Thomas - Tess of the D'Urbervilles (Penguin Readers) Hawkins, Paula - The Girl On The Train (Penguin Readers) Highsmith, Patricia - The Talented Mr Ripley (Penguin Readers) Joyce, James - Dubliners (Penguin Readers) Joyce, Rachel - The Unlikely Pilgrimage of Harold Fry (Penguin Readers) Le Carré, John - The Spy Who Came In From The Cold (Penguin Readers) Shelley, Mary - Frankenstein (Penguin Readers) Tóibín, Colm - Brooklyn (Penguin Readers) Upper-Intermediate (B2) Austen, Jane - Sense and Sensibility (Oxford Bookworms) Asimov, Isaac - I, Robot (Oxford Bookworms) Brontë, Emily - Wuthering Heights (Oxford Bookworms) Christie, Agatha - The ABC Murders (Collins English Readers) Christie, Agatha - Evil Under The Sun (Collins English Readers) Collins, Wilkie - The Woman in White (Penguin Readers) Dick, Philip K - Do Androids Dream of Electric Sheep? (Oxford Bookworms) Evaristo, Bernardine - Girl, Woman, Other (Penguin Readers) Fitzgerald, F. Scott - The Great Gatsby (Oxford Bookworms) Gyasi, Yaa - Homegoing (Penguin Readers) Hardy, Thomas - Far From The Madding Crowd (Oxford Bookworms) Kerouac, Jack - On the Road (Pearson English Readers) Mansfield, Katherine - The Garden Party (Oxford Bookworms) McEwan, Ian - The Children Act (Penguin Readers) Melville, Herman - Moby Dick (Penguin Readers) Orwell, George - 1984 (Penguin Readers) Puso, Mario - The Godfather (Penguin Readers) Smith, Zadie - White Teeth (Penguin Readers) Stevenson, RL - The Strange Case of Dr Jekyll and Mr Hyde (Pearson English Readers) Wharton, Edith - The Age of Innocence (Oxford Bookworms) Woolf, Virginia - Mrs Dalloway (Penguin Readers) Upper-Intermediate + (B2+) Austen, Jane - Pride and Prejudice (Oxford Bookworms) Brontë, Charlotte - Jane Eyre (Oxford Bookworms) Conan Doyle, Arthur - Sherlock Holmes: The Sign of Four (Oxford Bookworms) Flaubert, Gustave - Madame Bovary (Pearson English Readers) Gaskell, Elizabeth - North and South (Pearson English Readers) Garland, Alex- The Beach (Pearson English Readers) Hardy, Thomas - Tess of the D'Urbervilles (Oxford Bookworms) Tan, Amy - The Joy Luck Club (Oxford Bookworms) Geoff says: "This is a great way to start reading more in English, to build your confidence, to focus on your needs and language, and to make fast progress."

Duration 2 Days 12 CPD hours This course is intended for This introductory-level course is intended for Business Analysts and Data Analysts (or anyone else in the data science realm) who are already comfortable working with numerical data in Excel or other spreadsheet environments. No prior programming experience is required, and a browser is the only tool necessary for the course. Overview This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Throughout the hands-on course students, will learn to leverage Python scripting for data science (to a basic level) using the most current and efficient skills and techniques. Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore (to a basic level): How to work with Python interactively in web notebooks The essentials of Python scripting Key concepts necessary to enter the world of Data Science via Python This course introduces data analysts and business analysts (as well as anyone interested in Data Science) to the Python programming language, as it?s often used in Data Science in web notebooks. This goal of this course is to provide students with a baseline understanding of core concepts that can serve as a platform of knowledge to follow up with more in-depth training and real-world practice. An Overview of Python Why Python? Python in the Shell Python in Web Notebooks (iPython, Jupyter, Zeppelin) Demo: Python, Notebooks, and Data Science Getting Started Using variables Builtin functions Strings Numbers Converting among types Writing to the screen Command line parameters Flow Control About flow control White space Conditional expressions Relational and Boolean operators While loops Alternate loop exits Sequences, Arrays, Dictionaries and Sets About sequences Lists and list methods Tuples Indexing and slicing Iterating through a sequence Sequence functions, keywords, and operators List comprehensions Generator Expressions Nested sequences Working with Dictionaries Working with Sets Working with files File overview Opening a text file Reading a text file Writing to a text file Reading and writing raw (binary) data Functions Defining functions Parameters Global and local scope Nested functions Returning values Essential Demos Sorting Exceptions Importing Modules Classes Regular Expressions The standard library Math functions The string module Dates and times Working with dates and times Translating timestamps Parsing dates from text Formatting dates Calendar data Python and Data Science Data Science Essentials Pandas Overview NumPy Overview SciKit Overview MatPlotLib Overview Working with Python in Data Science Additional course details: Nexus Humans Python for Data Science: Hands-on Technical Overview (TTPS4873) 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 Python for Data Science: Hands-on Technical Overview (TTPS4873) 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.

This comprehensive online interior design course includes 12 self study modules, and weekly 1:1 telephone and or video consultations. I will personally guide and inspire you through your creative learning journey.

ITIL® 4 Foundation: Virtual In-House Training ITIL® 4 is built on the established core of best practice in the ITIL® guidance. ITIL® 4 provides a practical and flexible approach to move to the new world of digital transformation and embrace an end-to-end operating model for the delivery and operation of products and services. ITIL® 4 also provides a holistic end-to-end picture that integrates frameworks such as Lean IT, Agile, and DevOps. The ITIL® 4 Foundation is based on the exam specifications specified by AXELOS for the ITIL® 4 Foundation certification. The fundamental objective of this course is to help the participants understand the key concepts of service management and the ITIL® 4 service management framework and prepare for the ITIL® 4 Foundation exam. In addition, this course offers a rich learning experience that helps the participants relate ITIL® to their own work environment. The course includes a case study (based on a fictitious organization, 'Axle Car Hire') that will help the participants understand and experience the ITIL® guiding principles, service value, practices through real-world challenges and opportunities. The rich learning experience is supported by additional learning tools such as pre-course reading materials, post-course reading material, and a set of quick reference cards. What You Will Learn At the end of this program, you will be able to: Understand the key concepts of ITIL® service management Understand how ITIL® guiding principles can help an organization to adopt and adapt ITIL® service management Understand the four dimensions of ITIL® service management Understand the purpose and components of the ITIL® service value system, and activities of the service value chain, and how they interconnect Understand the key concepts of continual improvement Learn the various ITIL® practices and how they contribute to value chain activities Course Introduction Let's Get to Know Each Other Course Overview Course Learning Objectives Course Structure Course Agenda Introduction to IT Service Management in the Modern World Introduction to ITIL® 4 Structure and Benefits of ITIL® 4 Case Study: Axle Car Hire Case Study: Meet the Key People at Axle Case Study: The CIOs Vision for Axle Exam Details ITIL® 4 Certification Scheme Service Management - Key Concepts Intent and Context Key Terms Covered in the Module Module Learning Objectives Value and Value Co-Creation Value: Service, Products, and Resources Service Relationships Value: Outcomes, Costs, and Risks Exercise: Multiple-Choice Questions The Guiding Principles Intent and Context Identifying Guiding Principles Key Terms Covered in the Module Module Learning Objectives The Seven Guiding Principles Applying the Guiding Principles Exercise: Multiple-Choice Questions The Four Dimensions of Service Management Intent and Context The Four Dimensions Key Terms Covered in the Module The Four Dimensions and Service Value System Module Learning Objectives Organizations and People Information and Technology Partners and Suppliers Value Streams and Processes External Factors and Pestle Model Exercise: Multiple-Choice Questions Service Value System Intent and Context Service Value System and Service Value Chain Module Learning Objectives Overview of Service Value System Overview of the Service Value Chain Exercise: Multiple-Choice Questions Continual Improvement Intent and Context Key Terms Covered in the Module Introduction to Continual Improvement Module Learning Objectives The Continual Improvement Model Relationship between Continual Improvement and Guiding Principles Exercise: Multiple-Choice Questions The ITIL® Practices Intent and Context ITIL® Management Practices Key Terms Covered in the Module Module Learning Objectives The Continual Improvement Practice The Change Control Practice The Incident Management Practice The Problem Management Practice The Service Request Management Practice The Service Desk Practice The Service Level Management Practice Purpose of ITIL® Practices Exercise: Crossword Puzzle