22638 Improv courses

Course Overview: Crack the Business Efficiency Code: Diploma in Lean Process and Six Sigma Millions saved, empires built. That's the power of Lean Process and Six Sigma. This isn't just a course; it's your launchpad. Go deeply into Lean's core: grasp its culture, principles, and value vs. waste. Master Value Stream Mapping and unlock the magic of flow and pull. Then, conquer Six Sigma. Master each phase, from defining problems to controlling solutions. Become the cost-saving genius and the efficiency expert. Enrol now. Lead the business transformation. Key Features of the Course: CPD certificate upon successful completion. 24/7 Learning Assistance for continuous guidance. Engaging and contemporary learning materials. Who is This Course For? This diploma in Lean Process and Six Sigma is ideal for aspiring process improvement specialists, business analysts, managers, and those keen to understand the intricacies of Lean and Six Sigma for organisational enhancement. Learning Outcome Gain an insightful understanding of the lean methodology. Recognise the intrinsic values and principles of Lean Culture. Discern the Five Principles of Lean and their application. Distinguish between Value and Waste in processes. Master the art of Value Stream Mapping (VSM). Learn the Principles of Flow and Pull. Acquire a comprehensive overview of Six Sigma. Navigate through the Six Sigma phases from Define to Control with confidence. Why Enrol in This Course: Stay ahead in the dynamic world of business by mastering process improvement skills. This Diploma in Lean Process and Six Sigma is a top-reviewed offering that has recently been updated and aligned with the latest trends. It is your ticket to achieving operational excellence. Requirements: Basic understanding of business processes. Familiarity with management principles is beneficial but optional. Career Path: Lean Process Consultant - Avg. UK salary: £45,000. Six Sigma Specialist - Avg. UK salary: £50,000. Operations Analyst - Avg. UK salary: £42,000. Quality Assurance Manager - Avg. UK salary: £55,000. Process Improvement Manager - Avg. UK salary: £53,000. Business Process Manager - Avg. UK salary: £52,000. Continuous Improvement Director - Avg. UK salary: £70,000. Certification: Learners will be awarded an accredited CPD certificate upon completing the Diploma in Lean Process and Six Sigma course. Course Curriculum 12 sections • 12 lectures • 03:31:00 total length •Understanding Lean: 00:22:00 •The Lean Culture: 00:19:00 •The Five Principles of Lean: 00:10:00 •Value and Waste: 00:19:00 •Value Stream Mapping (VSM): 00:19:00 •The Principles of Flow and Pull: 00:14:00 •Overview of Six Sigma: 00:12:00 •The Define Phase: 00:17:00 •The Measure Phase: 00:21:00 •The Analyse Phase: 00:23:00 •The Improve Phase: 00:21:00 •The Control Phase: 00:14:00

Overview This comprehensive course on Advance Lean Six Sigma Black Belt Course will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Advance Lean Six Sigma Black Belt Course comes with accredited certification, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. 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? There is no experience or previous qualifications required for enrolment on this Advance Lean Six Sigma Black Belt Course. It is available to all students, of all academic backgrounds. Requirements Our Advance Lean Six Sigma Black Belt Course is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 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 these various qualifications 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. Course Curriculum 10 sections • 36 lectures • 11:04:00 total length •Course Overview: 00:08:00 •Introduction: 00:15:00 •Introduction Part 2: 00:17:00 •Define: 00:25:00 •Define Part 2: 00:20:00 •Measure: 00:13:00 •Measure Part 2: 00:28:00 •Measure Part 3: 00:24:00 •Measure Part 4: 00:17:00 •Measure Part 5: 00:17:00 •Measure Part 6: 00:26:00 •Measure Part 7: 00:24:00 •Measure Part 8: 00:10:00 •Analyze: 00:14:00 •Analyze Part 2: 00:17:00 •Analyze Part 3: 00:28:00 •Analyze Part 4: 00:18:00 •Analyze Part 5: 00:35:00 •Analyze Part 6: 00:27:00 •Analyze Part 7: 00:38:00 •Analyze Part 8: 00:42:00 •Analyze Part 9: 00:33:00 •Analyze Part 10: 00:15:00 •Analyze Part 11: 00:15:00 •Analyze Part 12: 00:16:00 •Improve: 00:23:00 •Improve Part 2: 00:12:00 •Improve Part 3: 00:10:00 •Improve Part 4: 00:23:00 •Improve Part 5: 00:15:00 •Control: 00:17:00 •Control Part 2: 00:08:00 •Case Study: 00:10:00 •Conclusion: 00:04:00 •Resources - Advance Lean Six Sigma Black Belt Course: 00:00:00 •Assignment - Advance Lean Six Sigma Black Belt Course: 00:00:00

Our ISO 9001:2015 QMS Clauses Awareness course is a comprehensive program covering all ten clauses of the standard.

The Kettlebell Foundational Workshop is a comprehensive training program designed to teach the fundamentals of kettlebell exercises. This workshop provides participants with an in-depth understanding of proper technique and form, as well as safety guidelines for using kettlebells effectively. Ideal for fitness enthusiasts and personal trainers, this workshop offers valuable insights into the benefits of incorporating kettlebells into any workout routine. Join us today to learn how to master the art of kettlebell training!

Overview This comprehensive course on Data Science & Machine Learning with Python will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Data Science & Machine Learning with Python comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. 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? There is no experience or previous qualifications required for enrolment on this Data Science & Machine Learning with Python. It is available to all students, of all academic backgrounds. Requirements Our Data Science & Machine Learning with Python is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 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 Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 2 sections • 90 lectures • 10:24:00 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: 00:08:00 •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: 00:07:00 •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 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 This comprehensive course on English as a Foreign Language will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This English as a Foreign Language comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. 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? There is no experience or previous qualifications required for enrolment on this English as a Foreign Language. It is available to all students, of all academic backgrounds. Requirements Our English as a Foreign Language is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 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 these various qualifications 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. Course Curriculum 7 sections • 32 lectures • 04:49:00 total length •Introduction: 00:04:00 •How to Improve Vocabulary: 00:20:00 •Phrasal Verbs Lecture: 00:09:00 •Phrasal Verbs: 00:36:00 •Reading Tips: 00:04:00 •Reading Practice Example: 00:11:00 •Reading Practice - Learning Pronunciation: 00:03:00 •How to Improve Listening Skills: 00:14:00 •Novak Djokovic Interview: 00:12:00 •MLK Speech: 00:08:00 •George Foreman Interview: 00:11:00 •Michael Jordan Speech: 00:09:00 •Dolph Lundgren Speech: 00:06:00 •Roger Federer Interview: 00:07:00 •Neil Degrasse Tyson Interview: 00:08:00 •Kobe Bryant Interview: 00:08:00 •Lisa Kudrow Interview: 00:07:00 •Courteney Cox Interview: 00:06:00 •Yvonne Orji Interview: 00:07:00 •David Schwimmer Interview: 00:07:00 •Listening Practice: 00:07:00 •Listening Tips: 00:05:00 •How to speak fluently in English: 00:03:00 •How to improve speaking skills: 00:10:00 •Speaking Practice: 00:07:00 •Speaking Tips: 00:08:00 •How to improve writing skills: 00:10:00 •Writing Practice: 00:05:00 •Writing Tips: 00:04:00 •How to maintain the advanced level: 00:10:00 •Final Lecture: 00:23:00 •Assignment - English as a Foreign Language: 00:00:00

Overview This comprehensive course on Reach Advanced Level in English as a Foreign Language will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Reach Advanced Level in English as a Foreign Language comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. 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? There is no experience or previous qualifications required for enrolment on this Reach Advanced Level in English as a Foreign Language. It is available to all students, of all academic backgrounds. Requirements Our Reach Advanced Level in English as a Foreign Language is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 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 Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 7 sections • 32 lectures • 04:49:00 total length •Introduction: 00:04:00 •How to Improve Vocabulary: 00:20:00 •Phrasal Verbs Lecture: 00:09:00 •Phrasal Verbs: 00:36:00 •Reading Tips: 00:04:00 •Reading Practice Example: 00:11:00 •Reading Practice - Learning Pronunciation: 00:03:00 •How to Improve Listening Skills: 00:14:00 •Novak Djokovic Interview: 00:12:00 •MLK Speech: 00:08:00 •George Foreman Interview: 00:11:00 •Michael Jordan Speech: 00:09:00 •Dolph Lundgren Speech: 00:06:00 •Roger Federer Interview: 00:07:00 •Neil Degrasse Tyson Interview: 00:08:00 •Kobe Bryant Interview: 00:08:00 •Lisa Kudrow Interview: 00:07:00 •Courteney Cox Interview: 00:06:00 •Yvonne Orji Interview: 00:07:00 •David Schwimmer Interview: 00:07:00 •Listening Practice: 00:07:00 •Listening Tips: 00:05:00 •How to speak fluently in English: 00:03:00 •How to improve speaking skills: 00:10:00 •Speaking Practice: 00:07:00 •Speaking Tips: 00:08:00 •How to improve writing skills: 00:10:00 •Writing Practice: 00:05:00 •Writing Tips: 00:04:00 •How to maintain the advanced level: 00:10:00 •Final Lecture: 00:23:00 •Assignment - Reach Advanced Level in English as a Foreign Language: 00:00:00

Course Overview: Discover the Gateway to Success in Business Analysis! Step into the realm of business analysis and unlock a world of possibilities with our comprehensive Business Analysis Level 3 course. Designed to empower you with the knowledge and skills needed to excel in this rapidly evolving field, this course provides a deep dive into the core concepts and techniques that drive successful business analysis practices. This course offers a series of modules that cover a wide range of topics. Starting with an Introduction to Business Analysis, you will delve into the core concepts and principles that underpin this discipline. From there, you will explore essential areas such as Business Processes, Business Analysis Planning and Monitoring, Strategic Analysis and Product Scope, Solution Evaluation, Investigation Techniques, Ratio Analysis, Stakeholder Analysis and Management, Process Improvement with Gap Analysis, Documenting and Managing Requirements, and finally, a glimpse into the Career Prospect as a Business Analyst in the UK. Enrol in the Business Analysis Level 3 course to comprehensively understand these topics and advance your career as a business analyst. Key Features of the Course: CPD Certificate: Upon course completion, participants will receive a CPD certificate, a testament to their commitment to professional development and industry-recognised expertise. 24/7 Learning Assistance: Our dedicated support team is available around the clock to provide guidance, answer questions, and ensure a seamless learning experience for all participants. Who is This Course For? This course is ideal for professionals eager to enhance their business analysis skills and advance their careers. Whether you are an aspiring business analyst, a project manager, a consultant, or a professional seeking to broaden your skill set, this course will equip you with the knowledge and tools necessary to excel in your field. What You Will Learn: Throughout the course, participants will engage with various modules that cover a wide range of essential topics. The curriculum includes an Introduction to Business Analysis, where you will develop an understanding of the fundamental principles and methodologies. You will then delve into Business Processes, Business Analysis Planning and Monitoring, Strategic Analysis and Product Scope, Solution Evaluation, Investigation Techniques, Ratio Analysis, Stakeholder Analysis and Management, Process Improvement with Gap Analysis, and Documenting and Managing Requirements. Finally, you will also gain valuable insights into the career prospects available as a Business Analyst in the UK. Why Enrol in This Course: Enrolling in the Business Analysis Level 3 course offers compelling reasons to learners. This course has received outstanding reviews, with the latest advancements and best practices in business analysis. By completing this course, learners will acquire high-demand essential skills, opening doors to various career opportunities. Requirements: Participants do not require specific prior experience or qualifications to enrol in this course. The course is designed to accommodate learners from diverse backgrounds, making it accessible to anyone interested in business analysis. Career Path: Upon completing the Business Analysis Level 3 course, participants will be equipped with the skills and knowledge for various rewarding business-analysis career paths. Here are seven course-related professions in the UK, along with their respective average salaries: Business Analyst - £45,000 per year Project Manager - £50,000 per year Requirements Analyst -: £40,000 per year Process Improvement Analyst - £42,000 per year Data Analyst - £38,000 per year Systems Analyst -: £45,000 per year Management Consultant - £55,000 per year Certification: Upon completing the course, participants will be awarded a CPD certificate, recognising their achievements and demonstrating their expertise in business analysis. This certification is valuable, boosting their professional credibility and enhancing their career prospects. Course Curriculum 11 sections • 11 lectures • 05:23:00 total length •Introduction to Business Analysis: 00:14:00 •Business Processes: 00:47:00 •Business Analysis Planning and Monitoring: 00:30:00 •Strategic Analysis and Product Scope: 00:28:00 •Solution Evaluation: 00:27:00 •Investigation Techniques: 00:48:00 •Ratio Analysis: 00:29:00 •Stakeholder Analysis and Management: 00:25:00 •Process Improvement with Gap Analysis: 00:28:00 •Documenting and Managing Requirements: 00:31:00 •Career Prospect as a Business Analyst in the UK: 00:16:00

Course Overview: Are you ready to unlock the world of digital possibilities by understanding the art and science of data analytics? We live in an era where data back every decision and every action requires insightful analysis. Clive Humby said, "Data is the new oil, and it's the new oil, so it's an invaluable resource for companies worldwide. This comprehensive course covers a broad spectrum of data analytics, starting with an engaging 'Introduction to the World of Data,' before delving into the fundamental components like the 'Basics of Data Analytics,' 'Statistics for Data Analytics,' and the 'Actions Taken in the Data Analysis Process.' Each subsequent module is carefully designed to guide you through various stages of data analytics. You'll explore 'Data Mining,' work with 'Excel for Data Analytics,' and discover 'Tools for Data Analytics.' The curriculum wraps up with a focus on 'Data-Analytic Thinking' and 'Data Visualisation.' Enrol today and start your journey to becoming a data analytics expert! Key Features of the Course: This course comes with a CPD certificate, affirming your proficiency in data analytics. It offers 24/7 learning assistance to ensure you get the most out of your learning journey. The content is presented in easy-to-understand and engaging learning materials, carefully curated to make your journey in data analytics enlightening. Who is This Course For? This course suits professionals seeking to leverage data analytics in their respective fields, individuals aspiring to venture into the data science arena, and students keen to acquire contemporary skills for the digital age. What You Will Learn: Understand the fundamental concepts of data analytics. Apply statistical techniques to analyse large data sets. Implement effective strategies for data collection and storage. Master the art of data mining and extraction of valuable insights. Utilise Excel and other tools effectively for data analysis. Develop a data-analytic mindset for problem-solving. Translate data insights into compelling visualisations. Why Enrol in This Course: This course will open doors to many opportunities. You will learn from top-notch professionals, utilise quality learning materials and have access to trending and recently updated curriculum. Requirements: A basic understanding of computers A willingness to learn Career Path: The expertise gained from this Data Analytics course can pave your way into a variety of professions, such as: Data Analyst (£30,000-£60,000) Business Analyst (£35,000-£70,000) Market Research Analyst (£27,000-£55,000) Operations Analyst (£31,000-£62,000) Quantitative Analyst (£45,000-£85,000) Data Scientist (£50,000-£90,000) Data Engineer (£35,000-£75,000) Certification: On successful completion of this course, you will receive a CPD certificate, testifying your mastery in the field of data analytics. With this recognition, you can confidently showcase your skills and expertise in the professional world. Data analytics is a powerful tool that can be used to make better decisions, improve efficiency, and drive innovation. If you want to join this growing field, this course is for you! FAQ What do you mean by data analytics? Data analytics is the process of collecting, cleaning, analyzing, and interpreting data to extract insights. What data analytics actually do? Data analytics helps businesses make better decisions by providing them with insights into their data. This can include insights into customer behavior, market trends, and product performance. What are the 5 data analytics? The 5 data analytics are: Descriptive analytics: This type of analytics describes what has happened in the past. Diagnostic analytics: This type of analytics identifies the root cause of problems. Predictive analytics: This type of analytics predicts what will happen in the future. Prescriptive analytics: This type of analytics recommends actions to take based on the predictions. Visual analytics: This type of analytics uses visual representations of data to make it easier to understand. What is data analytics examples? Here are some examples of data analytics: A retailer uses data analytics to track customer behaviour and identify trends. A bank uses data analytics to predict which customers are likely to default on their loans. A healthcare provider uses data analytics to identify patients who are at risk for certain diseases. What are 4 types of analytics? The 4 types of analytics are: Descriptive analytics: This type of analytics describes what has happened in the past. Diagnostic analytics: This type of analytics identifies the root cause of problems. Predictive analytics: This type of analytics predicts what will happen in the future. Prescriptive analytics: This type of analytics recommends actions to take based on the predictions. Why do we use data analytics? We use data analytics to make better decisions, improve efficiency, and drive innovation. Here are some of the benefits of using data analytics: Better decision-making: Data analytics can help businesses make better decisions by providing them with insights into their data. Improved efficiency: Data analytics can help businesses improve efficiency by identifying areas where they can save time and money. Driven innovation: Data analytics can help businesses drive innovation by identifying new opportunities and trends. Course Curriculum 13 sections • 13 lectures • 12:25:00 total length •Introduction to the World of Data: 01:00:00 •Basics of Data Analytics: 00:40:00 •Statistics for Data Analytics: 01:00:00 •Actions Taken in the Data Analysis Process: 00:55:00 •Gathering the Right Information: 01:00:00 •Storing Data: 01:15:00 •Data Mining: 01:00:00 •Excel for Data Analytics: 01:20:00 •Tools for Data Analytics: 01:20:00 •Data-Analytic Thinking: 01:10:00 •Data Visualisation That Clearly Describes Insights: 00:45:00 •Data Visualization Tools: 01:00:00 •Assignment - Data Analytics: 00:00:00