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IASSC lean six sigma yellow belt course. Online, 24/7 access to content and exam. Fee includes learning content, tutor support, and official IASSC exam.

Time Management and Productivity - Organizing and Prioritizing to Get Things Done!: On-Demand Effective time management reduces stress and helps you better adhere to commitments. This course is designed to help you identify personal and organizational priorities and gain better control of where you focus your attention. You will explore the importance of planning and organizing, and you will practice sorting through and setting priorities. You will also learn how to set better time boundaries and manage the demands of others. Working with a time management framework, you will use a set of practical techniques to organize and manage your work to better deliver on your commitments. Techniques for overcoming procrastination will be addressed, as well as strategies for dealing with information overload. What You Will Learn You'll learn how to: Plan and prioritize each day's activities in a more efficient, productive manner Establish strategies to execute priorities and overcome procrastination Understand how to make trade-offs when faced with fire drills How to set and communicate boundary conditions Getting Started Introductions Course orientation Participants expectations Foundation Concepts Exercise: A Day in Your Life Resources to implement change MindsetToolsetSkillset What is your time really worth? The dynamics of procrastination The myth of multi-tasking Brain Rules - how to optimize your efficiency Organization and Prioritization Time management best practices Goal setting Exercise: Identifying Your Priorities The importance of organization Time management framework Prioritizing time Time Management Techniques Tips for managing time Nine ways to overcome procrastination The STING technique Managing your time Handling unplanned urgenciesDealing with information overload Delegation and managing others time Creating your personal action plan Summary What did we learn, and how can we implement this in our work environment?

Project Leadership Skills - Français: On-Demand To be effective within an organization, project managers must have a wide variety of skills and abilities.These include: creating and executing a vision;motivate others;influence without authority;networking;communicate up, down and laterally;the negociation;stakeholder management;and manage conflicts. This workshop aims to develop the soft skills essential to leading a team and creating lasting business change.Participants will gain insight into social science and brain science to motivate and empower others.They will learn and experiment with various influencing strategies and tactics. Participants will also discover their personal communication preferences, strengths and blind spots and learn how to best communicate with others they find 'difficult'.They will learn better how to manage the human side of change and learn strategies for managing each step.In the process.Practical negotiation and conflict management activities reinforce theoretical learning, rooting it in real life and making it actionable. What You Will Learn At the end of this program, you will be able to: Explain the importance of a vision in driving motivation and engagement Apply a scientific approach to better motivate those around you Strategically maximize your personal and positional power for better project results Determine influence and network development strategies necessary for personal development Know how to respond to communication challenges related to different personality styles Make the link between the expectations of stakeholders and the success criteria of a project Assess key stakeholders across different dimensions of complexity Apply the four rules of principled negotiation to real-life conflict situations Recognize key aspects of a physiological response to conflict Make the right choice of tools and techniques to "demine" an emotional situation Maximize different strategies and tactics to manage ambiguity at work Manage vision and purpose / Motivate others Communication and alignment with the vision Link the present to the future The Importance of Purpose The art and science of motivation Network development and influence Positive policy and project success Types of power in an organization power and influence Network Development Best Practices Communication The medium and the message Personality and communication styles Communication challenges Stakeholder management / Negotiation Identification of stakeholders Stakeholder analysis The basics of negotiation reasoned negotiation Manage the conflict Conflict dynamics The Anatomy of Conflict Conflict management approaches and tools Dealing with Ambiguity Summary and Next Steps Review of key concepts Create your personal action plan

Description: A SCRUM master is a facilitator for the agile development team. A SCRUM master servers the Product owner in several ways such as - By finding methods for efficient Product Backlog management By facilitating the Scrum Team to understand the need for clear and concise Product Backlog items. By Understanding and analyzing product planning in an empirical environment. By educating the Product Owner to arrange the Product Backlog to maximise value. By Understanding and practicing agility. By Facilitating Scrum events The Scrum Product Owner Video Training Course provides all the necessary information related to SCRUM Product owner with a special focus on the topics like roles and responsibilities, planning, managing quality, change, and risk. Throughout the course, you will learn the sprint planning meeting, the ways of grooming the prioritized product backlog, the sprint review meeting, and shipping deliverables. In short, by the end of the Scrum Product Owner Video Training Course, you will learn how to use Scrum to optimise value, productivity, and goals. Assessment & Certification: To achieve a formal qualification, you are required to book an official exam separately with the relevant awarding bodies. However, you will be able to order a course completion CPD Certificate by paying an additional fee. Hardcopy Certificate by post - £19 Soft copy PDF via email - £10 Requirements Our Scrum Product Owner Video Training Course is fully compatible with any kind of device. Whether you are using Windows computer, Mac, smartphones or tablets, you will get the same experience while learning. Besides that, you will be able to access the course with any kind of internet connection from anywhere at any time without any kind of limitation. Career Path After completing this course you will be able to build up accurate knowledge and skills with proper confidence to enrich yourself and brighten up your career in the relevant job market. Introduction Introduction FREE 00:11:00 Introduction to Scrum Introduction to Scrum 00:01:00 Scrum Overview 00:21:00 Scrum Concepts & Principles 00:15:00 Scrum Phases & Processes 00:05:00 Introduction to Scrum Summary 00:02:00 Roles and Responsibilities Roles and Responsibilities 00:01:00 The Product Owner 00:06:00 The Scrum Master 00:04:00 The Scrum Team 00:09:00 Roles & Responsibility Summary 00:02:00 Manage Quality, Change, and Risk Planning 00:02:00 The Project Vision 00:11:00 User Group Meetings 00:11:00 Estimates 00:08:00 Prioritized Product Backlog 00:11:00 Release Plan 00:12:00 Planning Summary 00:02:00 Planning Manage Quality, Change, and Risk 00:01:00 Quality 00:07:00 Change 00:07:00 Risk 00:13:00 Manage Quality, Change & Risk Summary 00:02:00 Sprints Sprints 00:01:00 Sprint Planning Meeting 00:21:00 During the Sprint 00:07:00 Groom the Prioritized Product Backlog 00:04:00 Sprint Review Meeting 00:04:00 Ship Deliverables 00:02:00 Sprints 00:03:00 Mock Exam Mock Exam- Scrum Product Owner Video Training Course 00:20:00 Final Exam Final Exam- Scrum Product Owner Video Training Course 00:20:00 Order Your Certificate and Transcript Order Your Certificates and Transcripts 00:00:00

Think of a presentation organized as parts of a body: head and eyes, body, legs, and feet. We will guide you to select and outline supporting materials for each main point. Discover how to prepare your introduction and summary to deliver your main points. Your opener and close are the most impactful parts of your presentation. Learning Objectives Explain how to create a presentation using four parts of the "presentation body", Prepare effective visuals, transitions, introductions, and summaries, Write compelling openers, Recommend a closing call to action Target Audience Managers, Team Leaders, Young Professionals, Sales Professionals, Customer Service Teams

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The 'Complete Python Machine Learning & Data Science Fundamentals' course covers the foundational concepts of machine learning, data science, and Python programming. It includes hands-on exercises, data visualization, algorithm evaluation techniques, feature selection, and performance improvement using ensembles and parameter tuning. Learning Outcomes: Understand the fundamental concepts and types of machine learning, data science, and Python programming. Learn to prepare the system and environment for data analysis and machine learning tasks. Master the basics of Python, NumPy, Matplotlib, and Pandas for data manipulation and visualization. Gain insights into dataset summary statistics, data visualization techniques, and data preprocessing. Explore feature selection methods and evaluation metrics for classification and regression algorithms. Compare and select the best machine learning model using pipelines and ensembles. Learn to export, save, load machine learning models, and finalize the chosen models for real-time predictions. Why buy this Complete Python Machine Learning & Data Science Fundamentals? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Complete Python Machine Learning & Data Science Fundamentals there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This Complete Python Machine Learning & Data Science Fundamentals course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill. Prerequisites This Complete Python Machine Learning & Data Science Fundamentals does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Complete Python Machine Learning & Data Science Fundamentals was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Complete Python Machine Learning & Data Science Fundamentals is a great way for you to gain multiple skills from the comfort of your home. 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

Earned Value Management: On-Demand: On-Demand Earned Value Management (EVM) incorporates a set of proven practices appropriate for project or program management methodologies. These include integration of program scope, schedule, and cost objectives, establishment of a baseline plan for accomplishment of program objectives and use of earned value techniques for performance measurement during the execution of a program. Earned Value Management (EVM) incorporates a set of proven practices appropriate for project or program management methodologies. These include integration of program scope, schedule, and cost objectives, establishment of a baseline plan for accomplishment of program objectives and use of earned value techniques for performance measurement during the execution of a program. EVM provides a solid platform for risk identification, corrective actions, and management re-planning as may be required over the life of a project or program. The course emphasis is on the latest EVM principles and concepts in accordance with changes and guidelines for Earned Value Management in The Guide to the Project Management Body of Knowledge (PMBOK® Guide) and The Practice Standard for Earned Value Management published by the Project Management Institute. What you Will Learn You'll learn how to: Develop a project baseline, using an effective WBS Record actual project performance Calculate EVM measures Evaluate project performance based on EVM measures Respond to project variances Integrate EVM and risk management Determine how EVM will add value to your organization Develop an EVM implementation plan for your organization Getting Started Introductions Course structure Course goals and objectives Expectations Foundation Concepts Introduction to Earned Value Management (EVM) Benefits of EVM EVM Process Overview Applications of EVM Creating a Work Breakdown Structure Reviewing WBS concepts Reviewing WBS development process (decomposition) Using a WBS to support EVM Building a Project Baseline Defining a project baseline Developing a project baseline Using a project baseline Recording Actuals Recording actuals overview Collecting data for actual project performance Determining earned value - various methods EVM Performance Measures Using current status measures Using forecasting measures Analyzing EVM measures EVM and Risk Management Integrating EVM and Risk Management Using EVM measures in the risk register Exploring how EVM can facilitate reserves management Drawing down contingency reserves Responding to Variances Introduction to variances Process for responding to variances Response options Reporting Project Performance EVM reporting overview Meeting EVM reporting needs Addressing EVM reporting challenges Implementing an EVMS Defining EVMS requirements EVM for Agile projects Tailoring the EVMS Summary and Next Steps Review of content Review of objectives / expectations Personal action plan

IASSC lean six sigma yellow belt course. Online, 24/7 access to content and exam. Fee includes learning content, tutor support, and official IASSC exam.

Managing Agile and Waterfall Projects: On-Demand The concept of Agile project management refers to an iterative, incremental approach to managing the design and development of a product, service or result. The goal of this approach is to use it to manage the project in a way that is very flexible and interactive with the customer and/or end user, resulting in added value to the business. The concept of Waterfall project management refers to the use of a number of tools and techniques. A Guide to the Project Management Body of Knowledge (PMBOK® Guide) details 5 process groups: Initiating, Planning, Executing, Monitoring and Controlling, and Closing. As per the PMBOK® Guide - Sixth Edition, there are 49 processes entailed in these 5 process groups, which are focused on enabling project teams to deliver products to a pre-determined and agreed requirements/ specification. The selection of the most appropriate project management approach has, historically, been a choice of 'either/or' when it comes to these two approaches. It is now becoming clear that for many projects, the selection of a single project management approach does not satisfy the efficiency of the project team nor does it optimize the return on investment for the sponsoring organization. A different strategy is needed. "Managing Agile and Waterfall Projects" presents an approach to project management which capitalizes on the most appropriate elements of each approach, tailored to the specific project being undertaken. In this course, the Waterfall approach will be based on the PMBOK® Guide (predictive life-cycle). Each approach will be presented to highlight its particular strategy and strengths. The course will also propose project scenarios that require the project team to use a hybrid method which brings together aspects of both approaches. What You Will Learn You'll learn how to: Identify the strengths that the Waterfall approach brings to project work Identify the strengths that the Agile approach brings to project work Exploit the strengths of each method by combining their practices and protocols to maximize the potential for return on investment The Waterfall Approach to Competing Demands Optimization Foundation Concepts Getting Started The Agile Approach to Competing Demands Optimization Key Facets between Waterfall and Agile Examples for Implementing a Hybrid Approach The Challenges for the 'Combination' Project Management Team Practicing the Hybrid Approach Summary and Next Steps