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Time Management 1 Day Training in Leeds

Recognised Accreditation This course is accredited by continuing professional development (CPD). CPD UK is globally recognised by employers, professional organisations, and academic institutions, thus a certificate from CPD Certification Service creates value towards your professional goal and achievement. The Quality Licence Scheme is a brand of the Skills and Education Group, a leading national awarding organisation for providing high-quality vocational qualifications across a wide range of industries. What is CPD? Employers, professional organisations, and academic institutions all recognise CPD, therefore a credential from CPD Certification Service adds value to your professional goals and achievements. Benefits of CPD Improve your employment prospects Boost your job satisfaction Promotes career advancement Enhances your CV Provides you with a competitive edge in the job market Demonstrate your dedication Showcases your professional capabilities What is IPHM? The IPHM is an Accreditation Board that provides Training Providers with international and global accreditation. The Practitioners of Holistic Medicine (IPHM) accreditation is a guarantee of quality and skill. Benefits of IPHM It will help you establish a positive reputation in your chosen field You can join a network and community of successful therapists that are dedicated to providing excellent care to their client You can flaunt this accreditation in your CV It is a worldwide recognised accreditation What is Quality Licence Scheme? This course is endorsed by the Quality Licence Scheme for its high-quality, non-regulated provision and training programmes. The Quality Licence Scheme is a brand of the Skills and Education Group, a leading national awarding organisation for providing high-quality vocational qualifications across a wide range of industries. Benefits of Quality License Scheme Certificate is valuable Provides a competitive edge in your career It will make your CV stand out Course Curriculum Welcome to the course Introduction 00:02:00 Setting up R Studio and R crash course Installing R and R studio 00:05:00 Basics of R and R studio 00:10:00 Packages in R 00:10:00 Inputting data part 1: Inbuilt datasets of R 00:04:00 Inputting data part 2: Manual data entry 00:03:00 Inputting data part 3: Importing from CSV or Text files 00:06:00 Creating Barplots in R 00:13:00 Creating Histograms in R 00:06:00 Basics of Statistics Types of Data 00:04:00 Types of Statistics 00:02:00 Describing the data graphically 00:11:00 Measures of Centers 00:07:00 Measures of Dispersion 00:04:00 Introduction to Machine Learning Introduction to Machine Learning 00:16:00 Building a Machine Learning Model 00:08:00 Data Preprocessing for Regression Analysis Gathering Business Knowledge 00:03:00 Data Exploration 00:03:00 The Data and the Data Dictionary 00:07:00 Importing the dataset into R 00:03:00 Univariate Analysis and EDD 00:03:00 EDD in R 00:12:00 Outlier Treatment 00:04:00 Outlier Treatment in R 00:04:00 Missing Value imputation 00:03:00 Missing Value imputation in R 00:03:00 Seasonality in Data 00:03:00 Bi-variate Analysis and Variable Transformation 00:16:00 Variable transformation in R 00:09:00 Non Usable Variables 00:04:00 Dummy variable creation: Handling qualitative data 00:04:00 Dummy variable creation in R 00:05:00 Correlation Matrix and cause-effect relationship 00:10:00 Correlation Matrix in R 00:08:00 Linear Regression Model The problem statement 00:01:00 Basic equations and Ordinary Least Squared (OLS) method 00:08:00 Assessing Accuracy of predicted coefficients 00:14:00 Assessing Model Accuracy - RSE and R squared 00:07:00 Simple Linear Regression in R 00:07:00 Multiple Linear Regression 00:05:00 The F - statistic 00:08:00 Interpreting result for categorical Variable 00:05:00 Multiple Linear Regression in R 00:07:00 Test-Train split 00:09:00 Bias Variance trade-off 00:06:00 Test-Train Split in R 00:08:00 Regression models other than OLS Linear models other than OLS 00:04:00 Subset Selection techniques 00:11:00 Subset selection in R 00:07:00 Shrinkage methods - Ridge Regression and The Lasso 00:07:00 Ridge regression and Lasso in R 00:12:00 Classification Models: Data Preparation The Data and the Data Dictionary 00:08:00 Importing the dataset into R 00:03:00 EDD in R 00:11:00 Outlier Treatment in R 00:04:00 Missing Value imputation in R 00:03:00 Variable transformation in R 00:06:00 Dummy variable creation in R 00:05:00 The Three classification models Three Classifiers and the problem statement 00:03:00 Why can't we use Linear Regression? 00:04:00 Logistic Regression Logistic Regression 00:08:00 Training a Simple Logistic model in R 00:03:00 Results of Simple Logistic Regression 00:05:00 Logistic with multiple predictors 00:02:00 Training multiple predictor Logistic model in R 00:01:00 Confusion Matrix 00:03:00 Evaluating Model performance 00:07:00 Predicting probabilities, assigning classes and making Confusion Matrix in R 00:06:00 Linear Discriminant Analysis Linear Discriminant Analysis 00:09:00 Linear Discriminant Analysis in R 00:09:00 K-Nearest Neighbors Test-Train Split 00:09:00 Test-Train Split in R 00:08:00 K-Nearest Neighbors classifier 00:08:00 K-Nearest Neighbors in R 00:08:00 Comparing results from 3 models Understanding the results of classification models 00:06:00 Summary of the three models 00:04:00 Simple Decision Trees Basics of Decision Trees 00:10:00 Understanding a Regression Tree 00:10:00 The stopping criteria for controlling tree growth 00:03:00 The Data set for this part 00:03:00 Importing the Data set into R 00:06:00 Splitting Data into Test and Train Set in R 00:05:00 Building a Regression Tree in R 00:14:00 Pruning a tree 00:04:00 Pruning a Tree in R 00:09:00 Simple Classification Tree Classification Trees 00:06:00 The Data set for Classification problem 00:01:00 Building a classification Tree in R 00:09:00 Advantages and Disadvantages of Decision Trees 00:01:00 Ensemble technique 1 - Bagging Bagging 00:06:00 Bagging in R 00:06:00 Ensemble technique 2 - Random Forest Random Forest technique 00:04:00 Random Forest in R 00:04:00 Ensemble technique 3 - GBM, AdaBoost and XGBoost Boosting techniques 00:07:00 Gradient Boosting in R 00:07:00 AdaBoosting in R 00:09:00 XGBoosting in R 00:16:00 Maximum Margin Classifier Content flow 00:01:00 The Concept of a Hyperplane 00:05:00 Maximum Margin Classifier 00:03:00 Limitations of Maximum Margin Classifier 00:02:00 Support Vector Classifier Support Vector classifiers 00:10:00 Limitations of Support Vector Classifiers 00:01:00 Support Vector Machines Kernel Based Support Vector Machines 00:06:00 Creating Support Vector Machine Model in R The Data set for the Classification problem 00:01:00 Importing Data into R 00:08:00 Test-Train Split 00:09:00 Classification SVM model using Linear Kernel 00:16:00 Hyperparameter Tuning for Linear Kernel 00:06:00 Polynomial Kernel with Hyperparameter Tuning 00:10:00 Radial Kernel with Hyperparameter Tuning 00:06:00 The Data set for the Regression problem 00:03:00 SVM based Regression Model in R 00:11:00 Assessment Assessment - Machine Learning Masterclass 00:10:00 Certificate of Achievement Certificate of Achievement 00:00:00 Get Your Insurance Now Get Your Insurance Now 00:00:00 Feedback Feedback 00:00:00

Jalupro is used to regenerate the Extra Cellular Matrix (ECM) by stimulating the fibroblasts in the skin to produce a complete mix of collagen and elastin. These products are an excellent option for clients who wish to maintain a more natural look or those who do not want or need Botulinum Toxin or Dermal Fillers. Additional course details Course Prerequisites Be a medical professional registered to a medical body (NMC, GMC, GDC, GPhC, etc.) Have Level 3 NVQ in Beauty Therapy Previous Dermal Filler or Botox training Have six months of experience in SPMU, Microblading, and Microneedling) and six months of Anatomy & Physiology Level 3 Have 12 months of experience in advanced beauty treatments (e.g. SPMU, Microblading, Microneedling) Course Contents Health & Safety Infection Control Sharps disposal Facial Skin Anatomy Skin Ageing Jalupro classic, HMW, Super Hydro Injection Techniques Jalupro Procedure - Video Demonstration Contra action Contraindications Aftercare This course was designed for learners to refresh their subject knowledge and practical skill; with prior skin booster injection experience, we suggest you attend our onsite training course for learners without previous training. Course Features CPD Accredited CourseVetted accredited trainingFully Online TrainingTrain your way on any deviceFull DemonstrationComplete end to end treatment demonstrationImmediate CertificationDelivered immediately after completion Frequently Asked Questions How long do I have to complete the training course? Once you have logged in and started your training course you will have 3 months to complete your training. Can I train straight away after making payment? Yes. Once you have completed payment our system will automatically enrol you onto the training course. You will then receive an email with instructions and a direct link to login and start your course. Can I get insurance once I have completed this training? Our online training courses are CPD accredited. Acquiring insurance based on completion and accreditation from our online training courses is insurer specific and as with most cases also takes into account your personal background and status. We advise that you contact your insurance to ensure your prerequisites meets their requirements and that this training course meets their specific criteria for insurance. We have a relationship with Insync Insurance which we recommend. Is this course accredited? Yes. This training course is accredited by the CPD group. Where is the Cosmetic College The Cosmetic College is located at: 3 Locks Court, 429 Crofton Road, Orpington, BR6 8NL Do you offer in-person training? Yes, we perform in-person training at our training academy in Orpington, Kent. You can find out more details of our in-person Microneedling training here

Overview Uplift Your Career & Skill Up to Your Dream Job - Learning Simplified From Home! Kickstart your career & boost your employability by helping you discover your skills, talents, and interests with our special Advanced Mathematics Training Course. You'll create a pathway to your ideal job as this course is designed to uplift your career in the relevant industry. It provides the professional training that employers are looking for in today's workplaces. The Advanced Mathematics Training Course is one of the most prestigious training offered at Skillwise and is highly valued by employers for good reason. This Advanced Mathematics Training Course has been designed by industry experts to provide our learners with the best learning experience possible to increase their understanding of their chosen field. This Advanced Mathematics Training Course, like every one of Skillwise's courses, is meticulously developed and well-researched. Every one of the topics is divided into elementary modules, allowing our students to grasp each lesson quickly. At Skillwise, we don't just offer courses; we also provide a valuable teaching process. When you buy a course from Skillwise, you get unlimited Lifetime access with 24/7 dedicated tutor support. Why buy this Advanced Mathematics Training ? Lifetime access to the course forever Digital Certificate, Transcript, and student ID are all included in the price Absolutely no hidden fees Directly receive CPD Quality Standard-accredited qualifications after course completion Receive one-to-one assistance 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 Advanced Mathematics Training 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 free. Original Hard Copy certificates need to be ordered at an additional cost of £8. Who is this course for? This Advanced Mathematics Training course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already work in relevant fields and want to polish their knowledge and skills. Prerequisites This Advanced Mathematics Training does not require you to have any prior qualifications or experience. You can just enrol and start learning. This Advanced Mathematics Training was made by professionals and it is compatible with all PCs, Macs, 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 a bonus, you will be able to pursue multiple occupations. This Advanced Mathematics Training is a great way for you to gain multiple skills from the comfort of your home. Introduction Introduction 00:01:00 Mathematical Logic Introduction to Mathematical Logic, What is Sentence,Statements and their Types 00:02:00 Intro to Logical Connectivity,Tautology,Contradiction,Contingency,Pattern 00:06:00 Quantitative and Quantified Statement and types and example 00:03:00 Dual : Replacing of Connections and Symbols 00:02:00 Negations of Compound Statement , Converse, Inverse , & Contrapositive 00:03:00 Algebra of Statements and Law 00:05:00 Real Life application of Logic to Switching Electric Circuit 00:05:00 Matrices Intro to Matrices , Multiplication and Addition using Matrix 00:06:00 Inverse of Matrix Uniqueness of Inverse,Elementary Transformation 00:08:00 Method of REDUCTION AND INVERSION with real life example how we can implement 00:17:00 Trigonometric Functions Introduction to Trigonometric Function 00:03:00 General Solutions And Theorem 00:10:00 Solution of Triangle : Polar Co-ordinates 00:21:00 Rules and Theorems of SIn Cosine and TAN 00:22:00 Inverse Trigonometric Function 00:25:00 Pair Of Straight Line Introduction & Combined Equations 00:07:00 Degrees and Types 00:12:00 Some Theorem 00:17:00 Lines & Planes Introduction - vector cartesian theorem 00:02:00 Cartesian Equation & 2 Point Theorem 00:03:00 Theorems & Problem Solving 00:05:00 Distance of Point Line 00:05:00 Skew Lines 00:01:00 Distance of skew lines 00:03:00 Distance between parallel lines 00:02:00 Equation of Plane and Cartesian Form 00:10:00 Linear Programming Linear Programming Introduction 00:08:00 Introduction to LPP (Linear Programming Problem) 00:05:00 LPP PROBLEM SOLVING 00:07:00 Order Your Certificate Order Your Certificate QLS

Delve into the world of data analysis with 'R Programming for Data Science,' a course designed to guide learners through the intricacies of R, a premier programming language in the data science domain. The course opens with a broad perspective on data science, illuminating the pivotal role of R in this field. Learners are then introduced to R and RStudio, equipping them with the foundational tools and interfaces essential for R programming. The curriculum progresses with an introduction to the basics of R, ensuring learners grasp the core principles that underpin more complex operations. A highlight of this course is its in-depth exploration of R's versatile data structures, including vectors, matrices, factors, and data frames. Each unit is crafted to provide learners with a comprehensive understanding of these structures, pivotal for effective data handling and manipulation. The course also emphasizes the importance of relational and logical operators in R, key elements for executing data operations. As the course advances, learners will engage with the nuances of conditional statements and loops, essential for writing efficient and dynamic R scripts. Moving into more advanced territories, the course delves into the creation and usage of functions, an integral part of R programming, and the exploration of various R packages that extend the language's capabilities. Learners will also gain expertise in the 'apply' family of functions, crucial for streamlined data processing. Further units cover regular expressions and effective strategies for managing dates and times in data sets. The course concludes with practical applications in data acquisition, cleaning, visualization, and manipulation, ensuring learners are well-prepared to tackle real-world data science challenges using R. Learning Outcomes Develop a foundational understanding of R's role in data science and proficiency in RStudio. Gain fluency in R programming basics, enabling the handling of complex data tasks. Acquire skills in managing various R data structures for efficient data analysis. Master relational and logical operations for advanced data manipulation in R. Learn to create functions and utilize R packages for expanded analytical capabilities. Why choose this R Programming for Data Science course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Unlock career resources for CV improvement, interview readiness, and job success. Who is this R Programming for Data Science course for? Beginners in data science eager to learn R programming. Data analysts and scientists looking to enhance their skills in R. Researchers in various fields requiring advanced data analysis tools. Statisticians seeking to adopt R for more sophisticated data manipulations. Professionals in finance, healthcare, and other sectors needing data-driven insights. Career path Data Scientist (R Expertise): £30,000 - £70,000 Data Analyst (R Programming Skills): £27,000 - £55,000 Bioinformatics Scientist (R Proficiency): £35,000 - £60,000 Quantitative Analyst (R Knowledge): £40,000 - £80,000 Research Analyst (R Usage): £25,000 - £50,000 Business Intelligence Developer (R Familiarity): £32,000 - £65,000 Prerequisites This R Programming for Data Science does not require you to have any prior qualifications or experience. You can just enrol and start learning.This R Programming for Data Science 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. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Unit 01: Data Science Overview Introduction to Data Science 00:01:00 Data Science: Career of the Future 00:04:00 What is Data Science? 00:02:00 Data Science as a Process 00:02:00 Data Science Toolbox 00:03:00 Data Science Process Explained 00:05:00 What's next? 00:02:00 Unit 02: R and RStudio Engine and coding environment 00:03:00 Installing R and RStudio 00:04:00 RStudio: A quick tour 00:04:00 Unit 03: Introduction to Basics Arithmetic with R 00:03:00 Variable assignment 00:04:00 Basic data types in R 00:03:00 Unit 04: Vectors Creating a vector 00:05:00 Naming a vector 00:04:00 Arithmetic calculations on vectors 00:07:00 Vector selection 00:06:00 Selection by comparison 00:04:00 Unit 05: Matrices What's a Matrix? 00:02:00 Analyzing Matrices 00:03:00 Naming a Matrix 00:05:00 Adding columns and rows to a matrix 00:06:00 Selection of matrix elements 00:03:00 Arithmetic with matrices 00:07:00 Additional Materials 00:00:00 Unit 06: Factors What's a Factor? 00:02:00 Categorical Variables and Factor Levels 00:04:00 Summarizing a Factor 00:01:00 Ordered Factors 00:05:00 Unit 07: Data Frames What's a Data Frame? 00:03:00 Creating Data Frames 00:20:00 Selection of Data Frame elements 00:03:00 Conditional selection 00:03:00 Sorting a Data Frame 00:03:00 Additional Materials 00:00:00 Unit 08: Lists Why would you need lists? 00:01:00 Creating a List 00:06:00 Selecting elements from a list 00:03:00 Adding more data to the list 00:02:00 Additional Materials 00:00:00 Unit 09: Relational Operators Equality 00:03:00 Greater and Less Than 00:03:00 Compare Vectors 00:03:00 Compare Matrices 00:02:00 Additional Materials 00:00:00 Unit 10: Logical Operators AND, OR, NOT Operators 00:04:00 Logical operators with vectors and matrices 00:04:00 Reverse the result: (!) 00:01:00 Relational and Logical Operators together 00:06:00 Additional Materials 00:00:00 Unit 11: Conditional Statements The IF statement 00:04:00 IFELSE 00:03:00 The ELSEIF statement 00:05:00 Full Exercise 00:03:00 Additional Materials 00:00:00 Unit 12: Loops Write a While loop 00:04:00 Looping with more conditions 00:04:00 Break: stop the While Loop 00:04:00 What's a For loop? 00:02:00 Loop over a vector 00:02:00 Loop over a list 00:03:00 Loop over a matrix 00:04:00 For loop with conditionals 00:01:00 Using Next and Break with For loop 00:03:00 Additional Materials 00:00:00 Unit 13: Functions What is a Function? 00:02:00 Arguments matching 00:03:00 Required and Optional Arguments 00:03:00 Nested functions 00:02:00 Writing own functions 00:03:00 Functions with no arguments 00:02:00 Defining default arguments in functions 00:04:00 Function scoping 00:02:00 Control flow in functions 00:03:00 Additional Materials 00:00:00 Unit 14: R Packages Installing R Packages 00:01:00 Loading R Packages 00:04:00 Different ways to load a package 00:02:00 Additional Materials 00:00:00 Unit 15: The Apply Family - lapply What is lapply and when is used? 00:04:00 Use lapply with user-defined functions 00:03:00 lapply and anonymous functions 00:01:00 Use lapply with additional arguments 00:04:00 Additional Materials 00:00:00 Unit 16: The apply Family - sapply & vapply What is sapply? 00:02:00 How to use sapply 00:02:00 sapply with your own function 00:02:00 sapply with a function returning a vector 00:02:00 When can't sapply simplify? 00:02:00 What is vapply and why is it used? 00:04:00 Additional Materials 00:00:00 Unit 17: Useful Functions Mathematical functions 00:05:00 Data Utilities 00:08:00 Additional Materials 00:00:00 Unit 18: Regular Expressions grepl & grep 00:04:00 Metacharacters 00:05:00 sub & gsub 00:02:00 More metacharacters 00:04:00 Additional Materials 00:00:00 Unit 19: Dates and Times Today and Now 00:02:00 Create and format dates 00:06:00 Create and format times 00:03:00 Calculations with Dates 00:03:00 Calculations with Times 00:07:00 Additional Materials 00:00:00 Unit 20: Getting and Cleaning Data Get and set current directory 00:04:00 Get data from the web 00:04:00 Loading flat files 00:03:00 Loading Excel files 00:05:00 Additional Materials 00:00:00 Unit 21: Plotting Data in R Base plotting system 00:03:00 Base plots: Histograms 00:03:00 Base plots: Scatterplots 00:05:00 Base plots: Regression Line 00:03:00 Base plots: Boxplot 00:03:00 Unit 22: Data Manipulation with dplyr Introduction to dplyr package 00:04:00 Using the pipe operator (%>%) 00:02:00 Columns component: select() 00:05:00 Columns component: rename() and rename_with() 00:02:00 Columns component: mutate() 00:02:00 Columns component: relocate() 00:02:00 Rows component: filter() 00:01:00 Rows component: slice() 00:04:00 Rows component: arrange() 00:01:00 Rows component: rowwise() 00:02:00 Grouping of rows: summarise() 00:03:00 Grouping of rows: across() 00:02:00 COVID-19 Analysis Task 00:08:00 Additional Materials 00:00:00 Assignment Assignment - R Programming for Data Science 00:00:00

Duration 4 Days 24 CPD hours This course is intended for This course is for people who have on the job experience doing project management activities and running projects, regardless of their job title. It is for those who wish to become certified project managers, or those that want to build or reinforce a foundation in project management. This course is ideal for a person who wants to grow and formalize their project management skills on an industry neutral, global standard, the Project Management Institute. Overview After completing this course, students will be able to: Demonstrate an understanding of the various project life cycles and processes. Distinguish between predictive and adaptive approaches. Demonstrate an understanding of project management planning. Demonstrate an understanding of project roles and responsibilities. Explain the importance of the role the project manager plays. Determine how to follow and execute and respond to planned strategies or frameworks (e.g., communication, risks, etc.). Demonstrate an understanding of common problem-solving tools and techniques. Identify the suitability of a predictive, plan-based approach for the organizational structure (e.g., virtual, colocation, matrix structure, hierarchical, etc.). Determine and give examples of the activities within each process. Demonstrate an understanding of a project management plan schedule. Determine how to document project controls of predictive, plan-based projects. Explain when it is appropriate and sustainable to use an adaptive approach for the organizational structure. Compare the pros and cons of adaptive and predictive, plan-based projects. Identify the suitability of adaptive approaches for the organizational structure (e.g., virtual, colocation, matrix structure, hierarchical, etc.). Identify organizational process assets and environmental factors that facilitate the use of adaptive approaches. Determine how to plan project iterations. Determine how to document project controls for an adaptive project. Distinguish between the components of different adaptive methodologies (e.g., Scrum, Extreme Programming (XP), Scaled Adaptive Framework (SAFe), Kanban, etc.). Determine how to prepare and execute task management steps. Demonstrate an understanding of business analysis (BA) roles and responsibilities. Demonstrate the importance of communication for a business analyst between various teams and stakeholders. Determine how to gather requirements and using the best approach for a situation. Explain the application of a product roadmap. Determine how project methodologies influence business analysis processes. Validate requirements through product delivery. Every career in project management has a beginning and that is the purpose of this course. You will learn the fundamentals of project management. This includes project performance, when to use the predictive or adaptive methodologies, business analysis domains, and frameworks, as well as the proper use of one of the various adaptive frameworks. Every career in project management has a beginning and that is the purpose of this course. You will learn the fundamentals of project management. This includes project performance, when to use the predictive or adaptive methodologies, business analysis domains, and frameworks, as well as the proper use of one of the various adaptive frameworks.

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More!

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