2239 Evaluation courses in Bristol delivered On Demand

This course covers Python for data science and machine learning in detail and is for a beginner in Python. You will also learn about core concepts of data science, exploratory data analysis, statistical methods, role of data, challenges of bias, variance and overfitting, model evaluation techniques, model optimization using hyperparameter tuning, grid search cross-validation techniques, and more.

This Level 3 Award in Education and Training is accredited by NCFE and regulated by Ofqual. The National Council for Educational Awarding (NCFE) is a national educational awarding body that is well-known and respected throughout the world, which will improve your prospects of finding employment and showcase your professional growth. Course Curriculum Course Overview Course Overview - Level 3 Award in Education and Training 00:00:00 Lesson 1 - Roles and Responsibilities of Teachers Lesson 1 - Roles and Responsibilities of Teachers 00:05:00 Lesson 2 - Legislation, Regulatory Requirements and Codes of Practice in Teaching Lesson 2 - Legislation, Regulatory Requirements and Codes of Practice in Teaching 00:11:00 Lesson 3 - Factors Contributing to Effective Learning Lesson 3 - Factors Contributing to Effective Learning 00:13:00 Lesson 4 - Identifying Needs Lesson 4 - Identifying Needs 00:12:00 Lesson 5 - Planning in Teaching and Learning Lesson 5 - Planning in Teaching and Learning 00:11:00 Lesson 6 - Augmenting the Learning Process Lesson 6 - Augmenting the Learning Process 00:10:00 Lesson 7 - The Assessment Approach to Learning Lesson 7 - The Assessment Approach to Learning 00:13:00 Lesson 8 - The Evaluation Process in Learning Lesson 8 - The Evaluation Process in Learning 00:12:00 Lesson 9 - Learning Effective Teaching Microteaching Lesson 9 - Learning Effective Teaching Microteaching 00:10:00 Additional Resource Additional Resource - Level 3 Award in Education and Training 00:00:00 Assignment - Mandatory Units Assignment 1: Understanding Roles, Responsibilities and Relationships in Education and Training Assignment 1 - Understanding Roles, Responsibilities and Relationships in Education and Training 00:14:00 Assignment - Optional Units Assignment 2: Understanding and Using Inclusive Teaching and Learning Approaches in Education and Training Assignment 2 - Understanding and Using Inclusive Teaching and Learning Approaches in Education and Training 00:12:00 Assignment 3: Understanding the Principles and Practices of Assessment Assignment 3 - Understanding the Principles and Practices of Assessment 00:07:00 Feedback Feedback 00:00:00

Overview Mastering data science skills and expertise can open new doors of opportunities for you in a wide range of fields. Learn the fundamentals and develop a solid grasp of Python data science with the comprehensive Data Science with Python course. This course is designed to assist you in securing a valuable skill set and boosting your career. This course will provide you with quality training on the fundamentals of data analysis with Python. From the step-by-step learning process, you will learn the techniques of setting up the system. Then the course will teach you Python data structure and functions. You will receive detailed lessons on NumPy, Matplotlib, and Pandas. Furthermore, you will develop the skills for Algorithm Evaluation Techniques, visualising datasets and much more. After completing the course you will receive a certificate of achievement. This certificate will help you create an impressive resume. So join today! How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? This course Data Science with Python course is ideal for beginners in data science. It will help them develop a solid grasp of Python and help them pursue their dream career in the field of data science. Requirements The students will not require any formal qualifications or previous experience to enrol in this course. Anyone can learn from the course anytime from anywhere through smart devices like laptops, tabs, PC, and smartphones with stable internet connections. They can complete the course according to their preferable pace so, there is no need to rush. Career Path This course will equip you with valuable knowledge and effective skills in this area. After completing the course, you will be able to explore career opportunities in the fields such as Data Analyst Data Scientist Data Manager Business Analyst And much more! Course Curriculum 90 sections • 90 lectures • 10:19:00 total length •Course Overview & Table of Contents: 00:09:00 •Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types: 00:05:00 •Introduction to Machine Learning - Part 2 - Classifications and Applications: 00:06:00 •System and Environment preparation - Part 1: 00:04:00 •System and Environment preparation - Part 2: 00:06:00 •Learn Basics of python - Assignment 1: 00:10:00 •Learn Basics of python - Assignment 2: 00:09:00 •Learn Basics of python - Functions: 00:04:00 •Learn Basics of python - Data Structures: 00:12:00 •Learn Basics of NumPy - NumPy Array: 00:06:00 •Learn Basics of NumPy - NumPy Data: 00:08:00 •Learn Basics of NumPy - NumPy Arithmetic: 00:04:00 •Learn Basics of Matplotlib: 00:07:00 •Learn Basics of Pandas - Part 1: 00:06:00 •Learn Basics of Pandas - Part 2: 00:07:00 •Understanding the CSV data file: 00:09:00 •Load and Read CSV data file using Python Standard Library: 00:09:00 •Load and Read CSV data file using NumPy: 00:04:00 •Load and Read CSV data file using Pandas: 00:05:00 •Dataset Summary - Peek, Dimensions and Data Types: 00:09:00 •Dataset Summary - Class Distribution and Data Summary: 00:09:00 •Dataset Summary - Explaining Correlation: 00:11:00 •Dataset Summary - Explaining Skewness - Gaussian and Normal Curve: 00:07:00 •Dataset Visualization - Using Histograms: 00:07:00 •Dataset Visualization - Using Density Plots: 00:06:00 •Dataset Visualization - Box and Whisker Plots: 00:05:00 •Multivariate Dataset Visualization - Correlation Plots: 00:08:00 •Multivariate Dataset Visualization - Scatter Plots: 00:05:00 •Data Preparation (Pre-Processing) - Introduction: 00:09:00 •Data Preparation - Re-scaling Data - Part 1: 00:09:00 •Data Preparation - Re-scaling Data - Part 2: 00:09:00 •Data Preparation - Standardizing Data - Part 1: 00:07:00 •Data Preparation - Standardizing Data - Part 2: 00:04:00 •Data Preparation - Normalizing Data: 00:08:00 •Data Preparation - Binarizing Data: 00:06:00 •Feature Selection - Introduction: 00:07:00 •Feature Selection - Uni-variate Part 1 - Chi-Squared Test: 00:09:00 •Feature Selection - Uni-variate Part 2 - Chi-Squared Test: 00:10:00 •Feature Selection - Recursive Feature Elimination: 00:11:00 •Feature Selection - Principal Component Analysis (PCA): 00:09:00 •Feature Selection - Feature Importance: 00:06:00 •Refresher Session - The Mechanism of Re-sampling, Training and Testing: 00:12:00 •Algorithm Evaluation Techniques - Introduction: 00:07:00 •Algorithm Evaluation Techniques - Train and Test Set: 00:11:00 •Algorithm Evaluation Techniques - K-Fold Cross Validation: 00:09:00 •Algorithm Evaluation Techniques - Leave One Out Cross Validation: 00:05:00 •Algorithm Evaluation Techniques - Repeated Random Test-Train Splits: 00:07:00 •Algorithm Evaluation Metrics - Introduction: 00:09:00 •Algorithm Evaluation Metrics - Classification Accuracy: 00:08:00 •Algorithm Evaluation Metrics - Log Loss: 00:03:00 •Algorithm Evaluation Metrics - Area Under ROC Curve: 00:06:00 •Algorithm Evaluation Metrics - Confusion Matrix: 00:10:00 •Algorithm Evaluation Metrics - Classification Report: 00:04:00 •Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction: 00:06:00 •Algorithm Evaluation Metrics - Mean Absolute Error: 00:07:00 •Algorithm Evaluation Metrics - Mean Square Error: 00:03:00 •Algorithm Evaluation Metrics - R Squared: 00:04:00 •Classification Algorithm Spot Check - Logistic Regression: 00:12:00 •Classification Algorithm Spot Check - Linear Discriminant Analysis: 00:04:00 •Classification Algorithm Spot Check - K-Nearest Neighbors: 00:05:00 •Classification Algorithm Spot Check - Naive Bayes: 00:04:00 •Classification Algorithm Spot Check - CART: 00:04:00 •Classification Algorithm Spot Check - Support Vector Machines: 00:05:00 •Regression Algorithm Spot Check - Linear Regression: 00:08:00 •Regression Algorithm Spot Check - Ridge Regression: 00:03:00 •Regression Algorithm Spot Check - Lasso Linear Regression: 00:03:00 •Regression Algorithm Spot Check - Elastic Net Regression: 00:02:00 •Regression Algorithm Spot Check - K-Nearest Neighbors: 00:06:00 •Regression Algorithm Spot Check - CART: 00:04:00 •Regression Algorithm Spot Check - Support Vector Machines (SVM): 00:04:00 •Compare Algorithms - Part 1 : Choosing the best Machine Learning Model: 00:09:00 •Compare Algorithms - Part 2 : Choosing the best Machine Learning Model: 00:05:00 •Pipelines : Data Preparation and Data Modelling: 00:11:00 •Pipelines : Feature Selection and Data Modelling: 00:10:00 •Performance Improvement: Ensembles - Voting: 00:07:00 •Performance Improvement: Ensembles - Bagging: 00:08:00 •Performance Improvement: Ensembles - Boosting: 00:05:00 •Performance Improvement: Parameter Tuning using Grid Search: 00:08:00 •Performance Improvement: Parameter Tuning using Random Search: 00:06:00 •Export, Save and Load Machine Learning Models : Pickle: 00:10:00 •Export, Save and Load Machine Learning Models : Joblib: 00:06:00 •Finalizing a Model - Introduction and Steps: 00:07:00 •Finalizing a Classification Model - The Pima Indian Diabetes Dataset: 00:07:00 •Quick Session: Imbalanced Data Set - Issue Overview and Steps: 00:09:00 •Iris Dataset : Finalizing Multi-Class Dataset: 00:09:00 •Finalizing a Regression Model - The Boston Housing Price Dataset: 00:08:00 •Real-time Predictions: Using the Pima Indian Diabetes Classification Model: 00:07:00 •Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset: 00:03:00 •Real-time Predictions: Using the Boston Housing Regression Model: 00:08:00 •Resources - Data Science & Machine Learning with Python: 00:00:00

Overview of Data Science & Machine Learning with Python Join our Data Science & Machine Learning with Python course and discover your hidden skills, setting you on a path to success in this area. Get ready to improve your skills and achieve your biggest goals. The Data Science & Machine Learning with Python course has everything you need to get a great start in this sector. Improving and moving forward is key to getting ahead personally. The Data Science & Machine Learning with Python course is designed to teach you the important stuff quickly and well, helping you to get off to a great start in the field. So, what are you looking for? Enrol now! This Data Science & Machine Learning with Python Course will help you to learn: Learn strategies to boost your workplace efficiency. Hone your skills to help you advance your career. Acquire a comprehensive understanding of various topics and tips. Learn in-demand skills that are in high demand among UK employers This course covers the topic you must know to stand against the tough competition. The future is truly yours to seize with this Data Science & Machine Learning with Python. Enrol today and complete the course to achieve a certificate that can change your career forever. Details Perks of Learning with IOMH One-To-One Support from a Dedicated Tutor Throughout Your Course. Study Online - Whenever and Wherever You Want. Instant Digital/ PDF Certificate. 100% Money Back Guarantee. 12 Months Access. Process of Evaluation After studying the course, an MCQ exam or assignment will test your skills and knowledge. You have to get a score of 60% to pass the test and get your certificate. Certificate of Achievement Certificate of Completion - Digital / PDF Certificate After completing the Data Science & Machine Learning with Python course, you can order your CPD Accredited Digital / PDF Certificate for £5.99.  Certificate of Completion - Hard copy Certificate You can get the CPD Accredited Hard Copy Certificate for £12.99. Shipping Charges: Inside the UK: £3.99 International: £10.99 Who Is This Course for? This Data Science & Machine Learning with Python is suitable for anyone aspiring to start a career in relevant field; even if you are new to this and have no prior knowledge, this course is going to be very easy for you to understand.  On the other hand, if you are already working in this sector, this course will be a great source of knowledge for you to improve your existing skills and take them to the next level.  This course has been developed with maximum flexibility and accessibility, making it ideal for people who don't have the time to devote to traditional education. Requirements You don't need any educational qualification or experience to enrol in the Data Science & Machine Learning with Python course. Do note: you must be at least 16 years old to enrol. Any internet-connected device, such as a computer, tablet, or smartphone, can access this online course. Career Path The certification and skills you get from this Data Science & Machine Learning with Python Course can help you advance your career and gain expertise in several fields, allowing you to apply for high-paying jobs in related sectors. 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 00:04:00 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 Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using NumPy 00:04:00 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 00:06:00 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 - Data Science & Machine Learning with Python 00:00:00

The Professional Certificate Course in Project Procurement Legislative Framework provides learners with a comprehensive understanding of the procurement legal framework, its components, and examples of legal framework requirements. This course also covers the meaning and types of public procurement directives, as well as the tender process and its evaluation. By the end of the course, learners will have a solid grasp of procurement laws and regulations that govern the acquisition of goods and services. The Professional Certificate Course in Project Procurement Legislative Framework covers the legal framework and requirements of public procurement, including tender processes and evaluation. The course also explores various examples of legal frameworks and types of public procurement directives. After the successful completion of this course, you will understand the following The meaning and components of procurement legal framework. The various examples of legal framework requirements. The meaning and types of public procurement directives. The tender process and its evaluation. The Professional Certificate Course in Project Procurement Legislative Framework provides learners with a comprehensive understanding of the procurement legal framework, its components, and examples of legal framework requirements. This course also covers the meaning and types of public procurement directives, as well as the tender process and its evaluation. By the end of the course, learners will have a solid grasp of procurement laws and regulations that govern the acquisition of goods and services. VIDEO - Course Structure and Assessment Guidelines Watch this video to gain further insight. Navigating the MSBM Study Portal Watch this video to gain further insight. Interacting with Lectures/Learning Components Watch this video to gain further insight. Project procurement legislative framework Self-paced pre-recorded learning content on this topic. Project procurement legislative framework Put your knowledge to the test with this quiz. Read each question carefully and choose the response that you feel is correct. All MSBM courses are accredited by the relevant partners and awarding bodies. Please refer to MSBM accreditation in about us for more details. There are no strict entry requirements for this course. Work experience will be added advantage to understanding the content of the course. The certificate is designed to enhance the learner's knowledge in the field. This certificate is for everyone eager to know more and get updated on current ideas in their respective field. We recommend this certificate for the following audience, Professionals involved in project procurement Contract administrators Procurement officers Project managers Legal professionals working in procurement and contracts Average Completion Time 2 Weeks Accreditation 3 CPD Hours Level Advanced Start Time Anytime 100% Online Study online with ease. Unlimited Access 24/7 unlimited access with pre-recorded lectures. Low Fees Our fees are low and easy to pay online.

Level 3 QLS Endorsed Course | Endorsed Certificate Included | Plus 5 Career Guided Courses | CPD Accredited

Effecting Business Process Improvement - The Proactive Business Analyst: On-Demand Business analysts facilitate the solution of business problems. The solutions are put into practice as changes to the way people perform in their organizations and the tools they use. The business analyst is a change agent who must understand the basic principles of quality management. This course covers the key role that business analysts play in organizational change management. What You Will Learn You will learn how to: Define and document a business process Work with various business modeling techniques Perform an enterprise analysis in preparation for determining requirements Analyze business processes to discern problems Foundation Concepts Overview of business analysis and process improvement Defining the business process Introducing the proactive business analyst Focusing on business process improvement for business analysts Launching a Successful Business Process Improvement Project Overview of the launch phase Understanding and creating organizational strategy Selecting the target process Aligning the business process improvement project's goals and objectives with organizational strategy Defining the Current Process Overview of current process phase Documenting the business process Business modeling options: work-flow models Business modeling options: Unified Modeling Language (UML) model adaptations for business processes Analyzing the Current Process Process analysis overview Evaluation: establishing the control group Opportunity techniques: multi-discipline problem-solving Opportunity techniques: matrices Building and Sustaining a Recommended Process Overview of the recommended process and beyond Impact analysis Recommended process Transition to the business case Return to proactive state

Enhance your auditing expertise with our ISO 19011 Lead Auditor Course, covering management system auditing principles, ISO 9001:2015 clauses, and auditor competence evaluation. Become a proficient lead auditor and drive organizational improvement. Enroll now!

Are you looking to improve your teaching skills to establish a rewarding career? No Previous Experience Needed Available to individuals from all backgrounds, regardless of their previous work experience Unlimited Tutor Support With endless support from an expert tutor, learning has never been simpler Recognised Qualification Ofqual regulation and NCFE accreditation make this course a highly reputable qualification in the industry Interest Free Instalment Plan Pay in 4 interest-free instalments and spread the cost of your purchase over time Fast Track Accelerate your success and start your rewarding career faster with the fast track program Exam Pass Guarantee We are committed to your success and will work closely with you to ensure your success in the exam This comprehensive Level 3 Award in Education and Training (AET) | PTLLS will equip you with the skills to work as a teacher in the UK. With the help of this education and training certification, a nationally recognized credential for teaching in the area of lifelong learning, you can continue your education and develop as a certified and experienced teacher. Course Highlights Course Type: Online Learning Guided Learning Hours (GLH): 48 hours Accreditation: NCFE Qualification: Ofqual Regulated Access: 1 Year Access Certificate: Certificate upon completion (hard copy) Tutor Support- Personalised feedback on all your assignments Customer Support: 24/7 live chat available Level 3 Award in Education and Training This Award in Education and Training Level 3 (AET) | PTLLS will equip you with the necessary training and credentials to demonstrate your knowledge in the teaching profession. NCFE accredits and Ofqual regulates this course, which enhances your resume as a nationally recognised qualification. Upon successful completion of this course, you will gain extensive teaching skills and knowledge to evaluate, create resources, and assist students to reach their full potential. Learning Outcomes By the end of this course, you will: Recognise the duties and role of teaching in education and training Discover how to maintain a supportive and secure learning environment Discover the connections between teachers and other training and education professionals Who should take this course? This extensive online course is suitable for:  Aspiring teachers Teachers of any level Teacher trainers Teachers seeking to enhance their teaching skills Teachers looking to improve their job prospects Entry Requirements This Level 3 Award in Education and Training (AET) | PTLLS is available to all students, of all academic backgrounds and no experience or previous qualifications are required.  Assessment structureAssignment Students must complete a number of brief assignments for each section of the award. These assessments are designed to assess you in accordance with the following guidelines: Submit short assignments for each module Assignments should include theory, relevant to real-world workplaces and relate to actual teaching situations. These assignments will aid in your exploration and application of the entire teaching and learning cycle. Once the tutor receives and reviews the assignments, they will comment and ask you to resubmit them if with the required amendments. Microteaching Assessment We internally evaluate and externally monitor this certification to ensure quality. To complete the assessment process, you must submit a teaching evaluation that will assess you based on the following criteria: To complete the task, you need to conduct microteaching sessions lasting at least 15 minutes in a classroom environment and film and share videos of them. Once you submit your assessments, your teacher will review and grade them individually. This practical assessment will help you demonstrate your skills and knowledge gathered from three units Additionally, you'll need to provide teaching records and documentation such as lesson plans, learner evaluation, scheme of work, etc proving you've taught in a classroom. Learners who achieve this qualification could progress to: Level 4 Certificate in Education and Training Level 5 Diploma in Education and Training Course Curriculum Course Overview Course Overview - Level 3 Award in Education and Training Lesson 1 - Roles and Responsibilities of Teachers Lesson 1 - Roles and Responsibilities of Teachers Lesson 2 - Legislation, Regulatory Requirements and Codes of Practice in Teaching Lesson 2 - Legislation, Regulatory Requirements and Codes of Practice in Teaching Lesson 3 - Factors Contributing to Effective Learning Lesson 3 - Factors Contributing to Effective Learning Lesson 4 - Identifying Needs Lesson 4 - Identifying Needs Lesson 5 - Planning in Teaching and Learning Lesson 5 - Planning in Teaching and Learning Lesson 6 - Augmenting the Learning Process Lesson 6 - Augmenting the Learning Process Lesson 7 - The Assessment Approach to Learning Lesson 7 - The Assessment Approach to Learning Lesson 8 - The Evaluation Process in Learning Lesson 8 - The Evaluation Process in Learning Lesson 9 - Learning Effective Teaching Microteaching Lesson 9 - Learning Effective Teaching Microteaching Assignment 1: Understanding Roles, Responsibilities and Relationships in Education and Training Assignment 1 - Understanding Roles, Responsibilities and Relationships in Education and Training Assignment 2: Understanding and Using Inclusive Teaching and Learning Approaches in Education and Training Assignment 2 - Understanding and Using Inclusive Teaching and Learning Approaches in Education and Training Assignment 3: Understanding the Principles and Practices of Assessment Assignment 3 - Understanding the Principles and Practices of Assessment Recognised Accreditation This Level 3 Award in Education and Training (AET) | PTLLS is independently accredited by NCFE and regulated by Ofqual. It is a nationally recognised qualification that will help you pave your path to higher education and fulfil the entry requirements of any skilled-oriented job. About NCFE The National Council for Educational Awarding (NCFE) is a national educational awarding organisation that creates, develops, and accredits a range of widely accepted qualifications and awards, including those for online courses. The NCFE Functional Skills certificate is the best option for students who want to gain useful, transferable skills that will enable them to function freely, with self-assurance, and effectively in the real world. Certificate of Achievement Upon successful completion of this Award in Education and Training Level 3 (AET) you will be awarded the qualification: NCFE Level 3 Award in Education and Training which is valued by all employers in the UK and globally.  FAQs What is a level 3 award in education and training equivalent to in the UK? Level 3 qualifications are equivalent to A level and other equivalent qualifications. What can I do with a Level 3 award in education and training? You can advance to further vocational study and earn a Bachelor of Arts in Education or a BA in Education (BEd). Besides these progression opportunities, you can also enhance your resume and increase your chances of getting hired. Do I have to conduct my practical evaluation in a real classroom or workplace? For the Level 3 award in Education and Training qualification, you must be seen teaching in an actual classroom; simulation is not allowed, and your observations must be conducted in an actual classroom. How is this course assessed? The course is evaluated through the submission of 15 min micro teaching sessions that you conducted in a classroom setting and writing assignments based on the course's sections. Once you submit your assessments, your teacher will individually review and grade them. Can I submit my assignment again? Your instructor will provide feedback and the option to resubmit assignments if additional work is required. How can I produce evidence to support my lessons? The teaching session must be recorded using a smartphone, tablet, or other camera device and uploaded to your web portal in order to be graded. You will also be required to provide teaching records and documentation such as lesson plans, learner evaluation, scheme of work, etc proving you've taught in a classroom. How much time will it take to complete this course? For the supervised study, 48 hours are allotted. However, the total amount of time we anticipate you to spend on this award, including all the research and writing tasks is 120 hours. These hours can be distributed in any way you desire as you have the choice to complete the course whenever it's convenient for you over the course of a year. Will this course qualify me for the Qualified Teacher Status (QTLS)? No, this course will not qualify you for the QTLS. But you can take the Level 5 Diploma in Education and Training which will provide you with the opportunity and skills to apply for QTLS. For how long can I access this course? You have a year to access this course, so you can complete it at your own pace and convenience. Are there any prerequisites required to sign up for this course? There is no prerequisite in terms of prior knowledge for this training. Can I use a smartphone or a tablet to study? With no time limit on completion, our online courses are accessible for life. If there is a safe internet connection, every course is fully accessible from a tablet, phone, or laptop. What will I receive once the course is over? You will be able to order the NCFE-recognised Level 3 Award in Education and Training, which is governed by Ofqual, after completing the assignments and the teaching assessment. What is the difference between Level 3, Level 4 and Level 5 Education and training? Education and training levels differ in the depth of knowledge and skills acquired. Level 3 is foundational, Level 4 is subject-specific, and Level 5 is for higher expertise. Completing Level 5 can lead to Qualified Teacher Learning and Skills status. To be able to teach Level 2, one must undertake Level 3; for both Level 2 and 3, Level 4 must be completed; and for all three levels, one must take Level 5. What is the difference between QTS and QTLS? QTS is for teaching up to age 16, while QTLS covers beyond that. QTS is necessary for teaching young students, while QTLS is ideal for teaching at a higher education level. QTS requires an intensive course with work practice, while QTLS requires sector competence and 100 hours of teaching experience.

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