51 Linear courses in Cardiff delivered Live Online

This course will show delegates how to appraise projects in Excel, incorporate and communicate updates as they happen and ultimately understand how to convert an opportunistic property investment into a core asset that can be sold on.

This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open-source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. This course includes hands-on activities for each topic area.

About this Training Course Geomechanical evaluations are about the assessment of deformations and failure in the subsurface due to oil & gas production, geothermal operations, CO2 storage and other operations. All geomechanical evaluations include four types of modelling assumptions, which will be systematically addressed in this training, namely: 1. Geometrical modelling assumption: Impact of structural styles on initial stress and stress redistribution due to operations 2. Formation (or constitutive) behaviour: Linear elastic and non-linear behaviour, associated models and their parameters, and methods how to constrain these using 3. Initial stress: Relation with structural setting and methods to quantify the in-situ stress condition 4. Loading conditions: Changes in pore pressure and temperature on wellbore and field scale This 5 full-day course starts with the determination of the stresses in the earth, the impact of different structural styles, salt bodies, faulting and folding on the orientation of the three main principal stress components. Different (field) data sources will be discussed to constrain their magnitude, while exercises will be made to gain hands-on experience. Subsequently, the concepts of stress and strain will be discussed, linear elasticity, total and effective stress and poro-elasticity in 1D, 2D and 3D, as well as thermal expansion. Participants will be able to construct and interpret a Mohr-circles. Also, different failure mechanisms and associated models (plastic, viscous) will be discussed. All these concepts apply on a material point level. Next, geomechanics on the wellbore scale is addressed, starting with the stress distribution around the wellbore (Kirsch equations). The impact of mudweight on shear and tensile failure (fracturing) will be calculated, and participants will be able to determine the mudweight window stable drilling operations, while considering well deviation and the use of oil-based and water-based muds (pore pressure penetration). Fracturing conditions and fracture propagation will be addressed. Field-scale geomechanics is addressed on the fourth day, focussing on building a 3D geomechanical model that is fit-for-purpose (focussing on the risks that need evaluation). Here, geological interpretation (layering), initial stress and formation property estimation (from petrophysical logs and lab experiments) as well as determining the loading conditions come together. The course is concluded with interpretation of the field-wide geomechanical response to reservoir depletion with special attention to reservoir compaction & subsidence, well failure and fault reactivation & induced seismicity. Special attention is paid to uncertainties and formulating advice that impacts decision-making during development and production stages of a project. This course can also be offered through Virtual Instructor Led Training (VILT) format. Training Objectives Upon completing of this course, the participants will be able to: Identify potential project risks that may need a geomechanical evaluation Construct a pressure-depth plot based on available field data (density logs, (X)LOT, FIT, RFT) Employ log-based correlation function to estimate mechanical properties Produce a simplified, but appropriate geometrical (layered, upscaled) model that honours contrasts in initial stress, formation properties and loading conditions, including Construct and interpret a Mohr-circle for shear and tensile failure Calculate the mud weight that leads to shear and tensile failure (fracturing conditions) Identify potential lab experiments to measure required formation properties Describe the workflow and data to develop a field-wide fit-for-purpose geomechanical model Discuss the qualitative impact of pressure and temperature change on the risk related to compaction, well failure, top-seal integrity and fault reactivation Target Audience This course is intended for Drilling Engineers, Well Engineers, Production Technologists, Completion Engineers, Well Superintendents, Directional Drillers, Wellsite Supervisors and others, who wish to further their understanding of rock mechanics and its application to drilling and completion. There is no specific formal pre-requisite for this course. However, the participants are requested to have been exposed to drilling, completions and production operations in their positions and to have a recommended minimum of 3 years of field experience. Course Level Intermediate Trainer Your expert course leader has over 30 years of experience in the Oil & Gas industry, covering all geomechanical issues in the petroleum industry for Shell. Some of his projects included doing research and providing operational advice in wellbore stability, sand failure prediction, and oil-shale retortion among others. He guided multi-disciplinary teams in compaction & subsidence, top-seal integrity, fault reactivation, induced-seismicity and containment. He was also involved in projects related to Carbon Capture Storage (CCS). He is the founding father of various innovations and assessment tools, and developed new insights into the root causes seismicity induced by Oil & Gas production. Furthermore, he was the regional coordinator for technology deployment in Africa, and Smart Fields (DOFF, iField) design advisor for Shell globally. He was responsible for the Geomechanical competence framework, and associated virtual and classroom training programme in Shell for the last 10 years. He served as one of the Subject Matter Expert (SME) on geomechanics, provided Technical Assurance to many risk assessments, and is a co-author of Shell's global minimun standard on top-seal integry and containment. He has a MSc and PhD in Civil Engineering and computational mechanics from Delft University of Technology, The Netherlands. Training experience: Developed and delivered the following (between 2010 and 2020): The competence framework for the global geomechanical discipline in Shell Online Geomechanical training programs for petroleum engineers (post-doc level) The global minimum standard for top-seal integrity assessment in Shell Over 50 learning nuggets with Subject Matter Experts Various Shell virtual Geomechanical training courses covering all subjects Developed Advanced Geomechanical training program for experienced staff in Shell Coaching of KPC staff on Geomechanics and containment issues on an internship at Shell in The Netherlands, Q4 2014 Lectured at the Utrecht University summer school (The Netherlands, 2020) on induced seismicity among renowned earthquake experts (Prof. Mark Zoback, Prof. Jean-Philippe Avouac, Prof. Jean-Pierre Ampuero and Prof. Torsten Dahm) (https://www.nwo.nl/onderzoeksprogrammas/deepnl/bijeenkomsten/6-10-juli-2020-deepnl-webinar-series-induced-seismicity) Lectured at the Danish Technical University summer school (Copenhagen, 2021) summer school on Carbon Capture and Storage (https://www.oilgas.dtu.dk/english/Events/DHRTC-Summer-School) Virtual Carbon Capture and Storage (CCS): Project Risks & How to Manage Them training course (October and November 2021) POST TRAINING COACHING SUPPORT (OPTIONAL) To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster. Request for further information post training support and fees applicable Accreditions And Affliations

Essential 5G training course description This course is designed to give delegates an explanation of the technologies and interworking requirements of the next generation of cellular communications. It is not a definitive set of descriptions but a possibility of the final deployment. we will investigate the 10 pillars for 5G which will include various Radio Access Technologies that are required to interwork smoothly. We will look at the 4G Pro features and other RATs. What will you learn List the ten pillars of 5G deployment. Describe the 5G Internet. Explain virtualization and RAT virtulization. Describe Software Defined Networks (SDN). Explain carrier aggregation. Describe the mobile cloud. Explain an overall picture of 5G architecture. Essential 5G training course details Who will benefit: Anyone looking for an understanding of the technologies and interworking requirements of the next generation of cellular communications. Prerequisites: None. Duration 3 days Essential 5G training course contents Drivers for 5G 5G Road Map, 10 Pillars of 5G, evolving RATs, oSON, MTCm, mm-wave, backhaul, EE, new spectrum, spectrum sharing, RAN virtualisation. 4G LTE Advanced MIMO technology in release 8, Downlink & uplink MIMO R8, MIMO technology in LTE advanced, Downlink 8-layer SU-MIMO, Downlink MU-MIMO, Uplink MU-MIMO, Uplink transmit diversity, Coordinated multi-point operation (CoMP), Independent eNB & remote base station configurations, Downlink CoMP. ICIC & eICIC ICIC, Homogeneous to heterogeneous network evolution, Introduction to eICIC, Macro-pico scenario, Macro-femto scenario, Time orthogonal frequencies. Almost Blank Subframe (ABS). Carrier aggregation Component carriers (CC), CC aggregation deployments, Intra-band contiguous solutions, Intra-band non-contiguous solutions, Inter-band non-contiguous solutions, CA bandwidth classes, Aggregated transmission bandwidth configurations (ATBC), Possible carrier aggregation configs. eIMTA TDD UL-DL reconfig. for traffic adaptation, Reconfig. mechanisms, Interference mitigation schemes, Dynamic & flexible resource allocation. 5G architectures 5G in Europe, horizon 2020 framework, 5G infrastructure PPP, METIS project, 5G in North America, academy research, company R&D, 5G specifications. The 5G internet High-level view of Cloud Services, The Internet of Things & context awareness, Network reconfiguration & virtualization support, server proliferation, how VMs fix underutilised server problem, enter the hypervisor, why are VM such a big deal? SDN, evolution of the data centre network, high availability, low latency, scalability, security, cost model explodes, service-oriented API. OpenFlow switches, OpenFlow controllers, how SDN works. The big picture, pulling it all together, why the network had to change, how SDN & NFV tie together. Evolutionary approach to the internet, architectures for distributed mobility management, MEDIEVAL & MEDIVO projects, a clean slate approach, mobility first architecture. VNet, INM, NetInf, ForMux, MEEM. Generic Path (GP) & anchorless mobility (AM), Quality of Service support, network resource provisioning, resourcing inside a network. IntServ, RSVP, DiffServ, CoS. Emerging approach for resource over- provisioning, example use case architecture for scalable resource control scenarios in the 5G internet. Integrating SDN/NFV for efficient resource over-reservation control, control information repository, service admission control policies, network resource provisioning, control enforcement functions, network configurations & operations. Small cells for 5G Average spectral efficiency evolution, WiFi & Femto cells, Capacity limits. Achievable gains with densifications, multi-antenna techniques, small cells. Mobile data demand, approach & methodology, subscriber density and traffic demand projections to 2020. Demand versus capacity, global mobile data traffic increase modelling, country level backhaul traffic projections, Small cell challenges, backhaul, spectrum, automation. Cooperation for next gen wireless networks Diversity & relaying strategies, cooperation & network coding, ARQ & MAC protocols, NCCARQ & PRCSMA packet exchange, Physical layer impact on MAC protocol analysis, NCCARQ overview, PHY layer impact, Case study on NCCARQ. Mobile clouds Mobile cloud, Mobile cloud enablers, mobile user domain, wireless technologies, WWAN WLAN and WPAN range, Bluetooth, IEEE.802.15.4 & software stacks, infrared, near field communications (NFC). Network coding, store & forward vs compute & forward, linear network coding, random linear coding. Security for 5G communications Potential 5G communication systems architectures, Security issues & challenges. Mobile malware attacks targeting the UE, 5G mobile botnets, access networks, attacks on 4G networks, C-RNTI & packet sequence number based UE location tracking, false buffer status reports attacks, message insertion attacks, HeNB attacks, physical attacks, credential attacks, configuration and protocol attacks, attacks on MON, user data & identity attacks, mobile operator's core network, DDoS attacks targeting MON, signalling amplification, HSS saturation, external IP networks.

Lean Six Sigma Black Belt Certification Program: In-House Training This course is specifically for people wanting to become Lean Six Sigma Black Belts, who are already Lean Six Sigma practitioners. If advanced statistical analysis is needed to identify root causes and optimal process improvements, (Lean) Six Sigma Green Belts typically ask Black Belts or Master Black Belts to conduct these analyses. This course will change that. Green Belts wanting to advance their statistical abilities will have a considerable amount of hands-on practice in techniques such as Statistical Process Control, MSA, Hypothesis Testing, Correlation and Regression, Design of Experiments, and many others. Participants will also work throughout the course on a real-world improvement project from their own business environment. This provides participants with hands-on learning and provides the organization with an immediate ROI once the project is completed. IIL instructors will provide free project coaching throughout the course. What you Will Learn At the end of this program, you will be able to: Use Minitab for advanced data analysis Develop appropriate sampling strategies Analyze differences between samples using Hypothesis Tests Apply Statistical Process Control to differentiate common cause and special cause variation Explain and apply various process capability metrics Conduct Measurement System Analysis and Gage R&R studies for both discrete and continuous data Conduct and analyze simple and multiple regression analysis Plan, execute, and analyze designed experiments Drive sustainable change efforts through leadership, change management, and stakeholder management Successfully incorporate advanced analysis techniques while moving projects through the DMAIC steps Explain the main concepts of Design for Six Sigma including QFD Introduction: DMAIC Review IIL Black Belt Certification Requirements Review Project Selection Review Define Review Measure Review Analyze Review Improve Review Control Introduction: Minitab Tool Introduction to Minitab Minitab basic statistics and graphs Special features Overview of Minitab menus Introduction: Sampling The Central Limit Theorem Confidence Interval of the mean Sample size for continuous data (mean) Confidence Interval for proportions Sample size for discrete data (proportions) Sampling strategies (review) Appendix: CI and sample size for confidence levels other than 95% Hypothesis Testing: Introduction Why use advanced stat tools? What are hypothesis tests? The seven steps of hypothesis tests P value errors and hypothesis tests Hypothesis Testing: Tests for Averages 1 factor ANOVA and ANOM Main Effect Plots, Interaction Plots, and Multi-Vari Charts 2 factor ANOVA and ANOM Hypothesis Testing: Tests for Standard Deviations Testing for equal variance Testing for normality Choosing the right hypothesis test Hypothesis Testing: Chi Square and Other Hypothesis Test Chi-square test for 1 factor ANOM test for 1 factor Chi-square test for 2 factors Exercise hypothesis tests - shipping Non-parametric tests Analysis: Advanced Control Charts Review of Common Cause and Special Cause Variation Review of the Individuals Control Charts How to calculate Control Limits Four additional tests for Special Causes Control Limits after Process Change Discrete Data Control Charts Control Charts for Discrete Proportion Data Control Charts for Discrete Count Data Control Charts for High Volume Processes with Continuous Data Analysis: Non-Normal Data Test for normal distribution Box-Cox Transformation Box-Cox Transformation for Individuals Control Charts Analysis: Time Series Analysis Introduction to Time Series Analysis Decomposition Smoothing: Moving Average Smoothing: EWMA Analysis: Process Capability Process capability Discrete Data: Defect metrics Discrete Data: Yield metrics Process Capability for Continuous Data: Sigma Value Short- and long-term capabilities Cp, Cpk, Pp, Ppk capability indices Analysis: Measurement System Analysis What is Measurement System Analysis? What defines a good measurement system? Gage R&R Studies Attribute / Discrete Gage R&R Continuous Gage R&R Regression Analysis: Simple Correlation Correlation Coefficient Simple linear regression Checking the fit of the Regression Model Leverage and influence analysis Correlation and regression pitfalls Regression Analysis: Multiple Regression Analysis Introduction to Multiple Regression Multicollinearity Multiple Regression vs. Simple Linear Regression Regression Analysis: Multiple Regression Analysis with Discrete Xs Introduction Creating indicator variables Method 1: Going straight to the intercepts Method 2: Testing for differences in intercepts Logistic Regression: Logistic Regression Introduction to Logistic Regression Logistic Regression - Adding a Discrete X Design of Experiments: Introduction Design of Experiment OFAT experimentation Full factorial design Fractional factorial design DOE road map, hints, and suggestions Design of Experiments: Full Factorial Designs Creating 2k Full Factorial designs in Minitab Randomization Replicates and repetitions Analysis of results: Factorial plots Analysis of results: Factorial design Analysis of results: Fits and Residuals Analysis of results: Response Optimizer Analysis of results: Review Design of Experiments: Pragmatic Approaches Designs with no replication Fractional factorial designs Screening Design of Experiment Case Study Repair Time Blocking Closing: Organizational Change Management Organizational change management Assuring project sponsorship Emphasizing shared need for change Mobilizing stakeholder commitment Closing: Project Management for Lean Six Sigma Introduction to project management Project management for Lean Six Sigma The project baseline plan Work Breakdown Structure (WBS) Resource planning Project budget Project risk Project schedule Project executing Project monitoring and controlling and Closing Closing: Design for Lean Six Sigma Introduction to Design for Lean Six Sigma (DMADV) Introduction to Quality Function Deployment (QFD) Summary and Next Steps IIL's Lean Six Sigma Black Belt Certification Program also prepares you to pass the IASSC Certified Black Belt Exam (optional)

Lean Six Sigma Black Belt Certification Program: Virtual In-House Training This course is specifically for people wanting to become Lean Six Sigma Black Belts, who are already Lean Six Sigma practitioners. If advanced statistical analysis is needed to identify root causes and optimal process improvements, (Lean) Six Sigma Green Belts typically ask Black Belts or Master Black Belts to conduct these analyses. This course will change that. Green Belts wanting to advance their statistical abilities will have a considerable amount of hands-on practice in techniques such as Statistical Process Control, MSA, Hypothesis Testing, Correlation and Regression, Design of Experiments, and many others. Participants will also work throughout the course on a real-world improvement project from their own business environment. This provides participants with hands-on learning and provides the organization with an immediate ROI once the project is completed. IIL instructors will provide free project coaching throughout the course. What you Will Learn At the end of this program, you will be able to: Use Minitab for advanced data analysis Develop appropriate sampling strategies Analyze differences between samples using Hypothesis Tests Apply Statistical Process Control to differentiate common cause and special cause variation Explain and apply various process capability metrics Conduct Measurement System Analysis and Gage R&R studies for both discrete and continuous data Conduct and analyze simple and multiple regression analysis Plan, execute, and analyze designed experiments Drive sustainable change efforts through leadership, change management, and stakeholder management Successfully incorporate advanced analysis techniques while moving projects through the DMAIC steps Explain the main concepts of Design for Six Sigma including QFD Introduction: DMAIC Review IIL Black Belt Certification Requirements Review Project Selection Review Define Review Measure Review Analyze Review Improve Review Control Introduction: Minitab Tool Introduction to Minitab Minitab basic statistics and graphs Special features Overview of Minitab menus Introduction: Sampling The Central Limit Theorem Confidence Interval of the mean Sample size for continuous data (mean) Confidence Interval for proportions Sample size for discrete data (proportions) Sampling strategies (review) Appendix: CI and sample size for confidence levels other than 95% Hypothesis Testing: Introduction Why use advanced stat tools? What are hypothesis tests? The seven steps of hypothesis tests P value errors and hypothesis tests Hypothesis Testing: Tests for Averages 1 factor ANOVA and ANOM Main Effect Plots, Interaction Plots, and Multi-Vari Charts 2 factor ANOVA and ANOM Hypothesis Testing: Tests for Standard Deviations Testing for equal variance Testing for normality Choosing the right hypothesis test Hypothesis Testing: Chi Square and Other Hypothesis Test Chi-square test for 1 factor ANOM test for 1 factor Chi-square test for 2 factors Exercise hypothesis tests - shipping Non-parametric tests Analysis: Advanced Control Charts Review of Common Cause and Special Cause Variation Review of the Individuals Control Charts How to calculate Control Limits Four additional tests for Special Causes Control Limits after Process Change Discrete Data Control Charts Control Charts for Discrete Proportion Data Control Charts for Discrete Count Data Control Charts for High Volume Processes with Continuous Data Analysis: Non-Normal Data Test for normal distribution Box-Cox Transformation Box-Cox Transformation for Individuals Control Charts Analysis: Time Series Analysis Introduction to Time Series Analysis Decomposition Smoothing: Moving Average Smoothing: EWMA Analysis: Process Capability Process capability Discrete Data: Defect metrics Discrete Data: Yield metrics Process Capability for Continuous Data: Sigma Value Short- and long-term capabilities Cp, Cpk, Pp, Ppk capability indices Analysis: Measurement System Analysis What is Measurement System Analysis? What defines a good measurement system? Gage R&R Studies Attribute / Discrete Gage R&R Continuous Gage R&R Regression Analysis: Simple Correlation Correlation Coefficient Simple linear regression Checking the fit of the Regression Model Leverage and influence analysis Correlation and regression pitfalls Regression Analysis: Multiple Regression Analysis Introduction to Multiple Regression Multicollinearity Multiple Regression vs. Simple Linear Regression Regression Analysis: Multiple Regression Analysis with Discrete Xs Introduction Creating indicator variables Method 1: Going straight to the intercepts Method 2: Testing for differences in intercepts Logistic Regression: Logistic Regression Introduction to Logistic Regression Logistic Regression - Adding a Discrete X Design of Experiments: Introduction Design of Experiment OFAT experimentation Full factorial design Fractional factorial design DOE road map, hints, and suggestions Design of Experiments: Full Factorial Designs Creating 2k Full Factorial designs in Minitab Randomization Replicates and repetitions Analysis of results: Factorial plots Analysis of results: Factorial design Analysis of results: Fits and Residuals Analysis of results: Response Optimizer Analysis of results: Review Design of Experiments: Pragmatic Approaches Designs with no replication Fractional factorial designs Screening Design of Experiment Case Study Repair Time Blocking Closing: Organizational Change Management Organizational change management Assuring project sponsorship Emphasizing shared need for change Mobilizing stakeholder commitment Closing: Project Management for Lean Six Sigma Introduction to project management Project management for Lean Six Sigma The project baseline plan Work Breakdown Structure (WBS) Resource planning Project budget Project risk Project schedule Project executing Project monitoring and controlling and Closing Closing: Design for Lean Six Sigma Introduction to Design for Lean Six Sigma (DMADV) Introduction to Quality Function Deployment (QFD) Summary and Next Steps IIL's Lean Six Sigma Black Belt Certification Program also prepares you to pass the IASSC Certified Black Belt Exam (optional)

Satellite communications training course description This course starts by recaping some of the essential satellite knowledge required and proceeds to explore the deeper aspects of satellite communications, including hardware, communications and error control coding. What will you learn Explain how satellite communications work. Explain how RF works Explain the architecture of satellite systems. Use spectrum analysers. Satellite communications training course details Who will benefit: Anyone working with satellite systems. Prerequisites: None. Duration 3 days Satellite communications training course contents Basic Principles of Satellite Communications GEO, MEO and LEO satellites. Launching and orbits. Frequency bands and polarisation. Satellite footprints. Multibeam coverage. Power spectra. Link budgets. Modulation and coding. Access technologies. Earth station components. Space segment components. Satellite system services. Satellite operators. Radio frequency propagation Electromagnetic waves principles and generation. Reception of the EM wave. Space wave, sky wave and surface wave theory. The isotropic radiator. Types of antennae and their basic properties. Polar diagrams. International frequency allocation. Spectrum management and utilisation. Radio wave propagation. Line of sight propagation. Propagation for satellite comms. Free space path loss. Path attenuation. Noise and Interference. Power and its measurement. Satellite antennae and other hardware Power flux density. Effective aperture. Horn antennae. Parabolic reflector. Offset feed. Cassegrain and Gregorian antennae. Antenna feed systems - Horn, TMC, OMJ and polarizer. Antenna steering and mount systems. Array antennae. LNA, LNB, LNC. Microwave tubes - TWT and Klystron. Polarizers. Earth and Space Segments and the link Earth station antennae. Transponders. Antennae sub systems. Power supplies. Link budgets. System noise. System losses. Interference. Satellite switching. Ground Communications Equipment Baseband signals. Analogue and Digital systems. Overview of modulation - AM, FM, PM. Digital Modulation. Frequency conversion -up and down conversion. Filters, mixers, local oscillators, IF amplifiers and group delay equalisers. Access methods - single and multiple access systems. Data networks. Television transmission - analogue and digital. Digital signal compression. MPEG processing. Satellite Navigation Longitude, latitude, altitude, GPS, How GPS works, timing, alternatives to GPS. Mobile satellite services Voice and Phones, BGAN, TV, GPS to program aerial, VSAT. Error Control Coding The need for coding. Linear block codes. Cyclic codes. Convolution codes. Interleaving and concatenated codes. Coding gain. Turbo codes. Test and measurement Theory and practice of Spectrum Analysers.

Duration 2 Days 12 CPD hours This course is intended for Anyone who works with IBM SPSS Statistics and wants to learn advanced statistical procedures to be able to better answer research questions. Overview Introduction to advanced statistical analysis Group variables: Factor Analysis and Principal Components Analysis Group similar cases: Cluster Analysis Predict categorical targets with Nearest Neighbor Analysis Predict categorical targets with Discriminant Analysis Predict categorical targets with Logistic Regression Predict categorical targets with Decision Trees Introduction to Survival Analysis Introduction to Generalized Linear Models Introduction to Linear Mixed Models This course provides an application-oriented introduction to advanced statistical methods available in IBM SPSS Statistics. Students will review a variety of advanced statistical techniques and discuss situations in which each technique would be used, the assumptions made by each method, how to set up the analysis, and how to interpret the results. This includes a broad range of techniques for predicting variables, as well as methods to cluster variables and cases. Introduction to advanced statistical analysis Taxonomy of models Overview of supervised models Overview of models to create natural groupings Group variables: Factor Analysis and Principal Components Analysis Factor Analysis basics Principal Components basics Assumptions of Factor Analysis Key issues in Factor Analysis Improve the interpretability Use Factor and component scores Group similar cases: Cluster Analysis Cluster Analysis basics Key issues in Cluster Analysis K-Means Cluster Analysis Assumptions of K-Means Cluster Analysis TwoStep Cluster Analysis Assumptions of TwoStep Cluster Analysis Predict categorical targets with Nearest Neighbor Analysis Nearest Neighbor Analysis basics Key issues in Nearest Neighbor Analysis Assess model fit Predict categorical targets with Discriminant Analysis Discriminant Analysis basics The Discriminant Analysis model Core concepts of Discriminant Analysis Classification of cases Assumptions of Discriminant Analysis Validate the solution Predict categorical targets with Logistic Regression Binary Logistic Regression basics The Binary Logistic Regression model Multinomial Logistic Regression basics Assumptions of Logistic Regression procedures Testing hypotheses Predict categorical targets with Decision Trees Decision Trees basics Validate the solution Explore CHAID Explore CRT Comparing Decision Trees methods Introduction to Survival Analysis Survival Analysis basics Kaplan-Meier Analysis Assumptions of Kaplan-Meier Analysis Cox Regression Assumptions of Cox Regression Introduction to Generalized Linear Models Generalized Linear Models basics Available distributions Available link functions Introduction to Linear Mixed Models Linear Mixed Models basics Hierachical Linear Models Modeling strategy Assumptions of Linear Mixed Models Additional course details: Nexus Humans 0G09A IBM Advanced Statistical Analysis Using IBM SPSS Statistics (v25) 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 0G09A IBM Advanced Statistical Analysis Using IBM SPSS Statistics (v25) 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 The skills covered in this course converge on four areas-software development, IT operations, applied math and statistics, and business analysis. Target students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems. So, the target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decisionmaking products that bring value to the business. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification Overview In this course, you will develop AI solutions for business problems. You will: Solve a given business problem using AI and ML. Prepare data for use in machine learning. Train, evaluate, and tune a machine learning model. Build linear regression models. Build forecasting models. Build classification models using logistic regression and k -nearest neighbor. Build clustering models. Build classification and regression models using decision trees and random forests. Build classification and regression models using support-vector machines (SVMs). Build artificial neural networks for deep learning. Put machine learning models into operation using automated processes. Maintain machine learning pipelines and models while they are in production Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions. Solving Business Problems Using AI and ML Topic A: Identify AI and ML Solutions for Business Problems Topic B: Formulate a Machine Learning Problem Topic C: Select Approaches to Machine Learning Preparing Data Topic A: Collect Data Topic B: Transform Data Topic C: Engineer Features Topic D: Work with Unstructured Data Training, Evaluating, and Tuning a Machine Learning Model Topic A: Train a Machine Learning Model Topic B: Evaluate and Tune a Machine Learning Model Building Linear Regression Models Topic A: Build Regression Models Using Linear Algebra Topic B: Build Regularized Linear Regression Models Topic C: Build Iterative Linear Regression Models Building Forecasting Models Topic A: Build Univariate Time Series Models Topic B: Build Multivariate Time Series Models Building Classification Models Using Logistic Regression and k-Nearest Neighbor Topic A: Train Binary Classification Models Using Logistic Regression Topic B: Train Binary Classification Models Using k-Nearest Neighbor Topic C: Train Multi-Class Classification Models Topic D: Evaluate Classification Models Topic E: Tune Classification Models Building Clustering Models Topic A: Build k-Means Clustering Models Topic B: Build Hierarchical Clustering Models Building Decision Trees and Random Forests Topic A: Build Decision Tree Models Topic B: Build Random Forest Models Building Support-Vector Machines Topic A: Build SVM Models for Classification Topic B: Build SVM Models for Regression Building Artificial Neural Networks Topic A: Build Multi-Layer Perceptrons (MLP) Topic B: Build Convolutional Neural Networks (CNN) Topic C: Build Recurrent Neural Networks (RNN) Operationalizing Machine Learning Models Topic A: Deploy Machine Learning Models Topic B: Automate the Machine Learning Process with MLOps Topic C: Integrate Models into Machine Learning Systems Maintaining Machine Learning Operations Topic A: Secure Machine Learning Pipelines Topic B: Maintain Models in Production

Duration 1 Days 6 CPD hours This course is intended for This course is intended for individuals who want to become more rational and disciplined thinkers. Overview Upon successful completion of this course, students will boost their performance with an increase in their decision-making ability. In this course, students will learn develop their ability to analyze and evaluate information. 1 - GETTING STARTED Icebreaker Housekeeping Items The Parking Lot Workshop Objectives 2 - COMPONENTS OF CRITICAL THINKING Applying Reason Open Mindedness Analysis Logic Case Study 3 - NON-LINEAR THINKING Step Out of Your Comfort Zone Don't Jump to Conclusions Expect and Initiate Change Being Ready to Adapt Case Study 4 - LOGICAL THINKING Ask the Right Questions Organize the Data Evaluate the Information Draw Conclusions Case Study 5 - CRITICAL THINKERS (I) Active Listening Be Curious Be Disciplined Be Humble Case Study 6 - CRITICAL THINKERS (II) Seeing the Big Picture Objectivity Using Your Emotions Being Self-Aware Case Study 7 - EVALUATE INFORMATION Making Assumptions Watch out for Bias Ask Clarifying Questions SWOT Analysis Case Study 8 - BENEFITS OF CRITICAL THINKING Being More Persuasive Better Communication Better Problem Solving Increased Emotional Intelligence Case Study 9 - CHANGING YOUR PERSPECTIVE Limitations of Your Point of View Considering Others Viewpoint Influences on Bias When New Information Arrives Case Study 10 - PROBLEM SOLVING Identify Inconsistencies Trust Your Instincts Asking Why? Evaluate the Solution(s) Case Study 11 - PUTTING IT ALL TOGETHER Retaining Your New Skills Reflect and Learn From Mistakes Always Ask Questions Practicing Critical Thinking Case Study 12 - WRAPPING UP Words from the Wise Review of Parking Lot Lessons Learned Completion of Action Plans and Evaluations