384 Sciences courses in Aylesbury delivered Live Online

Duration 3 Days 18 CPD hours This course is intended for Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary. Overview By the end of this course, you will be able to build your own marketing reporting and interactive dashboard solutions. The course starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation.As you make your way through the course, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding sections, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. Data Preparation and Cleaning Data Models and Structured Data pandas Data Manipulation Data Exploration and Visualization Identifying the Right Attributes Generating Targeted Insights Visualizing Data Unsupervised Learning: Customer Segmentation Customer Segmentation Methods Similarity and Data Standardization k-means Clustering Choosing the Best Segmentation Approach Choosing the Number of Clusters Different Methods of Clustering Evaluating Clustering Predicting Customer Revenue Using Linear Regression Understanding Regression Feature Engineering for Regression Performing and Interpreting Linear Regression Other Regression Techniques and Tools for Evaluation Evaluating the Accuracy of a Regression Model Using Regularization for Feature Selection Tree-Based Regression Models Supervised Learning: Predicting Customer Churn Classification Problems Understanding Logistic Regression Creating a Data Science Pipeline Fine-Tuning Classification Algorithms Support Vector Machine Decision Trees Random Forest Preprocessing Data for Machine Learning Models Model Evaluation Performance Metrics Modeling Customer Choice Understanding Multiclass Classification Class Imbalanced Data Additional course details: Nexus Humans Data Science for Marketing Analytics 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 Data Science for Marketing Analytics 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 The target audience for the SRE Practitioner course are professionals including: Anyone focused on large-scale service scalability and reliability Anyone interested in modern IT leadership and organizational change approaches Business Managers Business Stakeholders Change Agents Consultants DevOps Practitioners IT Directors IT Managers IT Team Leaders Product Owners Scrum Masters Software Engineers Site Reliability Engineers System Integrators Tool Providers Overview After completing this course, students will have learned: Practical view of how to successfully implement a flourishing SRE culture in your organization. The underlying principles of SRE and an understanding of what it is not in terms of anti-patterns, and how you become aware of them to avoid them. The organizational impact of introducing SRE. Acing the art of SLIs and SLOs in a distributed ecosystem and extending the usage of Error Budgets beyond the normal to innovate and avoid risks. Building security and resilience by design in a distributed, zero-trust environment. How do you implement full stack observability, distributed tracing and bring about an Observability-driven development culture? Curating data using AI to move from reactive to proactive and predictive incident management. Also, how you use DataOps to build clean data lineage. Why is Platform Engineering so important in building consistency and predictability of SRE culture? Implementing practical Chaos Engineering. Major incident response responsibilities for a SRE based on incident command framework, and examples of anatomy of unmanaged incidents. Perspective of why SRE can be considered as the purest implementation of DevOps SRE Execution model Understanding the SRE role and understanding why reliability is everyone's problem. SRE success story learnings This course introduces a range of practices for advancing service reliability engineering through a mixture of automation, organizational ways of working and business alignment. Tailored for those focused on large-scale service scalability and reliability. SRE Anti-patterns Rebranding Ops or DevOps or Dev as SRE Users notice an issue before you do Measuring until my Edge False positives are worse than no alerts Configuration management trap for snowflakes The Dogpile: Mob incident response Point fixing Production Readiness Gatekeeper Fail-Safe really? SLO is a Proxy for Customer Happiness Define SLIs that meaningfully measure the reliability of a service from a user?s perspective Defining System boundaries in a distributed ecosystem for defining correct SLIs Use error budgets to help your team have better discussions and make better data-driven decisions Overall, Reliability is only as good as the weakest link on your service graph Error thresholds when 3rd party services are used Building Secure and Reliable Systems SRE and their role in Building Secure and Reliable systems Design for Changing Architecture Fault tolerant Design Design for Security Design for Resiliency Design for Scalability Design for Performance Design for Reliability Ensuring Data Security and Privacy Full-Stack Observability Modern Apps are Complex & Unpredictable Slow is the new down Pillars of Observability Implementing Synthetic and End user monitoring Observability driven development Distributed Tracing What happens to Monitoring? Instrumenting using Libraries an Agents Platform Engineering and AIOPs Taking a Platform Centric View solves Organizational scalability challenges such as fragmentation, inconsistency and unpredictability. How do you use AIOps to improve Resiliency How can DataOps help you in the journey A simple recipe to implement AIOps Indicative measurement of AIOps SRE & Incident Response Management SRE Key Responsibilities towards incident response DevOps & SRE and ITIL OODA and SRE Incident Response Closed Loop Remediation and the Advantages Swarming ? Food for Thought AI/ML for better incident management Chaos Engineering Navigating Complexity Chaos Engineering Defined Quick Facts about Chaos Engineering Chaos Monkey Origin Story Who is adopting Chaos Engineering Myths of Chaos Chaos Engineering Experiments GameDay Exercises Security Chaos Engineering Chaos Engineering Resources SRE is the Purest form of DevOps Key Principles of SRE SREs help increase Reliability across the product spectrum Metrics for Success Selection of Target areas SRE Execution Model Culture and Behavioral Skills are key SRE Case study Post-class assignments/exercises Non-abstract Large Scale Design (after Day 1) Engineering Instrumentation- Instrumenting Gremlin (after Day 2)

Nowadays not only do we rely on our commercial and sales staff to hit that bottom line but we expect our engineers and project teams to play their part too - not only through their engineering and management skills but by behaving in a commercially minded way in their dealings with their counterparts in customer or supplier organisations. This means understanding, amongst other things, the issues surrounding the commencement of work ahead of contract, having a clear contract baseline, recognising the broader implications of contract change, the need for timeliness and the consequences of failing to meet the contracted timetable. This practical one-day programme has been designed specifically to give engineers, project staff and others just that understanding. The course is designed principally to provide engineers and project staff with an appreciation of contractual obligations, liabilities, rights and remedies so that they understand the implications of their actions. It is also suitable for business development staff who are negotiating contracts on behalf of the business. The main focus of the day is on creating an awareness of when a situation may have commercial implications that would harm an organisation's business interests if not recognised and handled appropriately and how taking a positive but more commercial approach to those situations can lead to a more positive outcome for the business. As well as providing an understanding of the commercial imperatives the day also focuses on specific areas affecting engineers and project staff, such as the recognition and management of change, the risks when working outside the contract and managing delays in contracts. The course identifies the different remedies that may apply according to the reasons for the delay and provides some thoughts on pushing back should such situations arise. On completion of this programme the participants will: appreciate the need for contractual controls and will have a better understanding of their relevance and how they can be applied, particularly the issues of starting work ahead of contract, implementing changes and inadvertently creating a binding contract by their behaviour; have gained an understanding of the terminology and procedural issues pertaining to contracting within a programme; and be more commercially aware and better equipped for their roles. 1 Basic contract law - bidding and contract formation Purpose of a contract Contract formation - the key elements required to create a legally binding agreement Completeness and enforceability Express and implied terms Conditions v warranties The use of, and issues arising from, standard forms of sale and purchase Use of 'subject to contract' Letters of intent Authority to commit 2 Change management Recognising changes to a contracted requirement Pricing change Implementation and management of change 3 Key contracting terms and conditions By the end of this module participants will be able to identify the key principles associated with: Pricing Getting paid and retaining payment Cashflow Delivery and acceptance Programme delaysExamining some reasons for non-performance...Customer failureContractor's failureNo fault delays ... and the consequences of non-performance: Damages claimsLiquidated damagesForce majeureContinued performance Waiver clauses and recent case law Use of best/reasonable endeavours Contract termination 4 Warranties, indemnities and liability Express and implied warranties Limiting liability 5 Protection of information Forms of intellectual property Background/foreground intellectual property Marking intellectual property Intellectual property rights Copyright Software Confidentiality agreements Internet

Course Overview: It is important that everybody who works in the care environment recognises the signs of potential eating and drinking difficulties and is able to support service users to eat and drink. This course combines both theory and practical sessions to equip those who work in care settings with this knowledge.   Course Aims: Define Dysphagia Identify the main parts of the human mouth and pharynx Recognise signs and symptoms of aspiration Know when to refer a service user Recognise good positions at mealtimes Experience food textures and being fed in different positions Management responsibilities

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

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 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.

M&O of engineering plant and services is becoming more important to the success of the core business. To select the right M&O technique for specific plant and equipment it is necessary to know what options are available, what they deliver and how they should be implemented. This course will help you consider options and techniques that provide best value based on that thorough understanding of the business need. M&O will increasingly be required to demonstrate it is delivering the optimum of cost and value and the main purpose of the course is to show how this can be achieved. Note: this is a purely indicative list of topics that can be covered. The content, duration, objectives and material used would all be adapted to match your specific requirements. This course will help you: Determine what the business needs from the M&O service Determine the cost and value of the various M&O options Prepare and present the business case for the selected M&O strategy Introduce meaningful KPIs based on performance-based service by the M&O provider Undertake a review of current M&O and make recommendations for improvement Introduce energy and carbon management performance criteria in M&O Better deal with project risk and uncertainties Motivate others to deliver a reliable and cost effective M&O service Note: this is a purely indicative list of topics that can be covered. The content, duration, objectives and material used would all be adapted to match your specific requirements. 1 What is maintenance and what is operation? - their relative importance to the business 2 Comparison of the various maintenance options and techniques, including: Planned preventative Run to failure Condition monitoring Business focused Business critical Total productive Reliability centred maintenance 3 Forms of contracts and service, including: Comprehensive Input driven Output driven Limited replacement Performance based M&O 4 Selecting the right options and making the business case 5 Continuous commissioning as a tool for delivering best value 6 Case studies

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. 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. Additional course details: Nexus Humans Python for Data Science Primer: Hands-on Technical Overview (TTPS4872) 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 Primer: Hands-on Technical Overview (TTPS4872) 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 for VMware Horizon should enroll in this course. These individuals are responsible for the creation, maintenance, and or delivery of remote and virtual desktop services. Additional duties can include the implementation, support, and administration of an organization's end-user computing infrastructure. Overview By the end of the course, you should be able to meet the following objectives: Assess the business and application requirements of an environment Analyze design choices and design an EUC infrastructure architecture that addresses the needs of the environment Design an EUC architecture that addresses the needs of the organization and follows VMware best practices Document a design that can be implemented Design a comprehensive VMware EUC solution This five-day course presents a methodology for designing a VMware end-user computing (EUC) solution. The design methodology includes recommendations for the types of information and data that must be gathered and analyzed to make sound design decisions for the client systems, the desktop options, the VMware vSphere© infrastructure, VMware Horizon©, VMware Horizon© Cloud ServiceTM, VMware Workspace ONE© UEM, VMware Workspace ONE© AccessTM, VMware NSX-TTM, and VMware Unified Access GatewayTM. VMware best practices are presented for each phase of the design process. In this course, you apply your new knowledge by working with other participants to design a VMware EUC solution for a real-world project. Course Introduction Introductions and course logistics Course objectives VMWare EUC Design Methodology Recognize the characteristics of a good design. Identify the phases of VMware EUC Design Methodology Presenting Solutions to Executive Management Identify behaviors that can affect team performance negatively List common mistakes when working in a team Describe how to tailor a presentation to the company?s values, and stakeholders? backgrounds and roles List questions you should ask to identify how to address a problem a client is trying to solve Infrastructure Assessment and Services Definition Define customer business objective Define a use case for your virtual desktop and application infrastructure Convert customer requirements to use-case attributes Horizon Architecture and Components Determine the components required for a Horizon on-premises or Horizon Cloud deployment Implement the design considerations and recommendations for various Horizon components Analyze the use-case scenarios for various the Horizon deployment options Component Design: Horizon Pods Design a single-site Horizon desktop block and pod configuration for a given use case Design Cloud Pod Architecture for multisite pool access Design Control Plane Services Architecture for multisite pool access Component Design: App Volumes and Dynamic Environment Manager Identify the functionalities of the components in App Volumes Logical Architecture Implement the recommended practices when designing an application delivery mechanism using App Volumes Component Design: Workspace ONE Map the Horizon desktop building block and the Horizon management building block to VMware vSphere. Identify factors and design decisions that determine the sizing for ESXi hosts Workspace ONE & Horizon Infrastructure Design Design the environment resources required to support a Workspace ONE and Horizon deployment Identify factors and design decisions that determine the sizing for ESXi host Discuss the factors that determine the sizing for shared storage Identify the design decisions related to bandwidth utilization Discuss the implications of using load balancing and traffic management Identify factors and design decisions that determine the sizing of the Azure pods Design Integration and Delivery List the platform components that needs to be integrated along with their dependent services. Integrate Workspace ONE and Horizon platform components. Additional course details:Notes Delivery by TDSynex, Exit Certified and New Horizons an VMware Authorised Training Centre (VATC) Nexus Humans VMware End User Computing: Design 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 VMware End User Computing: Design 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.