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Master Scrum, a popular framework used to develop software applications, using Agile technology. This course provides you with step-by-step instructions for learning Scrum and gaining a strong foundation toward various Scrum certifications. Learn Scrum with a follow-along experience and apply Scrum in your project team at work.

Duration 5 Days 30 CPD hours This course is intended for Data center administrators Data center engineers Systems engineers Server administrators Network managers Cisco integrators and partners Data center designers Technical solutions architects Network architects Overview After taking this course, you should be able to: Describe the foundations of data center networking Describe Cisco Nexus products and explain the basic Cisco NX-OS functionalities and tools Describe Layer 3 first-hop redundancy Describe Cisco FEX connectivity Describe Ethernet port channels and vPCs Introduce switch virtualization, machine virtualization, and describe network virtualization Compare storage connectivity options in the data center Describe Fibre Channel communication between the initiator server and the target storage Describe Fibre Channel zone types and their uses Describe NPV and NPIV Describe data center Ethernet enhancements that provide a lossless fabric Describe FCoE Describe data center server connectivity Describe Cisco UCS Manager Describe the purpose and advantages of APIs Describe Cisco ACI Describe the basic concepts of cloud computing The Understanding Cisco Data Center Foundations (DCFNDU) v1.1 course helps you prepare for entry-level data center roles. In this course, you will learn the foundational knowledge and skills you need to configure Cisco© data center technologies including networking, virtualization, storage area networking, and unified computing. You will get an introduction to Cisco Application Centric Infrastructure (Cisco ACI), automation and cloud computing. You will get hands-on experience with configuring features on Cisco Nexus Operating System (Cisco NX-OS) and Cisco Unified Computing System (Cisco UCS). This course does not lead directly to a certification exam, but it does cover foundational knowledge that can help you prepare for several CCNP and other professional-level data center courses and exams. Describing the Data Center Network Architectures Cisco Data Center Architecture Overview Three-Tier Network: Core, Aggregation, and Access Spine-and-Leaf Network Two-Tier Storage Network Describing the Cisco Nexus Family and Cisco NX-OS Software Cisco Nexus Data Center Product Overview Cisco NX-OS Software Architecture Cisco NX-OS Software CLI Tools Cisco NX-OS Virtual Routing and Forwarding Describing Layer 3 First-Hop Redundancy Default Gateway Redundancy Hot Standby Router Protocol Virtual Router Redundancy Protocol Gateway Load Balancing Protocol Describing Cisco FEX Server Deployment Models Cisco FEX Technology Cisco FEX Traffic Forwarding Cisco Adapter FEX Describing Port Channels and vPCs Ethernet Port Channels Virtual Port Channels Supported vPC Topologies Describing Switch Virtualization Cisco Nexus Switch Basic Components Virtual Routing and Forwarding Cisco Nexus 7000 VDCs VDC Types VDC Resource Allocation VDC Management Describing Machine Virtualization Virtual Machines Hypervisor VM Manager Describing Network Virtualization Overlay Network Protocols VXLAN Overlay VXLAN BGP EVPN Control Plane VXLAN Data Plane Cisco Nexus 1000VE Series Virtual Switch VMware vSphere Virtual Switches Introducing Basic Data Center Storage Concepts Storage Connectivity Options in the Data Center Fibre Channel Storage Networking VSAN Configuration and Verification Describing Fibre Channel Communication Between the Initiator Server and the Target Storage Fibre Channel Layered Model FLOGI Process Fibre Channel Flow Control Describing Fibre Channel Zone Types and Their Uses Fibre Channel Zoning Zoning Configuration Zoning Management Describing Cisco NPV Mode and NPIV Cisco NPV Mode NPIV Mode Describing Data Center Ethernet Enhancements IEEE Data Center Bridging Priority Flow Control Enhanced Transmission Selection DCBX Protocol Congestion Notification Describing FCoE Cisco Unified Fabric FCoE Architecture FCoE Initialization Protocol FCoE Adapters Describing Cisco UCS Components Physical Cisco UCS Components Cisco Fabric Interconnect Product Overview Cisco IOM Product Overview Cisco UCS Mini Cisco IMC Supervisor Cisco Intersight Describing Cisco UCS Manager Cisco UCS Manager Overview Identity and Resource Pools for Hardware Abstraction Service Profiles and Service Profile Templates Cisco UCS Central Overview Cisco HyperFlex Overview Using APIs Common Programmability Protocols and Methods How to Choose Models and Processes Describing Cisco ACI Cisco ACI Overview Multitier Applications in Cisco ACI Cisco ACI Features VXLAN in Cisco ACI Unicast Traffic in Cisco ACI Multicast Traffic in Cisco ACI Cisco ACI Programmability Common Programming Tools and Orchestration Options Describing Cloud Computing Cloud Computing Overview Cloud Deployment Models Cloud Computing Services Lab outline Explore the Cisco NX-OS CLI Explore Topology Discovery Configure HSRP Configure vPCs Configure VRF Explore the VDC Elements Install ESXi and vCenter Configure VSANs Validate FLOGI and FCNS Configure Zoning Configure Unified Ports on a Cisco Nexus Switch and Implement FCoE Explore the Cisco UCS Server Environment Configure a Cisco UCS Service Profile Configure Cisco NX-OS with APIs Explore the Cisco UCS Manager XML API Management Information Tree Explore Cisco ACI

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

Register on the JavaScript Functions today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career. The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials. Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get an e-certificate as proof of your course completion. The JavaScript Functions is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablet, and smartphones. The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly! What You Get With The JavaScript Functions Receive a e-certificate upon successful completion of the course Get taught by experienced, professional instructors Study at a time and pace that suits your learning style Get instant feedback on assessments 24/7 help and advice via email or live chat Get full tutor support on weekdays (Monday to Friday) Course Design The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace. You are taught through a combination of Video lessons Online study materials Certification Upon successful completion of the course, you will be able to obtain your course completion e-certificate free of cost. Print copy by post is also available at an additional cost of £9.99 and PDF Certificate at £4.99. Who Is This Course For: The course is ideal for those who already work in this sector or are an aspiring professional. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge. Requirements: The online training is open to all students and has no formal entry requirements. To study the JavaScript Functions, all your need is a passion for learning, a good understanding of English, numeracy, and IT skills. You must also be over the age of 16.  Course Content Unit 01: Introduction Introduction 00:02:00 Why use JavaScript functions? 00:06:00 Unit 02: Defining and Invoking Functions Defining and Invoking Functions 00:04:00 Demo: Invoking and Defining JavaScript functions 00:07:00 Alternate ways to define functions 00:01:00 Demo: Alternate ways to define functions 00:04:00 Unit 03: Function Scope What is function scope? 00:03:00 Demo: Exploring Function Scope 00:04:00 Child Function Scope 00:02:00 Demo: Child Function Scope 00:06:00 Unit 04: Composing Functions What is function composition? 00:02:00 Demo: Currying Functions 00:05:00 Factory Functions 00:01:00 Demo: Factory Functions 00:04:00 Unit 05: Asynchronous Functions What are callback functions? 00:01:00 Demo: Callback Functions 00:04:00 Promises, Async and Generators 00:06:00 The 'this' keyword & demo 00:04:00 Self-Invoking Functions & demo 00:03:00 Error Catching & demo 00:04:00 Object Creators & demo 00:03:00 Course Summary 00:02:00 Frequently Asked Questions Are there any prerequisites for taking the course? There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course. Can I access the course at any time, or is there a set schedule? You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience. How long will I have access to the course? For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime. Is there a certificate of completion provided after completing the course? Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks. Can I switch courses or get a refund if I'm not satisfied with the course? We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase. How do I track my progress in the course? Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course. What if I have technical issues or difficulties with the course? If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.

Take your sales people from average to high performance. Motivate and develop experienced sales professionals with some new insights and learning. Applying NLP principles, techniques and models, this workshop will introduce the core attitudes and behaviours that differentiate the excellent sales person from the average one. The programme will help participants: Understand and adopt the mindset and beliefs needed for sales excellence Build rapport and connect with buyers at a deeper and more personal level Recognise some of the thinking and language patterns that make each individual unique Ask powerful questions to further understand the unique world of the individual and how they make decisions Apply tools and techniques to empathise with clients - seeing things from their perspectives Tailor their sales approach to the individual buyer's style, and talk in their language Influence with integrity and sell to organisations and individuals successfully 1 Introduction Aims and objectives of the programme Personal introductions and objectives Workshop overview 2 An introduction to NLP and sales excellence with NLP An overview of NLP and applying it to selling The pillars of NLP The NLP model of communication The difference that makes the difference 3 Building enhanced rapport Defining rapport and why it is important when selling Going beyond the initial small talk Building relationships with individual decision-makers Matching and mirroring Levels of rapport 4 Understanding the buyer's personal buying map How we take in, filter and process information How we judge others based on our own experiences of the world The different ways in which we communicate when selling Recognising and understanding the language and thinking patterns of others Adapting your sales communication style to different buyers 5 Making sense of the buying process How we filter information through our senses Understanding how we see, hear and experience the world Visual, auditory and kinaesthetic buyers Listening for key insights What different buyers want from you to help them to buy Applying sensory awareness to the sales process 6 Successful sales mindset The connection between thoughts and actions The sales beliefs of excellence Identifying negative thoughts and beliefs that are holding you back How to change your mindset Adopting the sales beliefs of excellence 7 Powerful questions Reviewing and honing your questioning skills Understanding the questions that great sales people ask Avoiding assumptions Clean language questions Getting to the bottom of it - precision questions Turbo-charging how you qualify 8 Influencing with integrity Understanding empathy Stepping into the buyer's shoes Speaking the buyer's language Tailoring your sales approach to the individual Match, pace, lead - how to take your buyer with you 9 Putting it all together Personal learning summary and action plans

This concise and comprehensive course takes you through the basic and advanced topics of Ansible, explaining all the concepts clearly and thoroughly. You will not only master the concepts but also learn how to use Ansible with cloud services and containers.

4-Hour Discovery Day - NLP Training Near Me | Ely Wellbeing Neuro Linguistic Programming (NLP) is a collection of models and techniques that provide insights into human psychological processes. Through its various models, strategies and tools, NLP helps people to change their lives by taking control of their thoughts, feelings and behaviours. Our 4 Hour Discovery Day gives you an opportunity to learn the basics of Neuro Linguistic Programming; how it

This course is your guide to deep learning in Python with Keras. You will discover the Keras Python library for deep learning and learn how to use it to develop and evaluate deep learning models.

Become a vulnerability assessment professional with the Nessus scanner for networks and learn to analyze and rank vulnerabilities, both manually and through automation. Explore vulnerability scanning with Metasploit and write custom professional reports. Learn to import results of Nmap in Nessus and create VA Project Worksheets to analyze vulnerability assessments.

Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step