1586 Basic courses in Sheffield delivered Live Online

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 4 Days 24 CPD hours This course is intended for This course is appropriate for anyone who wants to create applications or modules to automate and simplify common tasks with Perl. Overview Working within in an engaging, hands-on learning environment, guided by our expert web development, PHP practitioner, students will learn to: Create a working script that gets input from the command line, the keyboard, or a file Use arrays to store and process data from files Create formatted reports Use regular expressions Use the appropriate types of variables and data structures Refactor duplicate code into subroutines and modules What is available in the standard library Use shortcuts and defaults, and what they replace Introduction to Perl Programming Essentials is an Introductory-level practical, hands-on Perl scripting training course that guides the students from the basics of writing and running Perl scripts to using more advanced features such as file operations, report writing, the use of regular expressions, working with binary data files, and using the extensive functionality of the standard Perl library. Students will immediately be able to use Perl to complete tasks in the real world. Session: An Overview of Perl What is Perl? Perl is compiled and interpreted Perl Advantages and Disadvantages Downloading and Installing Perl Which version of Perl Getting Help Session: Creating and running Perl Programs Structure of a Perl program Running a Perl script Checking syntax and warnings Execution of scripts under Unix and Windows Session: Basic Data and I/O Numeric and Text literals Math operators and expressions Scalar variables Default values Writing to standard output Command line arguments Reading from the standard input Session: Logic and Loops About flow control The if statement and Boolean values Using unless and elsif Statement modifiers warn() and die() The conditional construct Using while loop and its variants Using the for loop Exiting from loops Session: Lists and Arrays The list data type Accessing array elements Creating arrays List interpolation Arrays and memory Counting elements Iterating through an array List evaluation Slices and ranges Session: Reading and writing text files File I/O Overview Opening a file Reading text files Writing to a text file Arrays and file I/O Using the <> operator Session: List functions Growing and shrinking arrays The split() function Splitting on whitespace Assigning to literal lists The join() function The sort() function Alternate sort keys Reversing an array Session: Formatting output Using sprintf() and printf() Report formatting overview Defining report formats The write() function Advanced filehandle magic Session: Hashes Hash overview Creating hashes Hash attributes Traversing a hash Testing for existence of elements Deleting hash elements Session: References What is a reference? The two ways to create references References to existing data References to anonymous data Dereferencing scalar, array, and ash references Dereferencing elements of arrays and hashes Multidimensional arrays and other data structures Session: Text and Regular Expressions String length The substr() function The index() and rindex() functions String replication Pattern matching and substitution Regular expressions Session: Raw file and data access Opening and closing raw (binary) files Reading raw data Using seek() and tell() Writing raw data Raw data manipulation with pack() and unpack() Session: Subroutines and variable scope Understanding packages Package and Lexical variables Localizing builtin variables Declaring and calling subroutines Calling subroutines Passing parameters and returning values Session: Working with the operating system Determining current OS Environment variables Running external programs User identification Trapping signals File test operators Working with files Time of day Session: Shortcuts and defaults Understanding $_ shift() with no array specified Text file processing Using grep() and Using map() Command-line options for file processing Session: Data wrangling Quoting in Perl Evaluating arrays Understanding qw( ) Getting more out of the <> operator Read ranges of lines Using m//g in scalar context The /o modifier Working with embedded newlines Making REs more readable Perl data conversion Session: Using the Perl Library The Perl library Old-style library files Perl modules Modules bundled with Perl A selection of modules Getting modules from ActiveState Getting modules from CPAN Using Getopt::Long Session: Some Useful Tools Sending and receiving files with Net::FTP Using File::Find to search for files and directories Grabbing a Web page Some good places to find scripts Perl man pages for more information Zipping and unzipping files

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

These events are designed to introduce the BOOK & basic ideas behind Understanding & Dealing with Everyday Racism The Six Stages Framework

Duration 3 Days 18 CPD hours This course is intended for This course is intended for intermediate to advanced Business Analysts who are looking to improve their skills for eliciting, analyzing, documenting, validating, and communicating requirements. Overview Obtain a thorough understanding of the core responsibilities of the business analyst Understand the main professional associations and standards supporting business analysts in the industry Discuss and explore the components of each of the domains/knowledge areas that comprise the work of business analysis Recognize the importance of properly defining the business need prior to engaging in requirements activities Formulate a strong understanding of the concepts that comprise strategy analysis Obtain experience with identifying and analyzing stakeholders Decipher between project and product scope and successfully use models to communicate scope Thoroughly understand and identify the various requirements categories and be able to recognize requirements of various types Explore business rules analysis Understand the benefits of process modeling and the common modeling language of BPMN Discuss process models and how the techniques can capture details about the as-is/to-be environment Learn how to properly prepare and conduct interviews Explore the components of use cases Learn what it means to package requirements Obtain hands-on experience with a number of business analysis techniques and gain hands-on experience eliciting, defining, and writing requirements. This course provides students a clear understanding of all the facets of the business analysis role, including a thorough walkthrough of the various domain/knowledge areas that comprise the business analysis profession. Students are provided an opportunity to try their hand at several business analysis techniques to assist with improving their skills in stakeholder identification, scope definition, and analyzing, documenting, and modeling requirements. Introduction to Business Analysis What is business analysis Benefits and challenges of business analysis Project success factors A Closer Look at the Business Analyst Role Definition of a business analyst Responsibilities of a business analyst Importance of communication/collaboration BA role vs. PM role Project roles involved in requirements IIBA/PMI and the goals of a professional association Purpose for having a BA standard IIBA?s BABOK© Guide and PMI?s Practice Guide in Business Analysis Business analysis beyond project work Business analysis core concepts Business analysis perspectives IIBA and PMI certifications for business analysts Workshop: Choose Your Project Supporting the Project Portfolio (Enterprise Analysis) Define Strategy Analysis When to perform Strategy Analysis Components of Strategy Analysis Defining the business need Envisioning the Product and Project Defining business requirements The importance of stakeholders Stakeholder identification Tips for analyzing stakeholders Techniques for managing stakeholder lists Discussion: Who is involved in strategy analysis? Workshops: Define the Business Need, Write Business Requirements, and Identify Stakeholders Understanding and Defining Solution Scope Defining solution scope Techniques for defining solution scope Applying the brainstorming technique Project scope vs. Product scope Finding solution boundaries The Context Diagram Actors and key information Workshop: Draw a Context Diagram Understanding Requirements What is a requirement? Requirement types Business, Stakeholder, Solution, and Transition requirements Assumptions and constraints Business rules Taxonomy of business rules Decision tables How to write simple calculations Requirements vs. business rules Document requirements Workshop ? Document Requirements Elicitation and Process Modeling Why do we model processes? What is Business Process Management? Using a modeling notation ?As Is? vs. ?To Be? modeling Why use BPMN? Basic BPM notation Business Process Modeling ? A case study Business Process Realignment ?As Is? vs. ?To Be? activity diagrams Workshop: Create a Business Process Model Planning & Eliciting Requirements Interviewing ? what and why? Preparing for an effective interview Types of questions to ask Sequencing questions Active listening techniques Planning for elicitation Conducting the interview Establishing rapport Active Listening Feedback techniques Types of elicitation techniques Workshops: Planning for Elicitation and Conducting an Elicitation Session Use Case & User Story Analysis What is an Actor? Types of Actors How to ?find? Use Cases? Diagramming Use Cases Tips on naming Use Cases Explaining scenarios The use case template Components of a use case Scenario examples Best practices for writing Use Cases Scenarios and flows Alternate and exception flows Exercises: Drawing a Use Case Diagram, Writing the Main Success Scenario, and Writing Alternate and Exception Scenarios Analyzing & Documenting Requirements Requirements and Use Cases Non-Functional requirements User Interface Requirements UI Data Table Reporting requirements Data requirements Data accessibility requirements Characteristics of good requirements The business requirements document (BRD) BRD vs. Functional Requirements Specification Preparing the requirements package Requirements traceability Workshops: Develop a User Interface, Analyzing Requirements, and Tracing requirements Additional Resources Useful books and links on writing effective requirements

Duration 2 Days 12 CPD hours This course is intended for Executives, Project Managers, Business Analysts, Business and IT stakeholders working with analysts, Quality and process engineers, technicians, corrective action coordinators or managers; supervisors, team leaders, and process operators; anyone who wants to improve their ability to solve recurring problems. Overview At the completion of this course, you should be able to:Identify the different types of tools and techniques availableApply change management successfullyReview what to look for when applying business case thinking to Root Cause AnalysisDevelop a process to systematically approach problems Problem determination skills cannot be acquired by reading a book. They mostly come from experience. After this course, you will have a general understanding of a structured problem determination approach, which you can then apply in your daily work. RCA Foundation Concepts and Objectives Section Learning Objectives Discuss Definitions ? IT Perspective Discuss What is a problem and why do they exist? Exercise 1a: Why is RCA important What is Root Cause? RCA Benefits and Approaches Exercise 1b: Why are multiple approaches important? Event and Casual Analysis Exercise 1c: Apply Event and Causal Analysis Event and Causal Analysis: Exercise 1c Worksheet RCA Tools for each approach Exercise 1D: Prepare for Case Study Section Summary and Conclusions Enhance Use of RCA Tools Why use a Particular Method Tool: Change Analysis Change Analysis Examples Exercise 2a: Apply Change Analysis Exercise 2b: Change Analysis Worksheet Tool: How to Resolve Conflict Tool: 5 Why?s Example Learning Management Problem Exercise 2b: Apply 5 Why?s Sequence-of-Events Exercise 2b: Worksheet Tool: Cause and Effect Exercise 2c: Apply Cause and Effect Exercise 2c: Cause and Effect Worksheet Tool: Fault Tree Analysis Why do we use Fault Tree Analysis? How does it work? Fault Tree Diagram Symbols Example #1 of FTA: Car Hits Object Exercise 2D: Apply Fault Tree Analysis Tool: Failure Modes and Effects Analysis (FMEA) Example: Failure Modes and Effects Analysis Exercise 2E: Apply FMEA Tool: Design / Application Review Exercise 2d: Apply Design/Application Review Section summary & conclusions Problem Resolution & Prevention Section Objectives The Secret of Solving Problems A Note about Statistical Control A Note about Fire Fighting Technique: Business Process Mapping Example: IGOE Exercise 3A: Problem as IGOE Exercise 3A: IGOE Template Technique: Lean Six Sigma and DMAIC Lean Six Sigma Benefits Exercise 3B: Apply Lean Six Sigma Importance of Understanding the Business Process The Business Process Mandate Technique: Process Modeling Graphical Notation Standard (BPMN) What is Business Process Modeling Notation (BPMN)? Benefits of BPMN Basic Components of BPMN Exercise 3C: Use BPMN to Document a Current State Technique: Business Process Maturity Model Five Levels of Maturity Exercise 3D: Appraise Business Process Maturity Level Section Summary and Conclusions Capability Improvement for RCA Section Learning Objectives Steps in Disciplined Problem Solving RCA as a RCA Process Key RCA Role Considerations Exercise 4A: Apply RCA to RCA Process Sustainable RCA Improvement Organizational Units Process Area Goals, Practices Specific and General Practices Specific Practice Examples Software Maturity Survey Exercise 4B: Apply Strengths, Weakness, Opportunities and Threats (SWOT) to RCA SWOT Analysis Worksheet Recognize the importance of the Change Management component in your RCA implementation Using the ADKAR Model to Communicate Change Review ADKAR© Model ? Awareness of the need for change Desire to participate and support the change Knowledge on how to change Ability to implement required skills and behaviors Reinforcement to sustain the change Exercise 4C: Create an Ability Checklist The ADKAR Model: Reinforcement Exercise 4D: Find two examples of how ADKAR will be different Section Summary and Conclusions Course Summary & Conclusions Plan the Proposal and Business Case Example: 1 Page Business Case Exercise 5A: Creating Action Plans Resource Guide Questions Additional course details: Nexus Humans BA17 - Advanced Root Cause Analysis 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 BA17 - Advanced Root Cause Analysis 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 2 Days 12 CPD hours This course is intended for This class assumes some prior experience with Git, plus basic coding or programming knowledge. 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. Working in a hands-on learning environment led by our expert team, students will explore: Getting Started with Collaboration Understanding the GitHub Flow Branching with Git Local Git Configuration Working Locally with Git Collaborating on Your Code Merging Pull Requests Viewing Local Project History Streaming Your Workflow with Aliases Workflow Review Project: GitHub Games Resolving Merge Conflicts Working with Multiple Conflicts Searching for Events in Your Code Reverting Commits Helpful Git Commands Viewing Local Changes Creating a New Local Repository Fixing Commit Mistakes Rewriting History with Git Reset Merge Strategies: Rebase This is a fast-paced hands-on course that provides you with a solid overview of Git and GitHub, the web-based version control repository hosting service. While the examples in this class are related to computer code, GitHub can be used for other content. It offers the complete distributed version control and source code management (SCM) functionality of Git as well as adding its own features. It provides access control and several collaboration features such as bug tracking, feature requests, task management, and wikis for every project. Getting Started with The GitHub Ecosystem What is Git? Exploring a GitHub Repository Using GitHub Issues Activity: Creating A GitHub Issue Using Markdown Understanding the GitHub Flow The Essential GitHub Workflow Branching with Git Branching Defined Activity: Creating a Branch with GitHub Introduction Class Diagram Interaction Diagrams Sequence Diagrams Communication Diagrams State Machine Diagrams Activity Diagram Implementation Diagrams Local Git Configuration Checking your Git version Git Configuration Levels Viewing your configurations Configuring your username and email Configuring autocrif Working Locally with Git Creating a Local copy of the repo Our favorite Git command: git status Using Branches locally Switching branches Activity: Creating a New File The Two Stage Commit Collaborating on Your Code Collaboration Pushing your changes to GitHub Activity: Creating a Pull Request Exploring a Pull Request Activity: Code Review Merging Pull Requests Merge Explained Merging Your Pull Request Updating Your Local Repository Cleaning Up the Unneeded Branches Viewing Local Project History Using Git Log Streaming Your Workflow with Aliases Creating Custom Aliases Workflow Review Project: GitHub Games User Accounts vs. Organization Accounts Introduction to GitHub Pages What is a Fork? Creating a Fork Workflow Review: Updating the README.md Resolving Merge Conflicts Local Merge Conflicts Working with Multiple Conflicts Remote Merge Conflicts Exploring Searching for Events in Your Code What is GitHub? What is Git bisect? Finding the bug in your project Reverting Commits How Commits are made Safe operations Reverting Commits Helpful Git Commands Moving and Renaming Files with Git Staging Hunks of Changes Viewing Local Changes Comparing changes with the Repository Creating a New Local Repository Initializing a new local repository Fixing Commit Mistakes Revising your last commit Rewriting History with Git Reset Understanding reset Reset Modes Reset Soft Reset Mixed Reset Hard Does gone really mean gone? Getting it Back You just want that one commit Oops, I didn?t mean to reset Merge Strategies: Rebase About Git rebase Understanding Git Merge Strategies Creating a Linear History Additional course details: Nexus Humans Introduction to GITHub for Developers (TTDV7551) 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 Introduction to GITHub for Developers (TTDV7551) 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 Server administrators Network engineers Systems engineers Consulting systems engineers Technical solutions architects Network administrators Storage administrators Network managers Cisco integrators and partners Overview After taking this course, you should be able to: Describe and implement Fibre Channel, zoning, and N-Port Virtualization (NPV) features on Cisco UCS Describe and implement Fibre Channel over Ethernet (FCoE) on Cisco UCS Describe Cisco UCS policies for service profiles Describe Cisco Adapter Fabric Extender (FEX) and Single Root I/O Virtualization Describe and implement Role-Based Access Control (RBAC) on Cisco UCS Describe and implement external authentication providers on Cisco UCS Manager Describe and implement key management on Cisco UCS Manager Describe Cisco UCS Director Describe and implement Cisco Intersight Describe the scripting options for Cisco UCS Manager Describe and implement monitoring on Cisco UCS Manager The Configuring Cisco Unified Computing System (DCCUCS) v1.0 shows you how to deploy, secure, operate, and maintain Cisco Unified Computing System? (Cisco UCS©) B-series blade servers, Cisco UCS C-Series, and S-Series rack servers for use in data centers. You will learn how to implement management and orchestration software for Cisco UCS. You will gain hands-on practice: configuring key features of Cisco UCS, Cisco UCS Director, and Cisco UCS Manager; implementing UCS management software including Cisco UCS Manager and Cisco Intersight?; and more. Implementing Cisco UCS Storage Area Network (SAN) SAN Introduction Cisco UCS Fabric Interconnect Fibre Channels modes Named VSANs Cisco UCS Fibre Channel and FCoE Storage Connectivity Describing Cisco UCS Policies for Service Profiles Storage Policies and Profiles Basic Input Output System (BIOS) Policies Boot Policy Intelligent Platform Management Interface (IPMI) Policies Scrub Policies Maintenance Policies Describing Cisco Adapter FEX and Single Root I/O Virtualization Cisco FEX Overview Cisco Adapter FEX Single Root I/O Virtualization Implementing RBAC on Cisco UCS RBAC in Cisco UCS Users, Roles, and Privileges Functions of Organizations and Locales Effective Rights of a User Implementing External Authentication Providers Options for External Authentication Providers Implementing Key Management on Cisco UCS Manager Public Key Infrastructure Implementing Cisco UCS Director Cisco UCS Director Overview Policies, Virtual Data Centers, and Catalogs Cisco UCS Director Virtualization Support Managing Compute with Cisco UCS Director Cisco UCS Manager Orchestration Self-Service Portal Reporting and Monitoring in Cisco UCS Director Implementing Cisco Intersight Cisco UCS Director Overview Important Features of Cisco Intersight Describing the Scripting Options for Cisco UCS Manager Cisco UCS Manager XML API Cisco UCS Management Information Tree Managed Object Browser Cisco UCS PowerTool Cisco UCS Python Software Development Kit (SDK) Implementing Key Management on Cisco UCS Manager Public Key Infrastructure Implementing Cisco Intersight Cisco Intersight Overview Important Features of Cisco Intersight Describing the Scripting Options for Cisco UCS Manager Cisco UCS Manager XML API Cisco UCS Management Information Tree Managed Object Browser Cisco UCS Manager PowerTool Cisco UCS Python SDK Implementing Monitoring on Cisco UCS Manager Logging Sources in Cisco UCS Manager Port Monitoring Capabilities of Cisco UCS Manager Simple Network Management Protocol (SNMP) Security Ramifications Cisco UCS Manager Call Home Feature Lab outline Configure Pod-Specific Device Aliases Configure Zoning Configure VSANs in Cisco UCS Manager Configure Unified Ports on Cisco UCS Fabric Interconnects Install and Boot VMware Elastic Sky X Integrated (ESXi) on Cisco UCS from the FCoE Logical Unit Number (LUN) via FCoE Configure RBAC Configure Cisco UCS Manager to Authenticate Users via Open Lightweight Directory Access Protocol (OpenLDAP) Configure a Trusted Point and Key Ring in Cisco UCS Manager Configure Cisco UCS with Cisco Intersight Configure Cisco UCS Manager Using Scripting Implement Syslog and Call Home Additional course details: Nexus Humans Cisco Configuring Cisco Unified Computing System v1.0 (DCCUCS) 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 Cisco Configuring Cisco Unified Computing System v1.0 (DCCUCS) 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.

Have you always been intrigued by dreamcatchers and want to learn the art of weaving your own, or do you know someone who loves them and want to gift them one? This ancient art is has gone through a lot of morphism and yet the old techniques are still here as always. Learn to weave a traditional dreamcatcher.

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