3128 Development courses delivered Live Online

Duration 4 Days 24 CPD hours This course is intended for The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview Overview of data science and machine learning at scale Overview of the Hadoop ecosystem Working with HDFS data and Hive tables using Hue Introduction to Cloudera Data Science Workbench Overview of Apache Spark 2 Reading and writing data Inspecting data quality Cleansing and transforming data Summarizing and grouping data Combining, splitting, and reshaping data Exploring data Configuring, monitoring, and troubleshooting Spark applications Overview of machine learning in Spark MLlib Extracting, transforming, and selecting features Building and evaluating regression models Building and evaluating classification models Building and evaluating clustering models Cross-validating models and tuning hyperparameters Building machine learning pipelines Deploying machine learning models Spark, Spark SQL, and Spark MLlib PySpark and sparklyr Cloudera Data Science Workbench (CDSW) Hue This workshop covers data science and machine learning workflows at scale using Apache Spark 2 and other key components of the Hadoop ecosystem. The workshop emphasizes the use of data science and machine learning methods to address real-world business challenges. Using scenarios and datasets from a fictional technology company, students discover insights to support critical business decisions and develop data products to transform the business. The material is presented through a sequence of brief lectures, interactive demonstrations, extensive hands-on exercises, and discussions. The Apache Spark demonstrations and exercises are conducted in Python (with PySpark) and R (with sparklyr) using the Cloudera Data Science Workbench (CDSW) environment. The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview of data science and machine learning at scaleOverview of the Hadoop ecosystemWorking with HDFS data and Hive tables using HueIntroduction to Cloudera Data Science WorkbenchOverview of Apache Spark 2Reading and writing dataInspecting data qualityCleansing and transforming dataSummarizing and grouping dataCombining, splitting, and reshaping dataExploring dataConfiguring, monitoring, and troubleshooting Spark applicationsOverview of machine learning in Spark MLlibExtracting, transforming, and selecting featuresBuilding and evauating regression modelsBuilding and evaluating classification modelsBuilding and evaluating clustering modelsCross-validating models and tuning hyperparametersBuilding machine learning pipelinesDeploying machine learning models Additional course details: Nexus Humans Cloudera Data Scientist Training 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 Cloudera Data Scientist Training 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.

Duration 5 Days 30 CPD hours This course is intended for This course is designed for business professionals who leverage data to address business issues. The typical student in this course will have several years of experience with computing technology, including some aptitude in computer programming. However, there is not necessarily a single organizational role that this course targets. A prospective student might be a programmer looking to expand their knowledge of how to guide business decisions by collecting, wrangling, analyzing, and manipulating data through code; or a data analyst with a background in applied math and statistics who wants to take their skills to the next level; or any number of other data-driven situations. Ultimately, the target student is someone who wants to learn how to more effectively extract insights from their work and leverage that insight in addressing business issues, thereby bringing greater value to the business. Overview In this course, you will learn to: Use data science principles to address business issues. Apply the extract, transform, and load (ETL) process to prepare datasets. Use multiple techniques to analyze data and extract valuable insights. Design a machine learning approach to address business issues. Train, tune, and evaluate classification models. Train, tune, and evaluate regression and forecasting models. Train, tune, and evaluate clustering models. Finalize a data science project by presenting models to an audience, putting models into production, and monitoring model performance. For a business to thrive in our data-driven world, it must treat data as one of its most important assets. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, it can also inform?by guiding decisions and influencing day-to-day operations. This calls for a robust workforce of professionals who can analyze, understand, manipulate, and present data within an effective and repeatable process framework. In other words, the business world needs data science practitioners. This course will enable you to bring value to the business by putting data science concepts into practice Addressing Business Issues with Data Science Topic A: Initiate a Data Science Project Topic B: Formulate a Data Science Problem Extracting, Transforming, and Loading Data Topic A: Extract Data Topic B: Transform Data Topic C: Load Data Analyzing Data Topic A: Examine Data Topic B: Explore the Underlying Distribution of Data Topic C: Use Visualizations to Analyze Data Topic D: Preprocess Data Designing a Machine Learning Approach Topic A: Identify Machine Learning Concepts Topic B: Test a Hypothesis Developing Classification Models Topic A: Train and Tune Classification Models Topic B: Evaluate Classification Models Developing Regression Models Topic A: Train and Tune Regression Models Topic B: Evaluate Regression Models Developing Clustering Models Topic A: Train and Tune Clustering Models Topic B: Evaluate Clustering Models Finalizing a Data Science Project Topic A: Communicate Results to Stakeholders Topic B: Demonstrate Models in a Web App Topic C: Implement and Test Production Pipelines

Duration 3 Days 18 CPD hours This course is intended for This course is intended for: Intermediate software developers Overview In this course, you will learn to: Set up the AWS SDK and developer credentials for Java, C#/.NET, Python, and JavaScript Interact with AWS services and develop solutions by using the AWS SDK Use AWS Identity and Access Management (IAM) for service authentication Use Amazon Simple Storage Service (Amazon S3) and Amazon DynamoDB as data stores Integrate applications and data by using AWS Lambda, Amazon API Gateway, Amazon Simple Queue Service (Amazon SQS), Amazon Simple Notification Service (Amazon SNS), and AWS Step Functions Use Amazon Cognito for user authentication Use Amazon ElastiCache to improve application scalability Leverage the CI/CD pipeline to deploy applications on AWS In this course, you learn how to use the AWS SDK to develop secure and scalable cloud applications using multiple AWS services such as Amazon DynamoDB, Amazon Simple Storage Service, and AWS Lambda. You explore how to interact with AWS using code and learn about key concepts, best practices, and troubleshooting tips. Module 0: Course Overview Agenda Introductions Student resources Module 1: Introduction to AWS Introduction to the AWS Cloud Cloud scenarios Infrastructure overview Introduction to AWS foundation services Module 2: Introduction to Developing on AWS Getting started with developing on AWS Introduction to developer tools Introduction to management tools Module 3: Introduction to AWS Identity and Access Management Shared responsibility model Introduction to IAM Use authentication and authorization Module 4: Introduction to the Lab Environment Introduction to the lab environment Lab 1: Getting started and working with IAM Module 5: Developing Storage Solutions with Amazon Simple Storage Service Overview of AWS storage options Amazon S3 key concepts Best practices Troubleshooting Scenario: Building a complete application Lab 2: Developing storage solutions with Amazon S3 Module 6: Developing Flexible NoSQL Solutions with Amazon DynamoDB Introduction to AWS database options Introduction to Amazon DynamoDB Developing with DynamoDB Best practices Troubleshooting Scenario: Building an end-to-end app Lab 3: Developing flexible NoSQL solutions with Amazon DynamoDB Module 7: Developing Event-Driven Solutions with AWS Lambda What is serverless computing? Introduction to AWS Lambda Key concepts How Lambda works Use cases Best practices Scenario: Build an end-to-end app Module 8: Developing Solutions with Amazon API Gateway Introduction to Amazon API Gateway Developing with API Gateway Best practices Introduction to AWS Serverless Application Model Scenario: Building an end-to-end app Lab 4: Developing event-driven solutions with AWS Lambda Module 9: Developing Solutions with AWS Step Functions Understanding the need for Step Functions Introduction to AWS Step Functions Use cases Module 10: Developing Solutions with Amazon Simple Queue Service and Amazon Simple Notification Service Why use a queueing service? Developing with Amazon Simple Queue Service Developing with Amazon Simple Notification Service Developing with Amazon MQ Lab 5: Developing messaging solutions with Amazon SQS and Amazon SNS Module 11: Caching Information with Amazon ElastiCache Caching overview Caching with Amazon ElastiCache Caching strategies Module 12: Developing Secure Applications Securing your applications Authenticating your applications to AWS Authenticating your customers Scenario: Building an end-to-end app Module 13: Deploying Applications Introduction to DevOps Introduction to deployment and testing strategies Deploying applications with AWS Elastic Beanstalk Scenario: Building an end-to-end app Lab 6: Building an end-to-end app Module 14: Course wrap-up Course overview AWS training courses Certifications Course feedback

Duration 1 Days 6 CPD hours In this hands on workshop for Agile Scrum Masters, Release Train Engineers and anyone serving as Jira Administrators, Jira experts will lead you through advanced configuration and customization settings in Jira, from installation through to customized screens, workflows, filters and reports. Jira Administration Adding and managing Users Administering and managing Groups Global Jira Settings Jira layout and interface customization User authentication and security Jira Customization Customization of screens and fields Customization of workflows Project and Board Administration Configuring and managing Projects Configuring and managing Boards Creating and managing Filters JQL Jira Integration Integrating Jira with Atlassian Tools Retrospectives and Documentation in Confluence Code management with Bitbucket Integration management with Bamboo Building a Dashboard with gadgets Jira Plug-ins and Marketplace

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Data platform engineers Solutions architects IT professionals Overview In this course, you will learn to: Apply data lake methodologies in planning and designing a data lake Articulate the components and services required for building an AWS data lake Secure a data lake with appropriate permission Ingest, store, and transform data in a data lake Query, analyze, and visualize data within a data lake In this course, you will learn how to build an operational data lake that supports analysis of both structured and unstructured data. You will learn the components and functionality of the services involved in creating a data lake. You will use AWS Lake Formation to build a data lake, AWS Glue to build a data catalog, and Amazon Athena to analyze data. The course lectures and labs further your learning with the exploration of several common data lake Introduction to data lakes Describe the value of data lakes Compare data lakes and data warehouses Describe the components of a data lake Recognize common architectures built on data lakes Data ingestion, cataloging, and preparation Describe the relationship between data lake storage and data ingestion Describe AWS Glue crawlers and how they are used to create a data catalog Identify data formatting, partitioning, and compression for efficient storage and query Lab 1: Set up a simple data lake Data processing and analytics Recognize how data processing applies to a data lake Use AWS Glue to process data within a data lake Describe how to use Amazon Athena to analyze data in a data lake Building a data lake with AWS Lake Formation Describe the features and benefits of AWS Lake Formation Use AWS Lake Formation to create a data lake Understand the AWS Lake Formation security model Lab 2: Build a data lake using AWS Lake Formation Additional Lake Formation configurations Automate AWS Lake Formation using blueprints and workflows Apply security and access controls to AWS Lake Formation Match records with AWS Lake Formation FindMatches Visualize data with Amazon QuickSight Lab 3: Automate data lake creation using AWS Lake Formation blueprints Lab 4: Data visualization using Amazon QuickSight Architecture and course review Post course knowledge check Architecture review Course review

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Chairing or facilitating a panel discussion is a particular skill. When many experts and specialists in their field are asked to do this, they discover that it is not as easy as they imagined. Pitching and introducing the session, involving everyone, promoting audience engagement, dealing with difficult panellists and ending the discussion can all be a challenge for inexperienced facilitators. Our panel facilitation training workshop focuses on providing delegates with the techniques and confidence they need to chair panel debates effectively. It is delivered by BBC presenter Martine Croxall and is highly interactive, blending theory with practical exercises. During the course, you will learn: the best way to prepare, including audience analysis and research how to pitch a panel how to introduce a panel how to involve everyone in the discussion the best way to promote audience engagement how to deal with difficult panellists how to end a discussion Each delegate will have the chance to chair a panel discussion, with the other delegates and Martine playing different roles as panellists.

This is not a single course but a set of menu options from which you can 'pick and mix' to create a draft programme yourself, as a discussion document which we can then fine-tune with you. For a day's training course, simply consider your objectives, select six hours' worth of modules and let us do the fine-tuning so that you get the best possible training result. Consider your objectives carefully for maximum benefit from the course. Is the training for new or experienced credit control staff? Are there specific issues to be addressed within your particular sector (eg, housing, education, utilities, etc)? Do your staff need to know more about the legal issues? Or would a practical demonstration of effective telephone tactics be more useful to them? Menu Rather than a generic course outline, the expert trainer has prepared a training 'menu' from which you can select those topics of most relevance to your organisation. We can then work with you to tailor a programme that will meet your specific objectives. Advanced credit control skills for supervisors - 1⁄2 day Basic legal overview: do's and don'ts of debt recovery - 2 hours Body language in the credit and debt sphere - 1⁄2 day County Court suing and enforcement - 1⁄2 day Credit checking and assessment - 1 hour Customer visits and 'face to face' debt recovery skills - 1⁄2 day Data Protection Act explained - 1⁄2 day Dealing with 'Caring Agencies' and third parties - 1 hour Debt counselling skills - 2 hours Elementary credit control skills for new staff - 1⁄2 day Granting credit and collecting debt in Europe - 1⁄2 day Identifying debtors by 'type' to handle them accurately - 1 hour Insolvency: Understanding bankruptcy / receivership / administration / winding-up / liquidation / CVAs and IVAs - 2 hours Late Payment of Commercial Debts Interest Act explained - 2 hours Liaison with sales and other departments for maximum credit effectiveness - 1 hour Suing in Scottish Courts (Small Claims and Summary Cause) - 1⁄2 day Telephone techniques for successful debt collection - 11⁄2 hours Terms and conditions of business with regard to credit and debt - 2 hours Tracing 'gone away' debtors (both corporate and individual) - 11⁄2 hours What to do if you/your organisation are sued - 1⁄2 day Other topics you might wish to consider could include: Assessment of new customers as debtor risks Attachment of Earnings Orders Bailiffs and how to make them work for you Benefit overpayments and how to recover them Cash flow problems (business) Charging Orders over property/assets Credit policy: how to write one Council and Local Authority debt recovery Consumer Credit Act debt issues Using debt collection agencies Director's or personal guarantees Domestic debt collection by telephone Exports (world-wide) and payment for Emergency debt recovery measures Education Sector debt recovery Forms used in credit control Factoring of sales invoices Finance Sector debt recovery needs Third Party Debt Orders (Enforcement) Government departments (collection from) Harassment (what it is - and what it is not) Health sector debt recovery skills Hardship (members of the public) Insolvency and the Insolvency Act In-house collection agency (how to set up) Instalments: getting offers which are kept Judgment (explanation of types) Keeping customers while collecting the debt Late payment penalties and sanctions Letter writing for debt recovery Major companies as debtors Members of the public as debtors Monitoring of major debtors and risks Negotiation skills for debt recovery Old debts and how to collect them Out of hours telephone calls and visits Office of Fair Trading and collections Oral Examination (Enforcement) Pro-active telephone collection Parents of young debtors Partnerships as debtors Positive language in debt recovery Pre-litigation checking skills Power listening skills Questions to solicit information Retention of title and 'Romalpa' clauses Sale of Goods Act explained Salesmen and debt recovery Sheriffs to enforce your judgment Students as debtors Statutory demands for payment Small companies (collection from) Sundry debts (collection of) Terms and Conditions of Contract Tracing 'gone away' debtors The telephone bureau and credit control Taking away reasons not to pay Train the trainer skills Utility collection needs Visits for collection and recovery Warrant of execution (enforcement)

This very practical session is designed to enable participants to improve the impact, clarity and accuracy of their reports. It focuses equally on the two key areas - structure and writing technique. This course will help participants: Scope reports based on objective and intended readership Write a structured report Use the Fog Index to ensure readability Write grammatically correct and well-punctuated text Review and edit their work. 1 Introduction Objectives and overview Introductions and personal aims 2 What makes a good report? Practical activity and feedback 3 Before you start The planning process and scoping a report Organising information Key report headings What goes where? Writing practice and review 4 Writing tips and techniques Clear English and use of language Grammar and sentence structure Refresher in punctuation Writing in the third person The Fog Index - and how to measure readability 5 Pulling it all together Reviewing and proofing 6 Review Summary of key learning points Action planning

This intensive one-day training programme has been developed to help those involved in producing specifications create high quality documents in an organised and effective way. The programme explains the primary purpose of specifications and the importance of understanding the context in which they are used. It focuses particularly on how to develop and structure content and write requirements that are clear and concise. The methods and techniques presented will provide a practical foundation course for those new to the topic whilst offering new insights to those with more experience. The objectives of the workshop are to: Review and discuss the role and purpose of specifications Present a structured approach for organising and producing specifications Explain each of the key steps involved in creating effective specifications Review some methods for assisting in defining requirements Explain how to define the scope and develop the structure for a specification Present methods to assist the writing and editing of specifications Review how specifications should be issued and controlled 1 Introduction Course objectives Review of participants' needs and objectives 2 Specifications in perspective The role and purpose of specifications The impact of specifications on commercial performance The qualities of an effective specification The five key steps of 'POWER' writing: prepare-organise-write-edit-release 3 Step 1: Preparing to write Defining the purpose the specification; integrating the specification and contract Deciding how to specify: when to specify in functional and technical terms Getting the right people involved at the right time; engaging stakeholders Applying procedures for writing, issuing and controlling specifications 4 Step 2: Organising the specification content Scoping the document: scope maps, check lists, structured brainstorming Clarifying requirements; separating needs and desires Dealing with requirements that are difficult to quantify Useful techniques: cost benefit analysis, Pareto analysis Deciding what goes where; typical contents and layout for a specification Creating and using model forms: typical sections and sub sections 5 Step 3: Writing the specification Identifying and understanding the readers needs Choosing and using the right words; dealing with jargon Important words; will, shall, must; building a glossary Using sentence structure and punctuation to best effect Understanding the impact of style, format and appearance Avoiding common causes of ambiguity; being concise and ensuring clarity 6 Step 4: Editing the specification Why editing is difficult; how to develop a personal editing strategy Key areas to review: structure, content, accuracy, clarity, style and grammar Editing tools and techniques 7 Step 5: Releasing and controlling the specification Key requirements for document issue and control Final formatting and publication issues; document approval Requirements management: managing revisions and changes 8 Course review and action planning What actions should be implemented to improve specifications? Conclusion