722 Software Development courses in Leighton Buzzard delivered Live Online

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

Duration 4 Days 24 CPD hours This course is intended for This course is geared for experienced skilled Java developers, software developers, data scientists, machine learning experts or others who wish to transtion their coding skills to Scala, learning how to code in Scala and apply it in a practical way. This is not a basic class. Overview Working in a hands-on learning environment led by our expert instructor you'll: Get comfortable with Scala's core principles and unique features, helping you navigate the language confidently and boosting your programming skills. Discover the power of functional programming and learn techniques that will make your code more efficient, maintainable, and enjoyable to write. Become proficient in creating dynamic web applications using the Play Framework, and easily connect to databases with the user-friendly Slick library. Master concurrency programming with Akka, empowering you to build scalable and fault-tolerant applications that excel in performance. Enhance your testing skills using ScalaTest and ScalaCheck, ensuring the reliability and quality of your Scala applications, while having fun in the process. Explore the fascinating world of generative AI and GPT technologies, and learn how to integrate them into your projects, adding a touch of innovation and intelligence to your Scala solutions. If your team requires different topics, additional skills or a custom approach, our team will collaborate with you to adjust the course to focus on your specific learning objectives and goals. Discover the power of Scala programming in our comprehensive, hands-on technical training course designed specifically for experienced object-oriented (OO) developers. Scala is a versatile programming language that combines the best of both OO and functional programming paradigms, making it ideal for a wide range of projects, from web applications to big data processing and machine learning. By mastering Scala, you'll be able to develop more efficient, scalable, and maintainable applications. Fast Track to Scala Programming for OO / Java Developers is a four day hands-on course covers the core principles of Scala, functional programming, web application development, database connectivity, concurrency programming, testing, and interoperability between Scala and Java. Additionally, you'll explore cutting-edge generative AI and GPT technologies, learning how to integrate them into your Scala applications for intelligent suggestions or automation. Throughout the course you?ll explore the latest tools and best practices in the Scala ecosystem, gaining valuable knowledge and experience that can be directly applied to your day-to-day work. With 50% of the course content dedicated to hands-on labs, you'll gain practical experience applying the concepts you've learned across various projects, such as building functional web applications, connecting to databases, designing modular components, and implementing concurrency. Upon completing the course, you'll have a solid understanding of the language and its features, empowering you to confidently apply your new skills in data science and machine learning projects. You'll exit well-prepared to create efficient, scalable, and maintainable Scala applications, regardless of the complexity of your projects. Introduction to Scala Scala features and benefits Comparing Scala with Java and other OO languages Installing Scala and setting up the development environment Object-Oriented Programming in Scala Classes and objects Traits, mixins, and inheritance Companion objects and factories Encapsulation and polymorphism Functional Programming Basics Pure functions and referential transparency Higher-order functions and currying Immutability and persistent data structures Pattern matching and recursion Having Fun with Functional Data Structures Lists, sets, and maps in Scala Folding and reducing operations Stream processing and lazy evaluation For-comprehensions Building Web Applications in Functional Style Introduction to Play Framework Functional web routing and request handling JSON handling with Play-JSON Middleware and functional composition Connecting to a Database Introduction to Slick library Database configuration and setup Querying and updating with Slick Transactions and error handling Building Scalable and Extensible Components Modular architecture and design patterns Dependency injection with MacWire Type classes and type-level programming Implicit parameters and conversions Concurrency Programming & Akka Introduction to Akka framework and Actor model Actor systems and message passing Futures and Promises Supervision and fault tolerance Building Confidence with Testing Introduction to ScalaTest and ScalaCheck Unit testing and property-based testing Test-driven development in Scala Mocking and integration testing Interoperability between Scala and Java Calling Java code from Scala Using Java libraries in Scala projects Converting Java collections to Scala collections Writing Scala code that can be called from Java Using Generative AI and GPT Technologies in Scala Programming Overview of GPT and generative AI Integrating GPT with Scala applications Use cases and practical examples Additional course details: Nexus Humans Fast Track to Scala Programming Essentials for OO / Java Developers (TTSCL2104) 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 Fast Track to Scala Programming Essentials for OO / Java Developers (TTSCL2104) 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.

Powerful 1-hour Python workshop course understand Python Basics. Practical. Instructor-led. Online. Learn to code a Password Validator in 1 hour. 

Duration 2 Days 12 CPD hours This course is intended for This is an introductory-level course for Administrators who are new to Jira (this is NOT for experienced Jira admin or users). Students should have a background in basic administration. Overview This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Working in a hands-on learning environment, exploring several practical use cases that provide context as to where and when to use Jira, students will learn about: user management global and project permissions project roles schemes configuration of issue types, workflows, and screens Tracking issues is a critical component of any project management strategy. JIRA provides a web based single repository for creating, tracking and reporting on feature requests, bugs reported, or managing workflow. Geared for administrators new to Jira, JumpStart to Jira for Administrators | Jira Administration is a two-day, hands-on course that explores the most important tasks required to set up Jira, providing students with ample hands-on experience using common administration tasks. This hands-on course enables the Student to administer a JIRA instance and ?learn by doing?. The focus of this course is on Best Practices, and practical skills. Getting started with JIRA Administration JIRA conceptual review Core concepts Terminology Infrastructure JIRA roles Groups vs Roles Overview Project roles Creating a role Project scaling JIRA User management Project Resolution Project status Resolved status Resolution date Schemes Overview Project scope schemes Adding users to schemes Issue type schemes Notification schemes Permission schemes Issue security schemes working with schemes JIRA as a Platform Overview What can be configured Basic JIRA project setup Advanced project setup Workflows Overview Designing a workflow Defining a workflow Implementing a workflow Deploying workflows Workflow events Transitions and sub-tasks Custom Fields Overview Field types Field context Limiting contexts Adding contexts Screens and field configuration Best practices for custom fields User Lifecycle Overview Adding users Adding third-party users Modifying users Deactivating users Remote JIRA Access Overview Emails SQL REST Webhooks XML and RSS Command Line Interface Integrating JIRA with other applications Migrating Data into JIRA Overview Migration steps The CSV importer JIRA cloud migration Summary and Best Practices Looking back at the ?Big Picture? Optional - Jira Certification Prep Review Additional course details: Nexus Humans JumpStart to Jira for Administrators | Jira Administration (TTDV7540) 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 JumpStart to Jira for Administrators | Jira Administration (TTDV7540) 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 4 Days 24 CPD hours Overview Topics Include:Installation of a multi-node Kubernetes cluster using kubeadm, and how to grow a cluster.Choosing and implementing cluster networking.Various methods of application lifecycle management, including scaling, updates and roll-backs.Configuring security both for the cluster as well as containers.Managing storage available to containers.Learn monitoring, logging and troubleshooting of containers and the cluster.Configure scheduling and affinity of container deployments.Use Helm and Charts to automate application deployment.Understand Federation for fault-tolerance and higher availability. In this vendor agnostic course, you'll learn the installation, configuration and administration of a production-grade Kubernetes cluster. Introduction Linux Foundation Linux Foundation Training Linux Foundation Certifications Laboratory Exercises, Solutions and Resources Distribution Details Labs Basics of Kubernetes Define Kubernetes Cluster Structure Adoption Project Governance and CNCF Labs Installation and Configuration Getting Started With Kubernetes Minikube kubeadm More Installation Tools Labs Kubernetes Architecture Kubernetes Architecture Networking Other Cluster Systems Labs APIs and Access API Access Annotations Working with A Simple Pod kubectl and API Swagger and OpenAPI Labs API Objects API Objects The v1 Group API Resources RBAC APIs Labs Managing State With Deployments Deployment Overview Managing Deployment States Deployments and Replica Sets DaemonSets Labels Labs Services Overview Accessing Services DNS Labs Volumes and Data Volumes Overview Volumes Persistent Volumes Passing Data To Pods ConfigMaps Labs Ingress Overview Ingress Controller Ingress Rules Labs Scheduling Overview Scheduler Settings Policies Affinity Rules Taints and Tolerations Labs Logging and Troubleshooting Overview Troubleshooting Flow Basic Start Sequence Monitoring Logging Troubleshooting Resources Labs Custom Resource Definition Overview Custom Resource Definitions Aggregated APIs Labs Kubernetes Federation Overview Federated Resources Labs Helm Overview Helm Using Helm Labs Security Overview Accessing the API Authentication and Authorization Admission Controller Pod Policies Network Policies Labs

Duration 3 Days 18 CPD hours

Duration 1 Days 6 CPD hours

Duration 5 Days 30 CPD hours

Duration 1.5 Days 9 CPD hours This course is intended for This course is designed for software developers or anyone interested in building Android applications. Overview Install Android Studio. Writing a Java Program. Run a Java Program. Write a Java Comment. Understand Java Variables and Their Data Types. Acquiring Good Knowledge of Java Control Flow Statements. Understanding The Java Methods and Arrays. Understanding the Object-Oriented Programming (OOP) Concepts and Java Class. Developing applications for Android? systems requires basic knowledge of Java programming language. Introductory course that focuses on the fundamentals of Java programming language, its framework, syntax, and paradigm. First Step in Java The History of Java How Java Programs work? Install Java JDK and JRE Why did Google choose Java over other programming languages? Android OS Structure Install Android Studio Create and Run Java Projects Creating an Android Project (Java Project) Using Android Studio Writing a Java Program Java Methods Running a Java Program Write a Comment Java Variables and Their Data Type Control Flow Statements Introduction IF ? Else Statement If?Else and Else?If... Statement If Else and Logical Operators Switch Statement While Loop Do-while Loop For Loop The Break Statement The Continue Statement Methods and Arrays Introduction Method Structure Call Method by Value Call Method by Reference Arrays Enter Data to Java Program Object-Oriented Programming (OOP) Concepts Java Class Additional course details: Nexus Humans Java Fundamentals for Android Development 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 Java Fundamentals for Android Development 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 4 Days 24 CPD hours This course is intended for Anyone who is preparing to build and run Kubernetes clusters Overview By the end of the course, you should be able to meet the following objectives: Build, test, and publish Docker container images Become familiar with YAML files that define Kubernetes objects Understand Kubernetes core user-facing concepts, including pods, services, and deployments Use kubectl, the Kubernetes CLI, and become familiar with its commands and options Understand the architecture of Kubernetes (Control plane and its components, worker nodes, and kubelet) Learn how to troubleshoot issues with deployments on Kubernetes Apply resource requests, limits, and probes to deployments Manage dynamic application configuration using ConfigMaps and Secrets Deploy other workloads, including DaemonSets, Jobs, and CronJobs Learn about user-facing security using SecurityContext, RBAC, and NetworkPolicies This four-day course is the first step in learning about Containers and Kubernetes Fundamentals and Cluster Operations. Through a series of lectures and lab exercises, the fundamental concepts of containers and Kubernetes are presented and put to practice by containerizing and deploying a two-tier application into Kubernetes. Course Introduction Introductions and objectives Containers What and Why containers Building images Running containers Registry and image management Kubernetes Overview Kubernetes project Plugin interfaces Building Kubernetes Kubectl CLI Beyond Kubernetes Basics Kubernetes objects YAML Pods, replicas, and deployments Services Deployment management Rolling updates Controlling deployments Pod and container configurations Kubernetes Networking Networking within a pod Pod-to-Pod Networking Services to Pods ClusterIP, NodePort, and LoadBalancer Ingress controllers Service Discovery via DNS Stateful Applications in Kubernetes Stateless versus Stateful Volumes Persistent volumes claims StorageClasses StatefulSets Additional Kubernetes Considerations Dynamic configuration ConfigMaps Secrets Jobs, CronJobs Security Network policy Applying a NetworkPolicy SecurityContext runAsUser/Group Service accounts Role-based access control Logging and Monitoring Logging for various objects Sidecar logging Node logging Audit logging Monitoring architecture Monitoring solutions Octant VMware vRealize Operations Manager Cluster Operations Onboarding new applications Backups Upgrading Drain and cordon commands Impact of an upgrade to running applications Troubleshooting commands VMware Tanzu portfolio overview