213 Logistics courses in Ryton delivered Live Online

Reggie Love Keynote Only We understand that not every client/attendee can make it along to a full day of training in London, but would still like the opportunity to tune into a keynote speech - we listened to the feedback, and we've decided to support clients/attendees who do wish to listen in to Reggie's Keynote the opportunity to do so.  No matter where you are located, you'll still get the chance to hear how Reggie supported one of the most well-known President of the USA in living memory.  Who is Reggie Love? Reggie Love is a Senior Advisor at Apollo Global Management. Reggie’s responsibilities include supporting and developing strategy for human capital, public engagement and DE&I initiatives.  Reggie previously served as Partner at RON Transatlantic EG from 2012-2019, an international financial holding company with interests in the financial services, logistics, energy, industrial and beer sectors in the United States, Latin America and Europe. The group, via its portfolio companies, encompasses assets under management and revenues of over $14 billion. Mr. Love’s work at the firm focused on financial services and energy investments.  Prior to joining Transatlantic, Reggie served at the White House as personal aide to President Barack Obama from 2009-2011, where he was responsible for assisting with the coordination and completion of the President’s daily schedule as well as coordinating with other White House offices to set up long and medium range planning. Love moved to the District of Columbia in 2006 to work for then Senator Obama after short stints in the NFL with the Dallas Cowboys and Green Bay Packers.  Love is a graduate of Duke University, holds and MBA from the Wharton School at the University of Pennsylvania and is the author of the New York Times Best Seller, Power Forward, My Presidential Education, which is a collection of stories and principles learned during his time working for President Obama and being a captain for the all-time most winning NCAA coach, Mike Krzyzewski.  Love also serves on the boards of Cox Media Group, Blade Air Mobility, National Summer Learning Association, Center for Environmental Farming Systems, Organizing for Action, Military Bowl Committee, Team Works, and The New Renaissance. Please note the following: This is the Keynote element ONLY - Not the entire session. Cameras will be off for the keynote. Questions are sought out two weeks prior, which will then be sent to the training team for the event. Please be advised that we may not be able to get to every question on the day - the training team will pass this across to the speaker before the session to ensure we can answer most of the questions on the day. Who will attend? PA's EA's Administrative roles This workshop is open to females and males, including trans women/males and non-binary professionals. Group Rate Discounts Group discounts are currently on request for this session - please call 0800 949 9111 or email support@ownyoursuccess.co.ukto discuss and confirm any group bookings. 

SAP Training London: Overview What is SAP? SAP is one of the largest ERP(Enterprise Resource Planning) software in the world. It provides end to end solution for Financials, Manufacturing, Logistics, Distributions etc. SAP applications, built around their latest R/3 system, provide the capability to manage financial, asset, and cost accounting, production operations and materials, personnel, plants, and archived documents. The R/3 system runs on a number of platforms including Windows and MAC and uses the client/server model. SAP Business Cases/ Live Project Our SAP training courses are designed in a way which gives the students maximum exposure of real-life scenario through no of business cases and guidance on implementation by professional SAP Consultants. Free SAP Access Free SAP Sandbox Access provided for 6 months so that you can practice even after your SAP training course. Certificate of Attendance- You receive a certificate once you finish the course from Osborne Training,  You can also do an external exam to receive certification from SAP (Optional), for details on exam booking you may visit the SAP website. SAP Training Modules Available Financial & Controlling (FICO) High-Performance Analytic Appliance (HANA) Supply Relationship Management (SRM) Customer Relationship Management (CRM)

Duration 4.375 Days 26.25 CPD hours This course is intended for This course provides a jump start on learning material that relates to the CMMC program, and to the planned Certified Professional (CP) certification in particular. It discusses the CMMC program, the current CMMC Model, and other CMMC materials as they exist today. The course may be of interest to individuals who wish to pursue the CMMC-CP certification when it is released. It may also be useful to stakeholders in organizations who will be involved in CMMC assessments, and who wish to gain a general understanding of the program and its requirements for their business planning purposes Overview In this course, you will examine the current CMMC model (Version 1.02), framework, context, and application within the DoD, as well as the expectations and requirements that will be imposed upon organizations that do business with the DoD. It will also help you to identify threats to cybersecurity and privacy within an IoT ecosystem and implement appropriate countermeasures. You will gain the skills to: · Identify risks within the defense supply chain and the established standards for managing them. · Describe how the CMMC model ensures compliance with federal acquisition regulations. · Identify responsibilities of the CMMC Certified Professional, including appropriate ethics and behavior. · Identify regulated information and establish the Certification and Assessment scope boundaries for evaluating the systems that protect that regulated information. · Evaluate OSC readiness and determine the objective evidence you intend to present to the assessor. · Use the NIST 800-171A and CMMC Assessment Guide to assess objective evidence for processes and practices. · Implement and evaluate practices required to meet CMMC maturity level 1. · Implement and evaluate processes and practices required to meet CMMC maturity level 2. · Implement and evaluate processes and practices required to meet CMMC maturity level 3. · Identify processes and practices required to meet CMMC maturity levels 4 and 5. · Work through the logistics of a CMMC assessment, including planning for and conducting the assessment, as well as any follow-up processes, such as remediation and adjudication. The Cybersecurity Maturity Model Certification (CMMC), managed by the CMMC Accreditation Body (CMMC-AB), is a program through which an organization's cybersecurity program is measured by their initial and ongoing compliance with applicable cybersecurity practices as well as their integration of corresponding policies and plans into their overall business operations. By Fiscal Year 2026, all organizations providing products or services to the United States Department of Defense (DoD) must obtain at least a Maturity Level 1 certification under this program. Managing Risk within the Defense Supply Chain Topic A: Identify Threats to the Defense Supply Chain Topic B: Identify Regulatory Responses Against Threats Ensuring Compliance through CMMC Topic A: Identify Limitations of Self-Certification Topic B: Identify Benefits of CMMC Topic C: Describe the CMMC Model Architecture Performing the General Responsibilities of a CMMC CP Topic A: Identify Responsibilities of the CMMC CP Topic B: Demonstrate Appropriate Ethics and Behavior Identifying and Scoping Regulated Information Topic A: Identify Regulated Information Topic B: Establish the Certification and Assessment Scope Boundaries Initiating the Assessment Process Topic A: Evaluate Readiness Topic B: Determine Objective Evidence Assessing Objective Evidence Topic A: Assess the Practices Using the CMMC Assessment Guides Topic B: Assess the Processes Using the CMMC Assessment Guide Level 3 Implementing and Evaluating CMMC Level 1 TTopic A: Maturity Level 1 Domains and Practices Topic B: Determine Scope Boundaries at Maturity Level 1 Topic C: Perform a Maturity Level 1 Gap Analysis Topic D: Perform a Maturity Level 1 Evidence Validation Implementing and Evaluating CMMC Levels 2 and 3 Topic A: Process Maturity Requirements for CMMC Levels 2 and 3 Topic B: Maturity Level 2 Practices Topic C: Maturity Level 3 Practices Identifying CMMC Levels 4 and 5 Topic A: Maturity Level 4 Processes and Practices Topic B: Maturity Level 5 Processes and Practices Working Through a CMMC Assessment Topic A: Define the Assessment Logistics Topic B: Resolve Assessment Related Issues Appendix A: Additional Documentation the CP Should Study

Duration 1 Days 6 CPD hours This course is intended for Security operations personnel, including analysts and incident responders Overview By the end of the course, you should be able to meet the following objectives: Utilize Carbon Black EDR throughout an incident Implement a baseline configuration for Carbon Black EDR Determine if an alert is a true or false positive Fully scope out an attack from moment of compromise Describe Carbon Black EDR capabilities available to respond to an incident Create addition detection controls to increase security This course teaches you how to use the VMware Carbon Black© EDR? product during incident response. Using the SANS PICERL framework, you will configure the server and perform an investigation on a possible incident. This course provides guidance on using Carbon Black EDR capabilities throughout an incident with an in-depth, hands-on, scenariobased lab. Course Introduction Introductions and course logistics Course objectives VMware Carbon Black EDR & Incident Response Framework identification and process Preparation Implement the Carbon Black EDR instance according to organizational requirements Identification Use initial detection mechanisms Process alerts Proactive threat hunting Incident determination Containment Incident scoping Artifact collection Investigation Eradication Hash banning Removing artifacts Continuous monitoring Recovery Rebuilding endpoints Getting to a more secure state Lessons Learned Tuning Carbon Black EDR Incident close out

Diploma in Maritime Business: 100% on line - start whenever you want - attend at your pace - from anywhere and anytime. Register today!

SAP Courses Overview SAP is one of the largest ERP(Enterprise Resource Planning) software in the world. It provides end to end solution for Financials, Manufacturing, Logistics, Distributions etc. SAP applications, built around their latest R/3 system, provide the capability to manage financial, asset, and cost accounting, production operations and materials, personnel, plants, and archived documents. The R/3 system runs on a number of platforms including Windows and MAC and uses the client/server model. SAP Business Cases/ Live Project Our SAP training courses are designed in a way which gives the students maximum exposure of real-life scenario through no of business cases and guidance on implementation by professional SAP Consultants. Certificate of Attendance- You receive a certificate once you finish the course from Osborne Training,  SAP Certification During the training, you get access to the SAP sandbox which continues even after training finishes until the completion of the Internship. You can also do an external exam to receive certification from SAP (Optional), for details on exam booking you may visit the SAP website. SAP Training Modules Available Financial & Controlling (FICO) High-Performance Analytic Appliance (HANA) Supply Relationship Management (SRM) Customer Relationship Management (CRM) Free SAP Access Free SAP Sandbox Access provided for 6 months so that you can practice even after your SAP training course.

Duration 1 Days 6 CPD hours This course is intended for System administrators, security operations personnel including analyst and managers. Overview By the end of the course, you should be able to meet the following objectives: Describe the components and capabilities of application control. Manage and configure the Carbon Black App Control server based on organizational requirements. Create policies to control enforcement levels and agent functionality. Implement rules to support the organization's security posture. Use the Carbon Black App Control tools to understand agent and server data. This course teaches you how to use the VMware Carbon Black© App ControlTM product and leverage the capabilities to configure and maintain the system according to their organization?s security posture and organizational policies. This course provides an in-depth, technical understanding of the Carbon Black App Control product through comprehensive coursework and hands-on scenario-based labs. Course Introduction Introductions and course logistics Course objectives Login Accounts and Groups User accounts User roles Policies Modes and Enforcement Levels Rule sets Notifiers Agent side display notification use Computer Details View agent information Related views Local Approval Timed Policy Override Use case and functionality Software Approvals Methods and use cases Custom Rules Rule type and use cases Tools Meters and Alerts Events Agent and server events Baseline Drift Changes in a baseline

Duration 1 Days 6 CPD hours This course is intended for System administrators and security operations personnel, including analysts and managers Overview By the end of the course, you should be able to meet the following objectives: Describe the components and capabilities of VMware Carbon Black Cloud Endpoint Standard Identify the architecture and data flows for Carbon Black Cloud Endpoint Standard communication Perform searches across endpoint data to discover suspicious behavior Manage the Carbon Black Cloud Endpoint Standard rules based on organizational requirements Configure rules to address common threats Evaluate the impact of rules on endpoints Process and respond to alerts Describe the different response capabilities available from VMware Carbon Black CloudTM This course teaches you how to use the VMware Carbon Black Cloud Endpoint? Standard product and leverage the capabilities to configure and maintain the system according to your organization?s security posture and policies. This course provides an in-depth, technical understanding of the product through comprehensive coursework and hands-on scenario-based labs. Course Introduction Introductions and course logistics Course objectives Data Flows and Communication Hardware and software requirements Architecture Data flows Searching Data Creating searches Analyzing events Search operators Advanced queries Policy Components Rules Local scanner Sensor capabilities Prevention Capabilities Using Rules Rule types Rule creation Reputation priority Configuring rules Evaluating rule impact Processing Alerts Alert triage Alert actions Response Capabilities Using quarantine Using live response Hash banning

Duration 1 Days 6 CPD hours This course is intended for System administrators and security operations personnel, including analysts and managers Overview By the end of the course, you should be able to meet the following objectives: Manage and configure the Carbon Black App Control sever based on organizational requirements. Implement rules to support business processes and automatic approvals. Identify scenarios and use cases for Custom rules and Event rules. Describe common troubleshooting scenarios for the Carbon Black App Control server. Describe common troubleshooting scenarios for the Carbon Black App Control Windows agent. This course teaches you how to configure and scope the rules within VMware Carbon Black© App ControlTM product to maintain the system according to your organization?s security posture and organizational policies. Additionally, this course covers troubleshooting both the server and the agent for Carbon Black App Control and how to identify issues that impact normal operations. This course provides an in-depth, technical understanding of the Carbon Black App Control product through comprehensive coursework and hands-on scenario-based labs. Course Introduction Introductions and course logistics Course objectives Custom Rules Basics Execute / Write action rules Precedence Paths tom Rules Best Practices Rule Triad Rule multiplication Rule Types Custom rule type overview Optimizing Custom Rules Evaluating events Event Rules Creating and editing Testing before implementing Creating and editing Testing before implementing Server versus agent issues Server Capabilities Tools, logs, common issues, scenarios Agent Capabilities Tools, logs, common issues, scenarios

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