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Duration 5 Days 30 CPD hours This course is intended for In order to be successful in this course you should have incoming hands-on experience with another programming language. This course is not for non-developers or new developers. Possible roles that may attend this course include: Software Developers: Professionals who have been working with other programming languages and want to expand their skillset by learning Java and its object-oriented features. Web Developers: Those who work on web applications and want to enhance their back-end development capabilities with Java. Mobile App Developers: Developers who wish to enter the world of Android app development, where Java is a widely used language for creating mobile applications. Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, designed to train attendees in core OO coding and Java development skills, coupling the most current, effective techniques with the soundest industry practices. Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Working in a hands-on learning environment, guided by our expert team, attendees will learn to: Understand what OO programming is and what the advantages of OO are in today's world Work with objects, classes, and OO implementations Understand the basic concepts of OO such as encapsulation, inheritance, polymorphism, and abstraction Understand not only the fundamentals of the Java language, but also its importance, uses, strengths and weaknesses Understand the basics of the Java language and how?it relates to OO programming and the Object Model Learn to use Java exception handling Understand and use classes, inheritance and polymorphism Understand and use collections, generics, autoboxing, and enumerations Become familiar with the concept of functional programming using Lambda Expressions Process large amounts of data using the Stream API introduced in Java 8 Discover the new Date/Time API Use the JDBC API for database access Work with annotations Take advantage of the Java tooling that is available with the programming environment being used in the class Java 8 Features: Lambda Expressions, Method and Constructor references, The Streams API, Collectors, The Optional class Geared for experienced developers, Basic Java Programming for Developers New to OO, this hands-on, workshop-style course will provide you with an immersive learning experience that will expand your skillset and open doors to new opportunities within the ever-growing technology landscape. Mastering Java and its powerful capabilities will provide you with the competitive edge you need to stand out in today's fast-paced development world. Working in a hands-on learning environment led by our expert coach, you?ll thoroughly explore the foundations of the Java platform, essential programming concepts, and advanced topics, ensuring you acquire a strong understanding of the language and its ecosystem. The object-oriented programming principles taught in this course promote code reusability and maintainability, enabling you to streamline development processes and reduce long-term costs. As you progress through the course, you will also gain familiarity with using an IDE, enhancing your development workflow and collaboration with other Java developers, enabling you to integrate seamlessly into new projects and teams. You?ll also gain practical experience in applying the concepts and techniques learned, solidifying your newly acquired skills and facilitating their direct application in real-world scenarios. You?ll exit this course empowered to create robust, scalable, and efficient Java-based applications that drive innovation and growth for your organization The Java Platform The Java Platform Lifecycle of a Java Program Responsibilities of JVM Documentation and Code Reuse Using the JDK Explain the JDK?s file structure Use the command line compiler to compile a Java class Use the command line Java interpreter to run a Java application class The IntelliJ Paradigm Introduce the IntelliJ IDE The Basics of the IntelliJ interface IntelliJ Projects and Modules Creating and running Java applications Writing a Simple Class Write a Java class that does not explicitly extend another class Define instance variables for a Java class Create object instances Primitives vs Object References Implement a main method to create an instance of the defined class Adding Methods to the Class Write a class with accessor methods to read and write instance variables Write a constructor to initialize an instance with data Write a constructor that calls other constructors of the class to benefit from code reuse Use the this keyword to distinguish local variables from instance variables Object-Oriented Programming Real-World Objects Classes and Objects Object Behavior Methods and Messages Inheritance, Abstraction, and Polymorphism Encapsulation Inheritance Method Overriding Polymorphism Essential Java Programming Essential Java Programming Language Statements Arithmetic operators Operators to increment and decrement numbers Comparison operators Logical operators Return type of comparison and logical operators Use for loops Switch Expressions Switch Expressions and yield Using Strings and Text Blocks Create an instance of the String class Test if two strings are equal Get the length of a string Parse a string for its token components Perform a case-insensitive equality test Build up a string using StringBuffer Contrast String, StringBuffer,and StringBuilder Compact Strings Text Blocks Specializing in a Subclass Constructing a class that extends another class Implementing equals and toString Writing constructors that pass initialization data to parent constructor Using instanceof to verify type of an object reference Pattern matching for instanceof Overriding subclass methods Safely casting references to a more refined type Fields and Variables Discuss Block Scoping Rules Distinguish between instance variables and method variables within a method Explain the difference between the terms field and variable List the default values for instance variables Final and Static fields and methods Local Variable type inference Using Arrays Declaring an array reference Allocating an array Initializing the entries in an array Writing methods with a variable number of arguments Records Data Objects in Java Introduce records as carrier of immutable data Defining records Java Packages and Visibility Use the package keyword to define a class within a specific package Discuss levels of accessibility/visibility Using the import keyword to declare references to classes in a specific package Using the standard type naming conventions Visibility in the Java Modular System Correctly executing a Java application class The Java modular system Defining Modules Inheritance and Polymorphism Write a subclass with a method that overrides a method in the superclass Group objects by their common supertype Utilize polymorphism Cast a supertype reference to a valid subtype reference Use the final keyword on methods and classes to prevent overriding Interfaces and Abstract Classes Define supertype contracts using abstract classes Implement concrete classes based on abstract classes Define supertype contracts using interfaces Implement concrete classes based on interfaces Explain advantage of interfaces over abstract classes Explain advantage of abstract classes over interfaces Static, default and private methods in interfaces Sealed classes Introduce Sealed classes The sealed and permits modifiers Sealed Interfaces Exception Handling Introduction to Exception Handling Introduce the Exception architecture Defining a try/catch blocks Checked vs Unchecked exceptions Exceptions Defining your own application exceptions Automatic closure of resources Suppressed exceptions Handling multiple exceptions in one catch Helpful Nullpointers Enhanced try-with-resources Java Developer's Toolbox Developing applications Introduce the wrapper classes Explain Autoboxing and Unboxing Converting String representations of primitive numbers into their primitive types Defining Enumerations Using static imports Deprecating methods Advanced Java Programming Introduction to Generics Generics and Subtyping Bounded Wildcards Generic Methods Legacy Calls To Generics When Generics Should Be Used Lambda Expressions and Functional Interface Understanding the concept of functional programming Writing lambda expressions Understanding functional interfaces Collections Provide an overview of the Collection API Review the different collection implementations (Set, List and Queue) Explore how generics are used with collections Examine iterators for working with collections Using Collections Collection Sorting Comparators Using the Right Collection Lambda expressions in Collections Bonus Topics: Time Permitting Streams Understanding the problem with collections in Java Thinking of program solutions in a declarative way Use the Stream API to process collections of data Understand the difference between intermediate and terminal stream operations Filtering elements from a Stream Finding element(s) within a Stream Collecting the elements from a Stream into a List takeWhile and dropWhile intermediate operations Collectors Using different ways to collect the items from a Stream Grouping elements within a stream Gathering statistics about numeric property of elements in a stream
The 'Complete Python Machine Learning & Data Science Fundamentals' course covers the foundational concepts of machine learning, data science, and Python programming. It includes hands-on exercises, data visualization, algorithm evaluation techniques, feature selection, and performance improvement using ensembles and parameter tuning. Learning Outcomes: Understand the fundamental concepts and types of machine learning, data science, and Python programming. Learn to prepare the system and environment for data analysis and machine learning tasks. Master the basics of Python, NumPy, Matplotlib, and Pandas for data manipulation and visualization. Gain insights into dataset summary statistics, data visualization techniques, and data preprocessing. Explore feature selection methods and evaluation metrics for classification and regression algorithms. Compare and select the best machine learning model using pipelines and ensembles. Learn to export, save, load machine learning models, and finalize the chosen models for real-time predictions. Why buy this Complete Python Machine Learning & Data Science Fundamentals? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Complete Python Machine Learning & Data Science Fundamentals there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This Complete Python Machine Learning & Data Science Fundamentals course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill. Prerequisites This Complete Python Machine Learning & Data Science Fundamentals does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Complete Python Machine Learning & Data Science Fundamentals was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Complete Python Machine Learning & Data Science Fundamentals is a great way for you to gain multiple skills from the comfort of your home. Course Curriculum Course Overview & Table of Contents Course Overview & Table of Contents 00:09:00 Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types 00:05:00 Introduction to Machine Learning - Part 2 - Classifications and Applications Introduction to Machine Learning - Part 2 - Classifications and Applications 00:06:00 System and Environment preparation - Part 1 System and Environment preparation - Part 1 00:08:00 System and Environment preparation - Part 2 System and Environment preparation - Part 2 00:06:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 1 00:10:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 2 00:09:00 Learn Basics of python - Functions Learn Basics of python - Functions 00:04:00 Learn Basics of python - Data Structures Learn Basics of python - Data Structures 00:12:00 Learn Basics of NumPy - NumPy Array Learn Basics of NumPy - NumPy Array 00:06:00 Learn Basics of NumPy - NumPy Data Learn Basics of NumPy - NumPy Data 00:08:00 Learn Basics of NumPy - NumPy Arithmetic Learn Basics of NumPy - NumPy Arithmetic 00:04:00 Learn Basics of Matplotlib Learn Basics of Matplotlib 00:07:00 Learn Basics of Pandas - Part 1 Learn Basics of Pandas - Part 1 00:06:00 Learn Basics of Pandas - Part 2 Learn Basics of Pandas - Part 2 00:07:00 Understanding the CSV data file Understanding the CSV data file 00:09:00 Load and Read CSV data file using Python Standard Library Understanding the CSV data file 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using Pandas Load and Read CSV data file using Pandas 00:05:00 Dataset Summary - Peek, Dimensions and Data Types Dataset Summary - Peek, Dimensions and Data Types 00:09:00 Dataset Summary - Class Distribution and Data Summary Dataset Summary - Class Distribution and Data Summary 00:09:00 Dataset Summary - Explaining Correlation Dataset Summary - Explaining Correlation 00:11:00 Dataset Summary - Explaining Skewness - Gaussian and Normal Curve Dataset Summary - Explaining Skewness - Gaussian and Normal Curve 00:07:00 Dataset Visualization - Using Histograms Dataset Visualization - Using Histograms 00:07:00 Dataset Visualization - Using Density Plots Dataset Visualization - Using Density Plots 00:06:00 Dataset Visualization - Box and Whisker Plots Dataset Visualization - Box and Whisker Plots 00:05:00 Multivariate Dataset Visualization - Correlation Plots Multivariate Dataset Visualization - Correlation Plots 00:08:00 Multivariate Dataset Visualization - Scatter Plots Multivariate Dataset Visualization - Scatter Plots 00:05:00 Data Preparation (Pre-Processing) - Introduction Data Preparation (Pre-Processing) - Introduction 00:09:00 Data Preparation - Re-scaling Data - Part 1 Data Preparation - Re-scaling Data - Part 1 00:09:00 Data Preparation - Re-scaling Data - Part 2 Data Preparation - Re-scaling Data - Part 2 00:09:00 Data Preparation - Standardizing Data - Part 1 Data Preparation - Standardizing Data - Part 1 00:07:00 Data Preparation - Standardizing Data - Part 2 Data Preparation - Standardizing Data - Part 2 00:04:00 Data Preparation - Normalizing Data Data Preparation - Normalizing Data 00:08:00 Data Preparation - Binarizing Data Data Preparation - Binarizing Data 00:06:00 Feature Selection - Introduction Feature Selection - Introduction 00:07:00 Feature Selection - Uni-variate Part 1 - Chi-Squared Test Feature Selection - Uni-variate Part 1 - Chi-Squared Test 00:09:00 Feature Selection - Uni-variate Part 2 - Chi-Squared Test Feature Selection - Uni-variate Part 2 - Chi-Squared Test 00:10:00 Feature Selection - Recursive Feature Elimination Feature Selection - Recursive Feature Elimination 00:11:00 Feature Selection - Principal Component Analysis (PCA) Feature Selection - Principal Component Analysis (PCA) 00:09:00 Feature Selection - Feature Importance Feature Selection - Feature Importance 00:07:00 Refresher Session - The Mechanism of Re-sampling, Training and Testing Refresher Session - The Mechanism of Re-sampling, Training and Testing 00:12:00 Algorithm Evaluation Techniques - Introduction Algorithm Evaluation Techniques - Introduction 00:07:00 Algorithm Evaluation Techniques - Train and Test Set Algorithm Evaluation Techniques - Train and Test Set 00:11:00 Algorithm Evaluation Techniques - K-Fold Cross Validation Algorithm Evaluation Techniques - K-Fold Cross Validation 00:09:00 Algorithm Evaluation Techniques - Leave One Out Cross Validation Algorithm Evaluation Techniques - Leave One Out Cross Validation 00:05:00 Algorithm Evaluation Techniques - Repeated Random Test-Train Splits Algorithm Evaluation Techniques - Repeated Random Test-Train Splits 00:07:00 Algorithm Evaluation Metrics - Introduction Algorithm Evaluation Metrics - Introduction 00:09:00 Algorithm Evaluation Metrics - Classification Accuracy Algorithm Evaluation Metrics - Classification Accuracy 00:08:00 Algorithm Evaluation Metrics - Log Loss Algorithm Evaluation Metrics - Log Loss 00:03:00 Algorithm Evaluation Metrics - Area Under ROC Curve Algorithm Evaluation Metrics - Area Under ROC Curve 00:06:00 Algorithm Evaluation Metrics - Confusion Matrix Algorithm Evaluation Metrics - Confusion Matrix 00:10:00 Algorithm Evaluation Metrics - Classification Report Algorithm Evaluation Metrics - Classification Report 00:04:00 Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction 00:06:00 Algorithm Evaluation Metrics - Mean Absolute Error Algorithm Evaluation Metrics - Mean Absolute Error 00:07:00 Algorithm Evaluation Metrics - Mean Square Error Algorithm Evaluation Metrics - Mean Square Error 00:03:00 Algorithm Evaluation Metrics - R Squared Algorithm Evaluation Metrics - R Squared 00:04:00 Classification Algorithm Spot Check - Logistic Regression Classification Algorithm Spot Check - Logistic Regression 00:12:00 Classification Algorithm Spot Check - Linear Discriminant Analysis Classification Algorithm Spot Check - Linear Discriminant Analysis 00:04:00 Classification Algorithm Spot Check - K-Nearest Neighbors Classification Algorithm Spot Check - K-Nearest Neighbors 00:05:00 Classification Algorithm Spot Check - Naive Bayes Classification Algorithm Spot Check - Naive Bayes 00:04:00 Classification Algorithm Spot Check - CART Classification Algorithm Spot Check - CART 00:04:00 Classification Algorithm Spot Check - Support Vector Machines Classification Algorithm Spot Check - Support Vector Machines 00:05:00 Regression Algorithm Spot Check - Linear Regression Regression Algorithm Spot Check - Linear Regression 00:08:00 Regression Algorithm Spot Check - Ridge Regression Regression Algorithm Spot Check - Ridge Regression 00:03:00 Regression Algorithm Spot Check - Lasso Linear Regression Regression Algorithm Spot Check - Lasso Linear Regression 00:03:00 Regression Algorithm Spot Check - Elastic Net Regression Regression Algorithm Spot Check - Elastic Net Regression 00:02:00 Regression Algorithm Spot Check - K-Nearest Neighbors Regression Algorithm Spot Check - K-Nearest Neighbors 00:06:00 Regression Algorithm Spot Check - CART Regression Algorithm Spot Check - CART 00:04:00 Regression Algorithm Spot Check - Support Vector Machines (SVM) Regression Algorithm Spot Check - Support Vector Machines (SVM) 00:04:00 Compare Algorithms - Part 1 : Choosing the best Machine Learning Model Compare Algorithms - Part 1 : Choosing the best Machine Learning Model 00:09:00 Compare Algorithms - Part 2 : Choosing the best Machine Learning Model Compare Algorithms - Part 2 : Choosing the best Machine Learning Model 00:05:00 Pipelines : Data Preparation and Data Modelling Pipelines : Data Preparation and Data Modelling 00:11:00 Pipelines : Feature Selection and Data Modelling Pipelines : Feature Selection and Data Modelling 00:10:00 Performance Improvement: Ensembles - Voting Performance Improvement: Ensembles - Voting 00:07:00 Performance Improvement: Ensembles - Bagging Performance Improvement: Ensembles - Bagging 00:08:00 Performance Improvement: Ensembles - Boosting Performance Improvement: Ensembles - Boosting 00:05:00 Performance Improvement: Parameter Tuning using Grid Search Performance Improvement: Parameter Tuning using Grid Search 00:08:00 Performance Improvement: Parameter Tuning using Random Search Performance Improvement: Parameter Tuning using Random Search 00:06:00 Export, Save and Load Machine Learning Models : Pickle Export, Save and Load Machine Learning Models : Pickle 00:10:00 Export, Save and Load Machine Learning Models : Joblib Export, Save and Load Machine Learning Models : Joblib 00:06:00 Finalizing a Model - Introduction and Steps Finalizing a Model - Introduction and Steps 00:07:00 Finalizing a Classification Model - The Pima Indian Diabetes Dataset Finalizing a Classification Model - The Pima Indian Diabetes Dataset 00:07:00 Quick Session: Imbalanced Data Set - Issue Overview and Steps Quick Session: Imbalanced Data Set - Issue Overview and Steps 00:09:00 Iris Dataset : Finalizing Multi-Class Dataset Iris Dataset : Finalizing Multi-Class Dataset 00:09:00 Finalizing a Regression Model - The Boston Housing Price Dataset Finalizing a Regression Model - The Boston Housing Price Dataset 00:08:00 Real-time Predictions: Using the Pima Indian Diabetes Classification Model Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:07:00 Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:00 Real-time Predictions: Using the Boston Housing Regression Model Real-time Predictions: Using the Boston Housing Regression Model 00:08:00 Resources Resources - Python Machine Learning & Data Science Fundamentals 00:00:00
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Course Curriculum HR and Payroll Management - Updated Version Human Resource Management Introduction to Human Resources 00:16:00 Employee Recruitment and Selection Procedure 00:35:00 Employee Training and Development Process 00:19:00 Performance Appraisal Management 00:19:00 Employee Relations 00:15:00 Ensuring Health and Safety at the Workplace 00:16:00 Motivation and Counselling 00:19:00 Employee Termination 00:15:00 Employer Records and Statistics 00:11:00 Essential UK Employment Law 00:35:00 Payroll Management Module 1: Payroll Basics 00:10:00 Module 2: Company Settings 00:08:00 Module 3: Legislation Settings 00:07:00 Module 4: Pension Scheme Basics 00:06:00 Module 5: Pay Elements 00:14:00 Module 6: The Processing Date 00:07:00 Module 7: Adding Existing Employees 00:08:00 Module 8: Adding New Employees 00:12:00 Module 9: Payroll Processing Basics 00:11:00 Module 10: Entering Payments 00:12:00 Module 11: Pre-Update Reports 00:09:00 Module 12: Updating Records 00:09:00 Module 13: e-Submissions Basics 00:09:00 Module 14: Process Payroll (November) 00:11:00 Module 15: Employee Records and Reports 00:13:00 Module 16: Editing Employee Records 00:07:00 Module 17: Process Payroll (December) 00:12:00 Module 18: Resetting Payments 00:05:00 Module 19: Quick SSP 00:10:00 Module 20: An Employee Leaves 00:13:00 Module 21: Final Payroll Run 00:07:00 Module 22: Reports and Historical Data 00:08:00 Module 23: Year-End Procedures 00:09:00 HR and Payroll Management - Old Version HR & Leadership Leader and HR Management 00:30:00 Commitment and HR Management 01:00:00 Team Management 00:30:00 Build A Mastermind Group 02:00:00 People Recognition in HR Management 00:30:00 Performance, Goals and Management 00:30:00 Think Outside The Box 00:30:00 Be Passionate about Your Work 00:30:00 The Importance Of A Good Team Leader 00:30:00 Human Resource Management Module One - Getting Started 00:30:00 Module Two - Human Resources Today 01:00:00 Module Three - Recruiting and Interviewing 01:00:00 Module Four - Retention and Orientation 01:00:00 Module Five - Following Up With New Employees 01:00:00 Module Six - Workplace Health & Safety 01:00:00 Module Seven - Workplace Bullying, Harassment, and Violence 01:00:00 Module Eight - Workplace Wellness 01:00:00 Module Nine - Providing Feedback to Employees 01:00:00 Module Ten - Disciplining Employees 01:00:00 Module Eleven - Terminating Employees 01:00:00 Module Twelve - Wrapping Up 00:30:00 Managing Payroll What Is Payroll? 00:30:00 Principles Of Payroll Systems 01:00:00 Confidentiality And Security Of Information 00:30:00 Effective Payroll Processing 01:00:00 Increasing Payroll Efficiency 01:00:00 Risk Management in Payroll 00:30:00 Time Management 00:30:00 Personnel Filing 00:30:00 When Workers Leave Employment 01:00:00 Hiring Employees 00:30:00 Paye and Payroll for Employers 01:00:00 Tell HMRC about a New Employee 01:00:00 Net And Gross Pay 00:30:00 Statutory Sick Pay 00:30:00 Minimum Wage for Different types of Work 01:00:00 Mock Exam Mock Exam - Level 3 Diploma in HR and Payroll Management 00:30:00 Final Exam Final Exam - Level 3 Diploma in HR and Payroll Management 00:30:00
Drug and Alcohol: Drug and Alcohol Awareness Course Online Unlock the Power of Drug and Alcohol: Drug and Alcohol Awareness Course: Enrol Now! In recent years, there has been an increase in drug, alcohol, and substance misuse in the United Kingdom. Drugs may destroy people's lives, and some of the most commonly used drugs, such as cocaine, cannabis, LSD, and heroin, lead to addiction that users cannot overcome. Managers in any workplace are uniquely placed to detect early signs of drug or alcohol abuse and put structures in place to minimize the damaging health, social, and economic effects of substance misuse in the UK. Learning Objectives After completing this Drug and Alcohol: Drug and Alcohol Awareness Course, the learner will be able to: Understand the fundamentals of substance misuse. Demonstrate a thorough understanding of the different types of illegal drugs. Understand the legal, health, and social implications of drug and alcohol abuse. Understand how organizations and individuals are expected to handle substance misuse issues. Demonstrate an understanding of the statistics on drug and alcohol abuse in the UK. Describe the various types of drug treatment services available in the UK. Plan a drug intervention and understand when someone requires assistance. Main Course: Drug and Alcohol Awareness Training Free Courses included with Drug and Alcohol: Drug and Alcohol Awareness Course: Course 01: Level 2 Award in Suicide Awareness Course 02: Level 5 Mental Health Care - MCA and DOLS Course 03: Diploma in Counselling and Psychology [ Note: Free PDF certificate as soon as completing the Drug and Alcohol: Drug and Alcohol Awareness Course] Drug and Alcohol: Drug and Alcohol Awareness Course Online This Drug and Alcohol: Drug and Alcohol Awareness Course consists of 06 modules. Assessment Method of Drug and Alcohol: Drug and Alcohol Awareness Course After completing Drug and Alcohol: Drug and Alcohol Awareness Course, you will get quizzes to assess your learning. You will do the later modules upon getting 60% marks on the quiz test. Apart from this, you do not need to sit for any other assessments. Certification of Drug and Alcohol: Drug and Alcohol Awareness Course After completing the Drug and Alcohol: Drug and Alcohol Awareness Course, you can instantly download your certificate for FREE. The hard copy of the certification will also be delivered to your doorstep via post, which will cost £13.99. Who is this course for? Drug and Alcohol: Drug and Alcohol Awareness Course Online This Drug and Alcohol: Drug and Alcohol Awareness Course is ideal for those working in high-risk industries like hospitality and construction and anyone who manages or works with vulnerable people like adolescents. Requirements Drug and Alcohol: Drug and Alcohol Awareness Course Online To enrol in this Drug and Alcohol: Drug and Alcohol Awareness Course, students must fulfil the following requirements: Good Command over English language is mandatory to enrol in our Drug and Alcohol: Drug and Alcohol Awareness Course. Be energetic and self-motivated to complete our Drug and Alcohol: Drug and Alcohol Awareness Course. Basic computer Skill is required to complete our Drug and Alcohol: Drug and Alcohol Awareness Course. If you want to enrol in our Drug and Alcohol: Drug and Alcohol Awareness Course, you must be at least 15 years old. Career path Drug and Alcohol: Drug and Alcohol Awareness Course Online
Drug and Alcohol Awareness: Drug and Alcohol Awareness Online Introducing Drug and Alcohol Awareness: Drug and Alcohol Awareness Course "Empowering the Next Generation" Online course! In recent years, there has been an increase in drug, alcohol, and substance misuse in the United Kingdom. Drugs may destroy people's lives, and some of the most commonly used drugs, such as cocaine, cannabis, LSD, and heroin, lead to addiction that users cannot overcome. Managers in any workplace are uniquely placed to detect early signs of drug or alcohol abuse and put structures in place to minimise the damaging health, social, and economic effects of substance misuse in the UK. Learning Objectives After completing this Drug and Alcohol Awareness: Drug and Alcohol Awareness course, the learner will be able to: Understand the fundamentals of substance misuse. Demonstrate a thorough understanding of the different types of illegal drugs. Understand the legal, health, and social implications of drug and alcohol abuse. Understand how organisations and individuals are expected to handle substance misuse issues. Demonstrate an understanding of the statistics on drug and alcohol abuse in the UK. Describe the various types of drug treatment services available in the UK. Plan a drug intervention and understand when someone requires assistance. Main Course: Drug and Alcohol Awareness Training Free Courses included with Drug and Alcohol Awareness: Drug and Alcohol Awareness Course: Course 01: Domestic Violence and Abuse Awareness Diploma Course 02: Level 2 Award in Suicide Awareness Course 03: Level 5 Mental Health Care - MCA and DOLS [ Note: Free PDF certificate as soon as completing Drug and Alcohol Awareness: Drug and Alcohol Awareness Course] Drug and Alcohol Awareness: Drug and Alcohol Awareness Online This Alcohol Awareness: Alcohol Awareness Course consists of 06 modules. Course Curriculum of Drug and Alcohol Awareness: Drug and Alcohol Awareness Module 01: Introduction Module 02: Historical Overview Module 03: Nature of Addiction Module 04: Policy Initiatives and Strategy in Alcohol and Drug Use Module 05: Understanding Alcohol Abuse Module 06: Drug Addiction & Abuse Assessment Method of Drug and Alcohol Awareness: Drug and Alcohol Awareness After completing Drug and Alcohol Awareness: Drug and Alcohol Awareness Course, you will get quizzes to assess your learning. You will do the later modules upon getting 60% marks on the quiz test. Apart from this, you do not need to sit for any other assessments. Certification of Drug and Alcohol Awareness: Drug and Alcohol Awareness After completing the Drug and Alcohol Awareness: Drug and Alcohol Awareness course, you can instantly download your certificate for FREE. The hard copy of the certification will also be delivered to your doorstep via post, which will cost £13.99. Who is this course for? Drug and Alcohol Awareness: Drug and Alcohol Awareness Online This Drug and Alcohol Awareness: Drug and Alcohol Awareness course is ideal for those working in high-risk industries like hospitality and construction and anyone who manages or works with vulnerable people like adolescents. Requirements Drug and Alcohol Awareness: Drug and Alcohol Awareness Online To enrol in this Drug and Alcohol Awareness: Drug and Alcohol Awareness Course, students must fulfil the following requirements: Good Command over English language is mandatory to enrol in our Drug and Alcohol Awareness Course. Be energetic and self-motivated to complete our Drug and Alcohol Awareness Course. Basic computer Skill is required to complete our Drug and Alcohol Awareness: Drug and Alcohol Awareness Course. If you want to enrol in our Drug and Alcohol Awareness: Drug and Alcohol Awareness Course, you must be at least 15 years old. Career path Drug and Alcohol Awareness: Drug and Alcohol Awareness Online
Mastering Psychology Diploma Course Online Psychology is an extremely versatile discipline because it attempts to understand, explain, and predict many aspects of human behaviour, making it applicable to a wide range of sectors, which includes business and social work, as well as traditional roles within psychology. This Mastering Psychology - Diploma course is designed for those interested in a specialist area of psychology or who prefer to take a more general path. Why choose this Mastering Psychology - Diploma course from the School of Health Care? Self-paced course, access available from anywhere in the world. High-quality study materials that are easy to understand. Course developed by industry experts. After each module, there will be an MCQ quiz to assess your learning. Assessment results are generated automatically and instantly. 24/7 support is available via live chat, phone call, or email. Free PDF certificate after completing the course. This Mastering Psychology course aims to help you understand and evaluate classic and contemporary psychology. The Mastering Psychology - Diplomacourse provides in-depth knowledge and understanding of consciousness, developmental psychology, social psychology, perception, and behaviour. Main Course: Mastering Psychology - Diploma Free Courses included with Mastering Psychology Diploma Course Course 01: Psychological Wellbeing Practitioner Course 02: Diploma in Counselling and Psychology Course 03: Clinical Psychology Diploma [ Note: Free PDF certificate as soon as completing the Mastering Psychology Diploma course] Mastering Psychology - Diploma Online Course Curriculum of Mastering Psychology - Diploma Course Module 01: Concepts of Psychology Module 02: Biological Behaviour of Psychology Module 03: Sensation and Perception Module 04: Consciousness Module 05: Learning Module 06: Introduction to Intelligence Module 07: Memory Module 08: Emotions Module 09: Motivation Module 10: Developmental Psychology Module 11: Behaviours (Personality) Module 12: Social Psychology Module 13: Persuasion Module 14: Psychological Disorders Module 15: Psychological Therapy Module 16: Intro to Statistics, Tests and Measmenture Assessment Method of Mastering Psychology - Diploma Course After completing Mastering Psychology Course, you will get quizzes to assess your learning. You will do the later modules upon getting 60% marks on the quiz test. Apart from this, you do not need to sit for any other assessments. Certification of Mastering Psychology - Diploma Course After completing the Mastering Psychology course, you can instantly download your certificate for FREE. The hard copy of the certification will also be delivered to your doorstep via post, which will cost £13.99. Who is this course for? Mastering Psychology Diploma Course Online This Mastering Psychology - Diploma course is ideal for those interested in studying people and their behaviour and who want to learn more about how the human mind works. It is also suitable for those who want to learn the main elements of psychology. Requirements Mastering Psychology Diploma Course Online To enrol in this Mastering Psychology - Diploma Course, students must fulfil the following requirements: Good Command over English language is mandatory to enrol in our Mastering Psychology - Diploma Course. Be energetic and self-motivated to complete our Mastering Psychology - Diploma Course. Basic computer Skill is required to complete our Mastering Psychology - Diploma Course. If you want to enrol in our Mastering Psychology - Diploma Course, you must be at least 15 years old. Career path Mastering Psychology Diploma Course Online There are several roles in Mastering Psychology - Diploma Course and many different ways to climb the ladder to the top, and a few of these career opportunities are listed below: Psychologist Clinical Psychologist Counsellor Child Psychologist Health Psychologist Occupational Psychologist