2981 Software Development courses in Waltham Cross delivered Online

Duration 4 Days 24 CPD hours This course is intended for Primary audience: IT administrators, architects, and business leaders who already manage Nutanix clusters in the datacenter, but who would like more in-depth knowledge of Nutanix datacenter administration. Anyone seeking preparation for the Nutanix Platform Professional (NPP) Advanced certification (in development).Secondary audience: Managers and technical staff seeking more detailed information before making a purchase decision. Overview Protect intellectual property and company data to guarantee business continuity with a complete, advanced data protection strategy.Practice advanced datacenter management procedures using hands on labs.Get the most out of Nutanix systems by maximizing configuration and operation for peak efficiency.Validate new skills by preparing for and completing the Nutanix Platform Professional Advanced certification. The Nutanix AAPM Advanced Administration & Performance Management course is an advanced level training program for experienced Nutanix data center administrators, technicians, and support personnel.The course features comprehensive coverage of performance management for Nutanix clusters, including options for performance optimization, troubleshooting issues and tuning. Learn through hands-on labs to monitor system performance, advanced networking and storage to help optimize data center administration.Advanced Administration explains in detail how to use the major Acropolis services such as Acropolis Block Services (ABS) and Acropolis File Services (AFS). The course also explains how to define and manage assets and applications using Calm, including how to connect to clouds, automation of the Life Cycle Management (LCM) application, and how to implement and configure Self Service Portal and governance.Take advantage of Flash mode to improve system performance as well as how to effectively clone and delete VMs, move them between storage containers, and how to manage VMs (tagging, sizing, migration). Performance ManagementManaging Controller VM ServicesAdvanced Virtual Machine AdministrationImplementing Business Continuity/Disaster RecoveryConfiguring Advanced NetworkingCustomizing Security ServicesManaging Acropolis ServicesPrism Central Management

Duration 3 Days 18 CPD hours This course is intended for This intermediate course is for application programmers who need to write embedded SQL COBOL or PL/I programs in either a DB2 9 or DB2 10 for z/OS environment. Overview Incorporate static SQL statements in an application program Prepare the program for execution Validate execution results are correct Produce code to support multiple rows being returned from the database manager using cursors Identify considerations regarding units of work, concurrency, and restart of programs Identify differences between static and dynamic SQL Provide test data for applications Discuss program and DB2 options relative to performance of static SQL This course enables you to acquire the skills necessary to produce application programs that manipulate DB2 databases. Emphasis is on embedding Structured Query Language (SQL) statements and preparing programs for execution. CV720G;CF82G;DB2 Concepts Identify DB2 family products Explain DB2 workstation component functions Identify DB2 objects Identify the key differences between static SQL and other application alternatives for accessing DB2 data Program Structure I Embed INSERT, UPDATE, DELETE and single-row SELECT statements in application programs Effectively communicate with DB2 when processing NULL values and determining success of statement execution Demonstrate use of DB2 coding aids Code CONNECT statements within an application program Identify connection types and impacts on a unit of work Program for the Call Attach Facility (CAF) Program Preparation Identify the additional steps necessary to prepare a program that contains embedded SQL for execution Describe the functions of the DB2 PRECOMPILE and BIND processes Describe factors relevant to the BIND process, including RUNSTATS positioning, package status, parameters, and authorization requirements Program Structure II Use DECLARE, OPEN, FETCH, and CLOSE CURSOR statements to handle select criteria that may return multiple rows in application programs Issue positioned UPDATE and DELETE statements Identify how scrollable cursors can be used Recovery and Locking Concepts Define a unit of recovery Identify the basic locking strategies used by DB2 Dynamic SQL Introduction Describe the difference between static and dynamic SQL List the types of dynamic statements Code dynamic SQL in a program Managing Test Data Identify methods to insert data into a table Use the LOAD or IMPORT utility Identify the purpose of the RUNSTATS utility Identify the purpose of the REORG utility Performance Considerations Use programming techniques that enhance DB2 application performance by following general guidelines, using indexable predicates, and avoiding unnecessary sorts Identify the access paths available to DB2 List common causes of deadlocks and avoid such causes when possible Use the EXPLAIN tools as aids to develop applications that emphasize performance Additional course details: Nexus Humans CV722 IBM DB2 11 for z/OS Application Programming Workshop 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 CV722 IBM DB2 11 for z/OS Application Programming Workshop 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.

About UX Academy: UX Academy provides live online hands-on training to help you take the next step in your career no matter what level you’re at. Offering Beginner, Intermediate UX courses, Product Design, Strategy and Voice Design developed in collaboration with Amazon.

Artificial Intelligence brings exciting new opportunities to the field of Conversational User Interfaces (CUI). Learn key concepts and proven design methods to deliver cutting-edge experiences and reach better business outcomes. Silvia Podesta is a Designer in the Client Engineering Team at IBM Nordics. She leverages design thinking, service and UX design to help clients identify opportunities for innovation and pioneer transformational experiences through IBM technology.

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Duration 1 Days 6 CPD hours This day-long workshop gives participants a thorough understanding of the iPad iOS operating system. This course is designed for both those who want to learn more about their iPads, those who work in business environments and who want to integrate the iPad into their existing company?s infrastructure, as well as personnel who are responsible for supporting other iPad users. Setting Up The iPad iPad Essentials The Home Screen Launching and Running Apps Changing Screen Orientation Locking the Rotation The Control Center Creating Folders Accessibility and Voice Over Settings General Settings Parameters Passcode Setting Up Notifications Location Services iCloud and Synching your iPad Other Application Settings Multi-Touch Gestures Tap, Touch and Hold Drag, Flick and Swipe Pinch, Rotate and Shake Switching Between Applications Using the Apple Applications Showing and Hiding Applications Closing Documents vs. Quitting Applications Working With Documents Type, Select, Cut, Copy, Paste and Replace Understanding the iPad Keyboard Opening Pages, Numbers and Keynote Accessing Files and Documents Copying files between the iPad and Computer Working with Microsoft Office Connecting To The Internet WI-FI and Bluetooth Devices Through Servers Browsing and Searching The Web Enterprise Network The iPad In Business iOS Security Deployment Seamless Integration Mobile Device Management Printing with AirPrint Creating Passcodes Working with Photos and Camera Photos and Video Recording Video Integrating Photos or Video Into Documents or Presentations Mirroring Video Finding and Installing Apps The App Store Apps for Enterprise Installing and Deleting Apps Resetting the iPad Connecting and Mirroring with the iPhone Battery Issues Tips for Improving Battery Use Rebooting the iPad Hidden Keystrokes Troubleshooting Connectivity Issues ReInstalling Apps Preserving Batter Power Accessibility Functions Additional course details: Nexus Humans iPad For Business 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 iPad For Business 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 Operators, administrators, and architects responsible for the creation, maintenance, or delivery of remote and virtual desktop services. Overview By the end of the course, you should be able to meet the following objectives: Recognize the features and benefits of Horizon Use VMware vSphere to create VMs to be used as desktops for Horizon Create and optimize Windows VMs to create Horizon desktops Install and configure Horizon Agent on Horizon desktop Configure and manage the VMware Horizon Client™ systems and connect the client to a VMware Horizon desktop Configure, manage, and entitle desktop pools of full VMs Configure, manage, and entitle pools of instant-clone desktops Create and use Remote Desktop Services (RDS) desktops and application pools Monitor the Horizon environment using Horizon Console Dashboard and Horizon Help Desk Tool Implement a structured approach to troubleshooting Resolve common issues that occur in a Horizon environment Troubleshoot issues with linked and instant clones Configure the Windows client Identify the correct log level for gathering logs Optimize protocols for the best end-user experience VMware Horizon© 8: Virtual Desktop Bootcamp is a five-day combination course of VMware Horizon 8: Skills for Virtual Desktop Management and VMware Horizon 8: Virtual Desktop Troubleshooting. This training combination gives you the skills to deliver virtual desktops and applications through a single virtual desktop infrastructure platform. You build skills in configuring and managing VMware Horizon 8 through a combination of lecture and hands-on labs. You learn how to configure and deploy pools of virtual machines and how to provide a customized desktop environment to end-users. Additionally, you learn how to resolve common issues that occur in a VMware Horizon environment. You engage in a series of lab exercises to bring existing environment issues to resolution. The exercises mirror real-world troubleshooting use cases. These exercises equip learners with the knowledge and practical skills to manage typical challenges faced by virtual desktop administrators and operators. Course Introduction Introductions and course logistics Course objectives Introduction to VMware Horizon Recognize the features and benefits of VMware Horizon Describe the conceptual and logical architecture of VMware Horizon Introduction to Use Case Define a use case for your virtual desktop and application infrastructure Convert customer requirements to use-case attributes vSphere for Horizon 8 Explain basic virtualization concepts Use VMware vSphere© Client? to access your vCenter Server system and VMware ESXi? hosts Create, provision, and remove a virtual machine VMware Horizon Desktops Create a Windows and a Linux virtual machine using vSphere Optimize and prepare Windows and Linux virtual machines to set up VMware Horizon desktop VMs VMware Horizon Agents Outline the configuration choices when installing Horizon Agent on Windows and Linux virtual machines Create a gold master for Windows Horizon desktops VMware Horizon Pools Identify the steps to set up a template for desktop pool deployment List the steps to add desktops to the VMware Horizon© Connection Server? inventory Compare dedicated-assignment and floatingassignment pools Outline the steps to create an automated pool Define user entitlement Explain the hierarchy of global, pool-level, and userlevel policies VMware Horizon Client Options Describe the different clients and their benefits Access Horizon desktop using various Horizon clients and HTML Configure integrated printing, USB redirection, and the shared folders option Configure session collaboration and media optimization for Microsoft Teams Creating and Managing Instant-Clone Desktop Pools List the advantages of instant clones Explain the provisioning technology used for instant-clone desktop pools Set up an automated pool of instant clones Push updated images to instant-clone desktop pools Creating RDS Desktop and Application Pools Explain the difference between an RDS desktop pool and an automated pool Compare and contrast an RDS session host pool, a farm, and an application pool Create an RDS desktop pool and an application ool Access RDS desktops and application from Horizon Client Use the instant clone technology to automate the build-out of RDSH farms Configure load-balancing for RDSHs on a farm Monitoring VMware Horizon Monitor the status of the VMware Horizon components using the Horizon Administrator console dashboard Monitor desktop sessions using the HelpDesk tool Overview of Virtual Desktop Troubleshooting Structured approach to troubleshooting configuration and operational problems Applying troubleshooting methods

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced attendees who wish to learn and use basic machine learning algorithms and concepts. Students should have skills at least equivalent to the Python for Data Science courses we offer. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to Understand the main concepts and principles of predictive analytics Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects Explore advanced predictive modeling algorithms w with an emphasis on theory with intuitive explanations Learn to deploy a predictive model's results as an interactive application Learn about the stages involved in producing complete predictive analytics solutions Understand how to define a problem, propose a solution, and prepare a dataset Use visualizations to explore relationships and gain insights into the dataset Learn to build regression and classification models using scikit-learn Use Keras to build powerful neural network models that produce accurate predictions Learn to serve a model's predictions as a web application Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This course provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. Hands-on Predictive Analytics with Python is a three-day, hands-on course that guides students through a step-by-step approach to defining problems and identifying relevant data. Students will learn how to perform data preparation, explore and visualize relationships, as well as build models, tune, evaluate, and deploy models. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seabor, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics. The Predictive Analytics Process Technical requirements What is predictive analytics? Reviewing important concepts of predictive analytics The predictive analytics process A quick tour of Python's data science stack Problem Understanding and Data Preparation Technical requirements Understanding the business problem and proposing a solution Practical project ? diamond prices Practical project ? credit card default Dataset Understanding ? Exploratory Data Analysis Technical requirements What is EDA? Univariate EDA Bivariate EDA Introduction to graphical multivariate EDA Predicting Numerical Values with Machine Learning Technical requirements Introduction to ML Practical considerations before modeling MLR Lasso regression KNN Training versus testing error Predicting Categories with Machine Learning Technical requirements Classification tasks Credit card default dataset Logistic regression Classification trees Random forests Training versus testing error Multiclass classification Naive Bayes classifiers Introducing Neural Nets for Predictive Analytics Technical requirements Introducing neural network models Introducing TensorFlow and Keras Regressing with neural networks Classification with neural networks The dark art of training neural networks Model Evaluation Technical requirements Evaluation of regression models Evaluation for classification models The k-fold cross-validation Model Tuning and Improving Performance Technical requirements Hyperparameter tuning Improving performance Implementing a Model with Dash Technical requirements Model communication and/or deployment phase Introducing Dash Implementing a predictive model as a web application Additional course details: Nexus Humans Hands-on Predicitive Analytics with Python (TTPS4879) 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 Hands-on Predicitive Analytics with Python (TTPS4879) 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 3 Days 18 CPD hours This course is intended for This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below Getting Started & Optional Python Quick Refresher Statistics and Probability Refresher and Python Practice Probability Density Function; Probability Mass Function; Naive Bayes Predictive Models Machine Learning with Python Recommender Systems KNN and PCA Reinforcement Learning Dealing with Real-World Data Experimental Design / ML in the Real World Time Permitting: Deep Learning and Neural Networks Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices. This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms Getting Started Installation: Getting Started and Overview LINUX jump start: Installing and Using Anaconda & Course Materials (or reference the default container) Python Refresher Introducing the Pandas, NumPy and Scikit-Learn Library Statistics and Probability Refresher and Python Practice Types of Data Mean, Median, Mode Using mean, median, and mode in Python Variation and Standard Deviation Probability Density Function; Probability Mass Function; Naive Bayes Common Data Distributions Percentiles and Moments A Crash Course in matplotlib Advanced Visualization with Seaborn Covariance and Correlation Conditional Probability Naive Bayes: Concepts Bayes? Theorem Naive Bayes Spam Classifier with Naive Bayes Predictive Models Linear Regression Polynomial Regression Multiple Regression, and Predicting Car Prices Logistic Regression Logistic Regression Machine Learning with Python Supervised vs. Unsupervised Learning, and Train/Test Using Train/Test to Prevent Overfitting Understanding a Confusion Matrix Measuring Classifiers (Precision, Recall, F1, AUC, ROC) K-Means Clustering K-Means: Clustering People Based on Age and Income Measuring Entropy LINUX: Installing GraphViz Decision Trees: Concepts Decision Trees: Predicting Hiring Decisions Ensemble Learning Support Vector Machines (SVM) Overview Using SVM to Cluster People using scikit-learn Recommender Systems User-Based Collaborative Filtering Item-Based Collaborative Filtering Finding Similar Movie Better Accuracy for Similar Movies Recommending movies to People Improving your recommendations KNN and PCA K-Nearest-Neighbors: Concepts Using KNN to Predict a Rating for a Movie Dimensionality Reduction; Principal Component Analysis (PCA) PCA with the Iris Data Set Reinforcement Learning Reinforcement Learning with Q-Learning and Gym Dealing with Real-World Data Bias / Variance Tradeoff K-Fold Cross-Validation Data Cleaning and Normalization Cleaning Web Log Data Normalizing Numerical Data Detecting Outliers Feature Engineering and the Curse of Dimensionality Imputation Techniques for Missing Data Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE Binning, Transforming, Encoding, Scaling, and Shuffling Experimental Design / ML in the Real World Deploying Models to Real-Time Systems A/B Testing Concepts T-Tests and P-Values Hands-on With T-Tests Determining How Long to Run an Experiment A/B Test Gotchas Capstone Project Group Project & Presentation or Review Deep Learning and Neural Networks Deep Learning Prerequisites The History of Artificial Neural Networks Deep Learning in the TensorFlow Playground Deep Learning Details Introducing TensorFlow Using TensorFlow Introducing Keras Using Keras to Predict Political Affiliations Convolutional Neural Networks (CNN?s) Using CNN?s for Handwriting Recognition Recurrent Neural Networks (RNN?s) Using an RNN for Sentiment Analysis Transfer Learning Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters Deep Learning Regularization with Dropout and Early Stopping The Ethics of Deep Learning Learning More about Deep Learning Additional course details: Nexus Humans Machine Learning Essentials with Python (TTML5506-P) 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 Machine Learning Essentials with Python (TTML5506-P) 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 students who want to learn the R programming language, particularly students who want to leverage R for data analysis and data science tasks in their organization. The course is also designed for students with an interest in applying statistics to real-world problems. A typical student in this course should have several years of experience with computing technology, along with a proficiency in at least one other programming language. Overview In this course, you will use R to perform common data science tasks.You will: Set up an R development environment and execute simple code. Perform operations on atomic data types in R, including characters, numbers, and logicals. Perform operations on data structures in R, including vectors, lists, and data frames. Write conditional statements and loops. Structure code for reuse with functions and packages. Manage data by loading and saving datasets, manipulating data frames, and more. Analyze data through exploratory analysis, statistical analysis, and more. Create and format data visualizations using base R and ggplot2. Create simple statistical models from data. In our data-driven world, organizations need the right tools to extract valuable insights from that data. The R programming language is one of the tools at the forefront of data science. Its robust set of packages and statistical functions makes it a powerful choice for analyzing data, manipulating data, performing statistical tests on data, and creating predictive models from data. Likewise, R is notable for its strong data visualization tools, enabling you to create high-quality graphs and plots that are incredibly customizable. This course will teach you the fundamentals of programming in R to get you started. It will also teach you how to use R to perform common data science tasks and achieve data-driven results for the business. Lesson 1: Setting Up R and Executing Simple Code Topic A: Set Up the R Development Environment Topic B: Write R Statements Lesson 2: Processing Atomic Data Types Topic A: Process Characters Topic B: Process Numbers Topic C: Process Logicals Lesson 3: Processing Data Structures Topic A: Process Vectors Topic B: Process Factors Topic C: Process Data Frames Topic D: Subset Data Structures Lesson 4: Writing Conditional Statements and Loops Topic A: Write Conditional Statements Topic B: Write Loops Lesson 5: Structuring Code for Reuse Topic A: Define and Call Functions Topic B: Apply Loop Functions Topic C: Manage R Packages Lesson 6: Managing Data in R Topic A: Load Data Topic B: Save Data Topic C: Manipulate Data Frames Using Base R Topic D: Manipulate Data Frames Using dplyr Topic E: Handle Dates and Times Lesson 7: Analyzing Data in R Topic A: Examine Data Topic B: Explore the Underlying Distribution of Data Topic C: Identify Missing Values Lesson 8: Visualizing Data in R Topic A: Plot Data Using Base R Functions Topic B: Plot Data Using ggplot2 Topic C: Format Plots in ggplot2 Topic D: Create Combination Plots Lesson 9: Modeling Data in R Topic A: Create Statistical Models in R Topic B: Create Machine Learning Models in R