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Duration 5 Days 30 CPD hours This course is intended for The primary audience for this course are Application Consultants, Business Process Architects, and Business Process Owners/Team Leads/Power Users. Overview This course will prepare you to:Describe tasks performed by a typical SAP Access Control userDiscuss Harmonization topics as they relate to SAP Access ControlExplain how SAP GRC helps you to address business challengesIdentify authorization risks in typical business processesDescribe the Segregation of Duties Risk Management ProcessDescribe and configure functionality and features for SAP Access Control 10.1Use the SAP Access Control 10.1 application to analyze and manage risk, design and manage roles, and provision and manage usersDescribe the SAP Access Control 10.1 architecture and landscape, SAP Access Control Repository, and Object Level SecurityDescribe the Periodic Access Review processPlan for and manage emergency accessDiscuss the reporting frameworkConfigure workflows, including multi-stage multi-path (MSMP) workflows and BRF+Describe how the different applications of the SAP GRC Solution integrate with each otherDiscuss key steps in the SAP Access Control implementation process In this course, students gain hands-on configuration and implementation experience of SAP Access Control 10.1, as well as important concepts they will need to know in order to prepare for implementation and ongoing access risk prevention. Introduction to SAP Access Control Discussing Business Challenges and Solutions Using SAP Access Control Architecture, Security, and Authorizations Describing the System Architecture Describing Security and Authorizations Shared Configuration Settings Configuring Shared GRC Settings Configuring Shared SAP Access Control Settings Authorization Risks and the Segregation of Duties (SoD) Management Process Identifying Authorization Risks Managing Risk by Segregating Duties SAP Access Control Repository Synchronizing Objects into the Repository Scheduling and Viewing Background Jobs Risk Analysis Maintaining Shared Master Data Configuring and Maintaining the Rule Set Configuring and Using Audit Trail Tracking Using the Risk Analysis Framework Remediating Risks Mitigating Risks Mitigating Multiple Risks at One Time Business Rule Framework Creating Rules in the Business Rule Framework (BRF) Defining Business Rules Multi-Stage, Multi-Path (MSMP) Workflow Describing Multi-Stage, Multi-Path (MSMP) Workflow Maintaining MSMP Workflow Building MSMP Workflow User Provisioning Configuring User Provisioning Settings Configuring Access Request Forms Requesting Access Preparing Roles and Owner Data for MSMP Workflow Creating Simplified Access Requests Reviewing Search Request Results SAP Fiori User Experience (UX) for GRC Describing SAP Fiori UX Role Design and Management Configuring Role Management Configuring Role Methodology Configuring Role Search Attributes Planning for Technical Role Definition Planning for Business Role Definition Consolidating Roles Through Role Mining Performing Role Mass Maintenance Operations Emergency Access Management Describing Emergency Access Management Planning for Emergency Access Monitoring Emergency Access Periodic Access Review Process Planning Periodic Review Monitoring Periodic Review Reports and Custom Fields Use the Reporting Framework SAP Access Control Implementation Using the SAP Access Control Implementation Process Designing the SAP Access Control Solution Planning Upgrade and Migration Configuring SAP Access Control Implementing the SAP Access Control solution Optimizing the SAP Access Control Suite Additional course details: Nexus Humans GRC300 SAP Access Control Implementation and Configuration 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 GRC300 SAP Access Control Implementation and Configuration 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.
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Course Curriculum Course Overview & Table of Contents Course Overview & Table of Contents 00:09:00 Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types 00:05:00 Introduction to Machine Learning - Part 2 - Classifications and Applications Introduction to Machine Learning - Part 2 - Classifications and Applications 00:06:00 System and Environment preparation - Part 1 System and Environment preparation - Part 1 00:08:00 System and Environment preparation - Part 2 System and Environment preparation - Part 2 00:06:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 1 00:10:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 2 00:09:00 Learn Basics of python - Functions Learn Basics of python - Functions 00:04:00 Learn Basics of python - Data Structures Learn Basics of python - Data Structures 00:12:00 Learn Basics of NumPy - NumPy Array Learn Basics of NumPy - NumPy Array 00:06:00 Learn Basics of NumPy - NumPy Data Learn Basics of NumPy - NumPy Data 00:08:00 Learn Basics of NumPy - NumPy Arithmetic Learn Basics of NumPy - NumPy Arithmetic 00:04:00 Learn Basics of Matplotlib Learn Basics of Matplotlib 00:07:00 Learn Basics of Pandas - Part 1 Learn Basics of Pandas - Part 1 00:06:00 Learn Basics of Pandas - Part 2 Learn Basics of Pandas - Part 2 00:07:00 Understanding the CSV data file Understanding the CSV data file 00:09:00 Load and Read CSV data file using Python Standard Library Understanding the CSV data file 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using Pandas Load and Read CSV data file using Pandas 00:05:00 Dataset Summary - Peek, Dimensions and Data Types Dataset Summary - Peek, Dimensions and Data Types 00:09:00 Dataset Summary - Class Distribution and Data Summary Dataset Summary - Class Distribution and Data Summary 00:09:00 Dataset Summary - Explaining Correlation Dataset Summary - Explaining Correlation 00:11:00 Dataset Summary - Explaining Skewness - Gaussian and Normal Curve Dataset Summary - Explaining Skewness - Gaussian and Normal Curve 00:07:00 Dataset Visualization - Using Histograms Dataset Visualization - Using Histograms 00:07:00 Dataset Visualization - Using Density Plots Dataset Visualization - Using Density Plots 00:06:00 Dataset Visualization - Box and Whisker Plots Dataset Visualization - Box and Whisker Plots 00:05:00 Multivariate Dataset Visualization - Correlation Plots Multivariate Dataset Visualization - Correlation Plots 00:08:00 Multivariate Dataset Visualization - Scatter Plots Multivariate Dataset Visualization - Scatter Plots 00:05:00 Data Preparation (Pre-Processing) - Introduction Data Preparation (Pre-Processing) - Introduction 00:09:00 Data Preparation - Re-scaling Data - Part 1 Data Preparation - Re-scaling Data - Part 1 00:09:00 Data Preparation - Re-scaling Data - Part 2 Data Preparation - Re-scaling Data - Part 2 00:09:00 Data Preparation - Standardizing Data - Part 1 Data Preparation - Standardizing Data - Part 1 00:07:00 Data Preparation - Standardizing Data - Part 2 Data Preparation - Standardizing Data - Part 2 00:04:00 Data Preparation - Normalizing Data Data Preparation - Normalizing Data 00:08:00 Data Preparation - Binarizing Data Data Preparation - Binarizing Data 00:06:00 Feature Selection - Introduction Feature Selection - Introduction 00:07:00 Feature Selection - Uni-variate Part 1 - Chi-Squared Test Feature Selection - Uni-variate Part 1 - Chi-Squared Test 00:09:00 Feature Selection - Uni-variate Part 2 - Chi-Squared Test Feature Selection - Uni-variate Part 2 - Chi-Squared Test 00:10:00 Feature Selection - Recursive Feature Elimination Feature Selection - Recursive Feature Elimination 00:11:00 Feature Selection - Principal Component Analysis (PCA) Feature Selection - Principal Component Analysis (PCA) 00:09:00 Feature Selection - Feature Importance Feature Selection - Feature Importance 00:07:00 Refresher Session - The Mechanism of Re-sampling, Training and Testing Refresher Session - The Mechanism of Re-sampling, Training and Testing 00:12:00 Algorithm Evaluation Techniques - Introduction Algorithm Evaluation Techniques - Introduction 00:07:00 Algorithm Evaluation Techniques - Train and Test Set Algorithm Evaluation Techniques - Train and Test Set 00:11:00 Algorithm Evaluation Techniques - K-Fold Cross Validation Algorithm Evaluation Techniques - K-Fold Cross Validation 00:09:00 Algorithm Evaluation Techniques - Leave One Out Cross Validation Algorithm Evaluation Techniques - Leave One Out Cross Validation 00:05:00 Algorithm Evaluation Techniques - Repeated Random Test-Train Splits Algorithm Evaluation Techniques - Repeated Random Test-Train Splits 00:07:00 Algorithm Evaluation Metrics - Introduction Algorithm Evaluation Metrics - Introduction 00:09:00 Algorithm Evaluation Metrics - Classification Accuracy Algorithm Evaluation Metrics - Classification Accuracy 00:08:00 Algorithm Evaluation Metrics - Log Loss Algorithm Evaluation Metrics - Log Loss 00:03:00 Algorithm Evaluation Metrics - Area Under ROC Curve Algorithm Evaluation Metrics - Area Under ROC Curve 00:06:00 Algorithm Evaluation Metrics - Confusion Matrix Algorithm Evaluation Metrics - Confusion Matrix 00:10:00 Algorithm Evaluation Metrics - Classification Report Algorithm Evaluation Metrics - Classification Report 00:04:00 Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction 00:06:00 Algorithm Evaluation Metrics - Mean Absolute Error Algorithm Evaluation Metrics - Mean Absolute Error 00:07:00 Algorithm Evaluation Metrics - Mean Square Error Algorithm Evaluation Metrics - Mean Square Error 00:03:00 Algorithm Evaluation Metrics - R Squared Algorithm Evaluation Metrics - R Squared 00:04:00 Classification Algorithm Spot Check - Logistic Regression Classification Algorithm Spot Check - Logistic Regression 00:12:00 Classification Algorithm Spot Check - Linear Discriminant Analysis Classification Algorithm Spot Check - Linear Discriminant Analysis 00:04:00 Classification Algorithm Spot Check - K-Nearest Neighbors Classification Algorithm Spot Check - K-Nearest Neighbors 00:05:00 Classification Algorithm Spot Check - Naive Bayes Classification Algorithm Spot Check - Naive Bayes 00:04:00 Classification Algorithm Spot Check - CART Classification Algorithm Spot Check - CART 00:04:00 Classification Algorithm Spot Check - Support Vector Machines Classification Algorithm Spot Check - Support Vector Machines 00:05:00 Regression Algorithm Spot Check - Linear Regression Regression Algorithm Spot Check - Linear Regression 00:08:00 Regression Algorithm Spot Check - Ridge Regression Regression Algorithm Spot Check - Ridge Regression 00:03:00 Regression Algorithm Spot Check - Lasso Linear Regression Regression Algorithm Spot Check - Lasso Linear Regression 00:03:00 Regression Algorithm Spot Check - Elastic Net Regression Regression Algorithm Spot Check - Elastic Net Regression 00:02:00 Regression Algorithm Spot Check - K-Nearest Neighbors Regression Algorithm Spot Check - K-Nearest Neighbors 00:06:00 Regression Algorithm Spot Check - CART Regression Algorithm Spot Check - CART 00:04:00 Regression Algorithm Spot Check - Support Vector Machines (SVM) Regression Algorithm Spot Check - Support Vector Machines (SVM) 00:04:00 Compare Algorithms - Part 1 : Choosing the best Machine Learning Model Compare Algorithms - Part 1 : Choosing the best Machine Learning Model 00:09:00 Compare Algorithms - Part 2 : Choosing the best Machine Learning Model Compare Algorithms - Part 2 : Choosing the best Machine Learning Model 00:05:00 Pipelines : Data Preparation and Data Modelling Pipelines : Data Preparation and Data Modelling 00:11:00 Pipelines : Feature Selection and Data Modelling Pipelines : Feature Selection and Data Modelling 00:10:00 Performance Improvement: Ensembles - Voting Performance Improvement: Ensembles - Voting 00:07:00 Performance Improvement: Ensembles - Bagging Performance Improvement: Ensembles - Bagging 00:08:00 Performance Improvement: Ensembles - Boosting Performance Improvement: Ensembles - Boosting 00:05:00 Performance Improvement: Parameter Tuning using Grid Search Performance Improvement: Parameter Tuning using Grid Search 00:08:00 Performance Improvement: Parameter Tuning using Random Search Performance Improvement: Parameter Tuning using Random Search 00:06:00 Export, Save and Load Machine Learning Models : Pickle Export, Save and Load Machine Learning Models : Pickle 00:10:00 Export, Save and Load Machine Learning Models : Joblib Export, Save and Load Machine Learning Models : Joblib 00:06:00 Finalizing a Model - Introduction and Steps Finalizing a Model - Introduction and Steps 00:07:00 Finalizing a Classification Model - The Pima Indian Diabetes Dataset Finalizing a Classification Model - The Pima Indian Diabetes Dataset 00:07:00 Quick Session: Imbalanced Data Set - Issue Overview and Steps Quick Session: Imbalanced Data Set - Issue Overview and Steps 00:09:00 Iris Dataset : Finalizing Multi-Class Dataset Iris Dataset : Finalizing Multi-Class Dataset 00:09:00 Finalizing a Regression Model - The Boston Housing Price Dataset Finalizing a Regression Model - The Boston Housing Price Dataset 00:08:00 Real-time Predictions: Using the Pima Indian Diabetes Classification Model Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:07:00 Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:00 Real-time Predictions: Using the Boston Housing Regression Model Real-time Predictions: Using the Boston Housing Regression Model 00:08:00 Resources Resources - Python Machine Learning & Data Science Fundamentals 00:00:00
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Course Curriculum of Midwifery: Midwifery course Module 01: Introduction to Midwifery Training Module 02: Preconception care Module 03: Sexual health Module 04: Antenatal care Module 05: Health advice in pregnancy Module 06: Minor disorders of pregnancy Module 07: Helping women cope with Pregnancy: complementary therapies Module 08: The Need for social support Module 09: Recognizing and managing pregnancy complications Module 10: Medical Conditions during Pregnancy Module 11: Normal labour: first stage Module 12: Pain relief: Non-pharmacological Module 13: Pain relief: pharmacological Module 14: Normal labour: second stage Module 15: Normal labour: third stage Module 16: Immediate care of the Newborn Module 17: Management of malpositions and malpresentation Module 18: High-risk labour Module 19: Emergencies during pregnancy, labour, and postnatally Maternal Module 20: Midwifery Postnatal care Module 21: Disorders of the postnatal period Module 22: Contraception Midwifery Module 23: Immediate care of the Newborn Module 24: Breastfeeding Module 25: Artificial feeding Assessment Method of Midwifery: Midwifery course After completing Midwifery and Maternity Care with Nursing: Midwifery course, you will get quizzes to assess your learning. You will do the later modules upon getting 60% marks on the quiz test. Certification of Midwifery: Midwifery course After completing the Midwifery and Maternity Care with Nursing: Midwifery course, you can instantly download your certificate for FREE. Who is this course for? Midwifery and Maternity Care with Nursing: Midwifery course Online This Midwifery and Maternity Care with Nursing: Midwifery course is designed for individuals who are passionate about providing compassionate care and support to expectant mothers and their babies. It is ideal for: Aspiring Midwives Healthcare Professionals Students Professionals Seeking a Career Change Individuals with Personal Interest Our Midwifery: Midwifery course is open to individuals who are committed to providing compassionate care, empowering birth experiences, and making a positive impact on the lives of expectant mothers and their babies. Requirements Midwifery and Maternity Care with Nursing: Midwifery course Online To enrol in this Midwifery and Maternity Care with Nursing: Midwifery course, students must fulfil the following requirements: Good Command over English language is mandatory to enrol in our Midwifery: Midwifery course. Be energetic and self-motivated to complete our Midwifery: Midwifery course. Basic computer Skill is required to complete our Midwifery: Midwifery course. If you want to enrol in our Midwifery: Midwifery course, you must be at least 15 years old. Career path Midwifery and Maternity Care with Nursing: Midwifery course Online The Midwifery and Maternity Care with Nursing: Midwifery course opens up a range of exciting opportunities for personal and professional growth. Here are some potential career paths you can pursue: Nurse Midwife Direct-Entry Midwife Midwifery Educator International Midwifery Midwifery Contracts Home Birthing