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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

⏰️ Available from 18th September at 7pm Price: £25 (non-refundable) Watch our pre-recorded webinar of ST4 and ST1 Applications and Interviews! This a recording from last year's highly successful webinar. 📝 Sign up to listen to a breakdown of the scoring matrix, how best to boost your portfolio to get a national training number and all the interview tips we can give you! Hear from National trainees who have been through the process, their advice, hints and tips. 🚨 Sign up and get access to the Live Q&A where we will talk about any updates and changes to the 2025 scoring matrix (on release) - delivered by current and recently appointed ST4 and ST1 trainees. This session will give you the best opportunity to discuss in real time any changes that come out! Please ensure you check all mail folders, including spam/junk folders, and add 'notifications@system.cademy.co.uk' Cademy to the list of 'safe senders', to ensure you receive all future emails. 

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Experienced tutors and mentors will be there for you whenever you need them, and solve all your queries through email and chat boxes. Benefits you'll get choosing Apex Learning for this Course: One payment, but lifetime access to 11 CPD courses Certificates, student ID for the title course included in a one-time fee Full tutor support available from Monday to Friday Free up your time - don't waste time and money travelling for classes Accessible, informative modules taught by expert instructors Learn at your ease - anytime, from anywhere Study the course from your computer, tablet or mobile device CPD accredited course - improve the chance of gaining professional skills Gain valuable knowledge without leaving your home What other courses are included with this Course? Level 2 Microsoft Office Essentials Microsoft Teams Leadership & Management Diploma Working from Home Essentials Mental Health and Working from Home Online Meeting Management Effective Communication Skills Time Management Report Writing Emotional Intelligence and Human Behaviour Curriculum ***Data Science Course Bundle 2022*** Welcome, Course Introduction & overview, and Environment set-up Welcome & Course Overview Set-up the Environment for the Course (lecture 1) Set-up the Environment for the Course (lecture 2) Two other options to setup environment Python Essentials Python data types Part 1 Python Data Types Part 2 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) Python Essentials Exercises Overview Python Essentials Exercises Solutions Python for Data Analysis using NumPy What is Numpy? A brief introduction and installation instructions. NumPy arrays, built-in methods, array methods and attributes. Indexing, slicing, broadcasting & boolean masking Arithmetic Operations & Universal Functions Exercises Overview Exercises Solutions Python for Data Analysis using Pandas What is pandas? A brief introduction and installation instructions. Pandas Introduction Pandas Data Structures - Series Pandas Data Structures - DataFrame Handling Missing Data Data Wrangling - Combining, merging, joining Groupby Useful Methods and Operations Project 1 (Overview) Customer Purchases Data Project 1 (Solutions) Customer Purchases Data Project 2 (Overview) Chicago Payroll Data Project 2 (Solutions Part 1) Chicago Payroll Data Python for Data Visualization using matplotlib Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach Matplotlib Essentials - Exercises Overview Matplotlib Essentials - Exercises Solutions Python for Data Visualization using Seaborn Introduction & Installation Distribution Plots Categorical Plots (Part 1) Categorical Plots (Part 2) Axis Grids Matrix Plots Regression Plots Controlling Figure Aesthetics Exercises Overview Exercise Solutions Python for Data Visualization using pandas Pandas Built-in Data Visualization Pandas Data Visualization Exercises Overview Panda Data Visualization Exercises Solutions Python for interactive & geographical plotting using Plotly and Cufflinks Interactive & Geographical Plotting (Part 1) Interactive & Geographical Plotting (Part 2) Interactive & Geographical Plotting Exercises (Overview) Interactive & Geographical Plotting Exercises (Solutions) Capstone Project - Python for Data Analysis & Visualization Project 1 - Oil vs Banks Stock Price during recession (Overview) Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1) Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2) Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3) Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview) Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Introduction to ML - What, Why and Types….. Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff Linear Regression Model - Hands-on (Part 1) Linear Regression Model Hands-on (Part 2) Good to know! How to save and load your trained Machine Learning Model! Linear Regression Model (Insurance Data Project Overview) Linear Regression Model (Insurance Data Project Solutions) Python for Machine Learning - scikit-learn - Logistic Regression Model Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificity…etc. Logistic Regression Model - Hands-on (Part 1) Logistic Regression Model - Hands-on (Part 2) Logistic Regression Model - Hands-on (Part 3) Logistic Regression Model - Hands-on (Project Overview) Logistic Regression Model - Hands-on (Project Solutions) Python for Machine Learning - scikit-learn - K Nearest Neighbors Theory: K Nearest Neighbors, Curse of dimensionality …. K Nearest Neighbors - Hands-on K Nearest Neighbors (Project Overview) K Nearest Neighbors (Project Solutions) Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging…. Decision Tree and Random Forests - Hands-on (Part 1) Decision Tree and Random Forests (Project Overview) Decision Tree and Random Forests (Project Solutions) Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) Support Vector Machines (SVMs) - (Theory Lecture) Support Vector Machines - Hands-on (SVMs) Support Vector Machines (Project 1 Overview) Support Vector Machines (Project 1 Solutions) Support Vector Machines (Optional Project 2 - Overview) Python for Machine Learning - scikit-learn - K Means Clustering Theory: K Means Clustering, Elbow method ….. K Means Clustering - Hands-on K Means Clustering (Project Overview) K Means Clustering (Project Solutions) Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Theory: Principal Component Analysis (PCA) Principal Component Analysis (PCA) - Hands-on Principal Component Analysis (PCA) - (Project Overview) Principal Component Analysis (PCA) - (Project Solutions) Recommender Systems with Python - (Additional Topic) Theory: Recommender Systems their Types and Importance Python for Recommender Systems - Hands-on (Part 1) Python for Recommender Systems - - Hands-on (Part 2) Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) Natural Language Processing (NLP) - (Theory Lecture) NLP-Challenges, Data Sources, Data Processing ….. Feature Engineering and Text Preprocessing in Natural Language Processing NLP - Tokenization, Text Normalization, Vectorization, BoW…. BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes … Pipeline feature to assemble several steps for cross-validation… How will I get my Certificate? After successfully completing the course you will be able to order your CPD Accredited Certificates (PDF + Hard Copy) as proof of your achievement. PDF Certificate: Free (Previously it was £10 * 11 = £110) Hard Copy Certificate: Free (For The Title Course) If you want to get hardcopy certificates for other courses, generally you have to pay £20 for each. But this Fall, Apex Learning is offering a Flat 50% discount on hard copy certificates, and you can get each for just £10! P.S. The delivery charge inside the U.K. is £3.99 and the international students have to pay £9.99. CPD 20 CPD hours / points Accredited by CPD Quality Standards Who is this course for? There is no experience or previous qualifications required for enrolment on this Data Science Course Bundle 2022. It is available to all students, of all academic backgrounds. Requirements Our Data Science Course Bundle 2022 is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible on tablets and smartphones so you can access your course on wifi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career path Having this CPD certificate will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. Certificates Certificate of completion Digital certificate - Included

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Whether you want to close a business deal or sell a house effective negotiation skills can help you seal the deal. This craft can take you a long way in both your personal and professional life. Learn this useful skill set from the comfort of your home with the Negotiation Skills Certificate course. This course will take your skills to the next level in no time. This course will provide quality training on negotiation. Through the informative and engaging modules, you will get a clear understanding of collaborative negotiation. Then it will provide elaborate lessons on the seven steps of successful negotiation. Next, you will learn how to apply these steps in your home. The course will also teach you tips and tricks to negotiate with different personalities. In addition to this valuable skill set you will also receive a certificate of achievement which will add significant value to your resume. Join now! Learning Outcomes  Get introduced to the essentials of collaborative negotiation Learn about the 7 steps of successful negotiation in detail Know how to use these 7 steps in your home Develop the skills to negotiate with different personality types Identify the practices you should avoid while negotiating Who is this Course for? Anyone who wants to improve their negotiation skills can join the course. The Negotiation Skills Certificate course will equip the learners with excellent negotiation techniques and help them close lucrative deals. Entry Requirements Individuals from any background can take the course A good understanding of the English language, numeracy and ICT Any smart device such as a laptop, pc, or smartphone with an internet connection CPD Certificate from Course Gate  After completing the final assignment, you will receive a CPD-accredited certificate. The PDF copy of the certificate will cost you £9. You can order the hard copy for £15, or you can get both the hard copy and PDF certificate for £22. Career Path This course will provide the learners with an in-depth understanding of the subject. After completing the course, the learners will develop the skills and knowledge to explore career opportunities in the following fields  Sales Executives Customer Service Executives Project Managers Estate Agent Contract Specialist Course Curriculum Introduction and The Principles of Collaborative Negotiation Welcome and Course Overview 00:06:00 Why Good Negotiation Practice Leads to Better Relationships 01:03:00 Shameless Book Plug 00:01:00 Millie's Cookie Story 00:07:00 Exercise 1: Intentions / Objectives for This Programme 00:01:00 Giving Structure to Your Negotiation Strategy Negotiation is not 00:02:00 Distinguishing Negotiation from -Haggling 00:07:00 The 7 Steps to Negotiation Success 00:06:00 Exercise 2: Giving Structure to your Negotiations 00:01:00 Step One - Preparing Yourself for Collaborative Negotiation Preparing Yourself and Your WIN Outcomes 00:06:00 Exercise 3: Securing Commitment to Negotiate 00:01:00 The 4 P's 00:01:00 The Importance of Personality 00:02:00 We, Then Me 00:02:00 Exercise 4: The 4 P's 00:01:00 Step Two - Preparation - Understanding the Power of Variables Introduction to Variables 00:04:00 Video Examples of Excellent Creativity in Variables 00:03:00 Exercise 5: Understanding the Power of Variables 00:01:00 Using the WIN Matrix 00:03:00 Exercise 6: Write Your Win Matrix 00:02:00 Step Three - Understanding Your Partner's Point of View Introduction 00:01:00 Example Story- Maps of the World - Dyl's Den 00:03:00 Exercise 7: Stepping Into Your Partner's Shoes 00:01:00 Step Four - Discussing Introduction- Stating Intentions 00:04:00 Co-Active Listening- Are You Really Listening 00:02:00 The Power of Pause 00:01:00 Exercise 8: Using Open Questions 00:01:00 Exercise 9: Going Above and Beyond Their Wildest Dreams 00:01:00 Exercise 10: Socratic Questioning 00:04:00 Exercise 11: Creating a Discussion Agreement Statement= 00:01:00 Step Five - Proposing Introduction to the Propose Stage 00:05:00 Exercise 12: Putting Your Proposal into Writing 00:01:00 Step Six - Bargaining Introduction 00:02:00 Exercise 13: Creating a Bargaining Agreement Statement 00:02:00 The Power of Silence 00:04:00 Exercise 14: Developing Your Time-Out Strategy 00:01:00 Step Seven - Agreeing Introduction to Bargaining 00:04:00 The Written Columbo 00:02:00 Exercise 15: Drafting an 'Agreement In Principle' 00:01:00 Getting Yourself Out of the Way - The Human Operating System Introduction - The Missing Link 00:04:00 Exercise 16: Noticing Your Thinking 00:02:00 What Does this Mean in Your Negotiations? 00:03:00 Understanding Personality Why Personality? 00:05:00 Introducing the 4 Colours 00:04:00 Introducing the 8 Aspects 00:04:00 Inspiration v Discipline Driven 00:04:00 Exercise 17: Teddy Bear 00:01:00 Big Picture vs Down to Earth 00:03:00 Exercise 18: Football Club Trip 00:01:00 People Focused vs Outcome Focused 00:02:00 Splash App 00:02:00 Exercise 19: Completing Your Own Assessment 00:01:00 Negotiation with Different 'Personality Types' 00:04:00 Using the Seven Steps at Home Introduction 00:04:00 Avoiding Common Gambits Some Negotiators Use Nibbling - The Columbo 00:02:00 The Flinch 00:02:00 The Red Herring 00:01:00 Higher Authority 00:02:00 The Reluctant Buyer - Seller 00:01:00 The Best of a Bad Choice 00:01:00 Conclusion - Can You Really Get More by Giving More? Conclusion & Thank You 00:02:00 Bonus Lecture 00:01:00 Certificate and Transcript Order Your Certificates or Transcripts 00:00:00

Overview This complete education course will improve your Project Management Training.This wide curriculum gives you the skills and information you need to succeed in this fast-paced industry, and the highly desired CPD accreditation can further improve your resume. With this training, you'll be prepared for a quick rise rather than taking it step by step up the career ladder. Participate now to see how your project management skills increase significantly! Why Choose this Project Management Bundle? FREE Project Management CPD-accredited certificate Get a free student ID card with Project Management training (£10 applicable for international delivery) Lifetime access to the Project Management course materials The Project Management program comes with 24/7 tutor support Get instant access to this Project Management course Learn Project Management training from anywhere in the world The Project Management training is affordable and simple to understand The Project Management training is entirely online How will I get my Project Management certificate? Upon successful completion of the Project Management Training course, obtaining your certificate is a straightforward process. Here's how it works: Course Completion: Once you've successfully finished the course, you'll be eligible to receive your certificate. Order Your Certificate: You can conveniently order your certificate directly through our platform. Who is This Project Management course for? There is no experience or previous qualifications required for enrolment on this Project Management Training. It is available to all students, of all academic backgrounds. Requirements Our Project Management Training course is designed for maximum flexibility and accessibility: It is optimised for use on PCs, Macs, laptops, tablets, and smartphones. Study easily on your tablet or smartphone, accessible with any Wi-Fi connection. No time limit for completion; study at your own pace and on your own schedule. Basic English proficiency is required to ensure effective learning. Career Path Having this Project Management qualification will enhance your CV and open you up to multiple sectors, such as : Project Manager: £45k - £90k per year Project Coordinator: £35k - £70k per year Program Manager: £50k - £95k per year Project Analyst: £30k - £65k per year Project Management Consultant: £55k - £100k+ per year Note: Salaries vary based on experience, location, and industry. Course Curriculum 12 sections • 86 lectures • 03:24:00 total length •1.1 Introduction to Project Management: 00:04:00 •1.2 6 Pathways of Project Management: 00:03:00 •1.3 Course Structure: Simplified Framework: 00:01:00 •1.4 System Thinking: 00:01:00 •1.5 Project Timeline: 00:01:00 •1.6 Pyramid Project: 00:03:00 •2.1 Project: 00:02:00 •2.2 Project Management mbmbmbm: 00:01:00 •2.3 The Matrix Environment: 00:04:00 •2.4 Enterprise Environmental Factors and Organisational Process Assets: 00:01:00 •2.5 Project Constraints: 00:01:00 •2.6 Process Groups: 00:03:00 •2.7 Integration Management: 00:02:00 •2.8 Stakeholder Management: 00:01:00 •2.9 Scope Management: 00:01:00 •2.10 Schedule Management: 00:01:00 •2.11 Cost, HR, Quality and Risk Management: 00:02:00 •2.12 Communication and Procurement Management: 00:01:00 •2.13 Knowledge Areas and Process Groups: 00:01:00 •3.1 Develop Project Charter: 00:05:00 •3.2 Project Charter Template: 00:06:00 •3.3 Timeline View of Project Documents: 00:01:00 •4.1 Identify Stakeholders: 00:04:00 •4.2 Stakeholder Register Template: 00:01:00 •4.3 Timeline View of Project Documents: 00:04:00 •5.1 Project Scope: 00:04:00 •5.2 Collect Requirements: 00:02:00 •5.3 Define Project Scope: 00:01:00 •5.4 Create WBS: Work Breakdown Structure: 00:03:00 •5.5 Project Scope Baseline: 00:06:00 •5.6 Timeline View of Project Documents: 00:01:00 •5.7 Microsoft Project: 00:02:00 •5.8 MS Project - User Interface Explained: 00:02:00 •5.9 MS Project - Entering Scope Items: 00:06:00 •6.1 Define Activities: 00:04:00 •6.2 Sequence Activities: 00:02:00 •6.3 Estimating Activity Durations: 00:02:00 •6.4 Develop Schedule Process: 00:01:00 •6.5 Critical Path Method: 00:03:00 •6.6 Compressing Schedule : Crashing and Fast-tracking: 00:03:00 •6.7 Timeline View of Project Documents: 00:01:00 •6.8 Microsoft Project - Entering Activities and Duration: 00:03:00 •6.9 Microsoft Project - Sequencing Activities & Creating Gantt Chart: 00:07:00 •6.10 Microsoft Project - The Project Schedule: 00:06:00 •7.1 Estimated Costs: 00:03:00 •7.2 Determined Budget: 00:01:00 •7.3 Timeline View of Project Documents: 00:01:00 •7.4 Estimate Activity Resources: 00:02:00 •7.5 Timeline View of Project Documents: 00:01:00 •7.6 Plan Quality: 00:04:00 •7.7 Timeline View of Project Documents: 00:01:00 •7.8 Introduction to Risks: 00:03:00 •7.9 Identify Risks: 00:02:00 •7.10 Perform Qualitative Risk Analysis: 00:04:00 •7.11 Perform Quantitative Risk Analysis: 00:01:00 •7.12 Plan Risk Responses: 00:02:00 •7.13 Timeline View of Project Documents: 00:01:00 •8.1 Plan Communication: 00:02:00 •8.2 Timeline View of Project Documents: 00:01:00 •8.3 Plan Procurement: 00:02:00 •8.4 Timeline View of Project Documents: 00:01:00 •9.1 HOW are things done on the project: 00:06:00 •9.2 Develop Project Management Plan: 00:01:00 •9.3 Individual Management Plans: 00:04:00 •9.4 Timeline View of Project Documents: 00:01:00 •9.5 Timeline View of Project Documents-2: 00:01:00 •9.6 Direct and Manage Project Work: 00:04:00 •9.7 Timeline View of Project Documents: 00:01:00 •9.8 Manage Project Knowledge: 00:02:00 •9.9 Timeline View of Project Documents: 00:01:00 •9.10 Monitor and Control Project Work: 00:02:00 •9.11 Timeline View of Project Documents: 00:01:00 •9.12 Perform Integrated Change Control: 00:04:00 •9.13 Steps to perform Change Control: 00:05:00 •9.14 Timeline View of Project Documents: 00:01:00 •9.15 Budget at Completion: BAC: 00:05:00 •9.16 Planned Value: PV: 00:02:00 •9.17 Earned Value: EV: 00:02:00 •9.18 Schedule Variance (SV) & Schedule Performance Index (SPI): 00:02:00 •9.19 Actual Cost(AC), Cost Variance and Cost Performance Index: 00:03:00 •9.20 Budget Forecasting - Estimate at & to completion: 00:06:00 •9.21 Close Project or Phase: 00:03:00 •9.22 Timeline View of Project Documents: 00:01:00 •Resources - Project Management Training: 00:00:00 •Assignment - Project Management Training: 3 days •Order Your Certificate: 00:00:00