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The Course offers an understanding of the statistical concepts and epidemiological strategies utilized in health and social care. The learner will be able to understand and apply strategies to deal with medical and pandemic-related emergencies and understand the statistical tools used to measure mortality, morbidity and general trends in health and social care. After the successful completion of this course, the learner will be able to; Define epidemiology and statistics in health and social care. Understand the importance of epidemiology and statistics in health and social care. Understand measures of disease frequency, including incidence and prevalence. Understand types of epidemiology, including descriptive and analytic epidemiology. Understand the applications of epidemiology in health and social care, including surveillance of disease, identification of risk factors, and evaluation of interventions. Understand descriptive statistics, including central tendency and variability measures. Understand the applications of statistics in health and social care, including data analysis, sample size calculation, and power calculation. The Course offers an understanding of the statistical concepts and epidemiological strategies utilized in health and social care. VIDEO - Course Structure and Assessment Guidelines Watch this video to gain further insight. Navigating the MSBM Study Portal Watch this video to gain further insight. Interacting with Lectures/Learning Components Watch this video to gain further insight. Epidemiology and Statistics in Health and Social Care Self-paced pre-recorded learning content on this topic. Epidemiology and Statistics in Health and Social Care Put your knowledge to the test with this quiz. Read each question carefully and choose the response that you feel is correct. All MSBM courses are accredited by the relevant partners and awarding bodies. Please refer to MSBM accreditation in about us for more details. There are no strict entry requirements for this course. Work experience will be added advantage to understanding the content of the course.The certificate is designed to enhance the learner's knowledge in the field. This certificate is for everyone eager to know more and gets updated on current ideas in their respective field. We recommend this certificate for the following audience. CEO, Director, Manager, Supervisor Clinic/Hospital Supervisor, Director, Manager Health Associate Public Health policy makers/professionals. Health and social care professionals. Social Entrepreneurs with interest in public health and general well-being. Average Completion Time 2 Weeks Accreditation 4 CPD Hours Level Advanced Start Time Anytime 100% Online Study online with ease. Unlimited Access 24/7 unlimited access with pre-recorded lectures. Low Fees Our fees are low and easy to pay online.
Embark on a transformative journey into data analytics with our comprehensive course. Discover the power of data & analytics through engaging modules designed to equip you with skills and knowledge. Whether you're a novice or a seasoned professional, this data analytics course is your gateway to unlocking the potential of big data analytics. From understanding the basics like what is data analytics and harnessing the power of statistics to mastering data visualisation tools, each step is crafted to empower you with the tools necessary to navigate the world of data analytics confidently. With the Google Data Analytics Professional Certificate, you'll gain invaluable insights and practical experience that can catapult you into lucrative data analytics jobs. From enhancing your employability to boosting your earning potential, this course opens doors to a myriad of opportunities in this field. Learning Outcomes: Acquire a solid foundation in data analytics principles and techniques. Develop proficiency in using various data analytics tools and software. Gain skills in data mining, storage, and visualisation. Cultivate a data-analytic mindset to tackle complex problems effectively. Explore career pathways in data science with confidence and clarity. Why buy this Data Analytics 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. Certification After studying the course materials of the Data Analytics 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 Data Analytics course for? Aspiring data analysts seeking to kickstart their careers. Professionals looking to enhance their skillset in this field. Students interested in exploring the fascinating world of data science. Entrepreneurs aiming to leverage analytics for business growth. Individuals seeking to transition into lucrative data analytics jobs. Prerequisites This Data Analytics does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Data Analytics 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 Data Analyst: £25,000 - £50,000 Per Annum Data Scientist: £30,000 - £70,000 Per Annum Business Intelligence Analyst: £28,000 - £55,000 Per Annum Data Engineer: £35,000 - £65,000 Per Annum Database Administrator: £25,000 - £55,000 Per Annum Analytics Manager: £40,000 - £80,000 Per Annum Course Curriculum Module 01: Introduction to the World of Data Introduction to the World of Data 01:00:00 Module 02: Basics of Data Analytics Basics of Data Analytics 00:40:00 Module 03: Statistics for Data Analytics Statistics for Data Analytics 01:00:00 Module 04: Actions Taken in the Data Analysis Process Actions Taken in the Data Analysis Process 00:55:00 Module 05: Gathering the Right Information Gathering the Right Information 01:00:00 Module 06: Storing Data Storing Data 01:15:00 Module 07: Data Mining Data Mining 01:00:00 Module 08: Excel for Data Analytics Excel for Data Analytics 01:20:00 Module 09: Tools for Data Analytics Tools for Data Analytics 01:20:00 Module 10: Data-Analytic Thinking Data-Analytic Thinking 01:10:00 Module 11: Data Visualisation That Clearly Describes Insights Data Visualisation That Clearly Describes Insights 00:45:00 Module 12: Data Visualisation Tools Data Visualisation Tools 01:00:00 Assignment Assignment - Data Analytics 00:00:00
2 QLS Endorsed Course | CPD Certified | Free PDF + Hardcopy Certificates | 80 CPD Points | Lifetime Access
Machine learning doesn’t need to be intimidating—especially when you’ve got R on your side. This course offers a clear, well-paced approach to learning machine learning using one of the most respected languages in data science. Whether you’re brushing up on your statistics or stepping into data modelling, the content is structured to help you think algorithmically and act analytically, without feeling overwhelmed by jargon or complexity. From regression techniques to classification methods and everything in-between, this course covers the core building blocks that give machine learning its predictive power. R is not just a programming language here—it’s your analytical toolkit. If terms like decision trees, clustering, and support vector machines sound like something out of a sci-fi novel, don’t worry—by the end, they’ll feel like familiar companions. Whether you’re analysing patterns or building predictive models, this course offers a confident route through the world of machine learning with an R-flavoured lens. Ask ChatGPT Learning Outcomes: Understand the basics of machine learning and its implementation using R. Develop the skills to build simple and multiple linear regression models. Learn how to use R to analyse datasets and develop predictive models. Understand the concept of dummy variables and the backward elimination approach. Learn how to make accurate predictions using machine learning algorithms and extract valuable insights from data. If you're looking to expand your knowledge in data analysis and machine learning, then the "Learn Machine Learning with R" course is perfect for you. This comprehensive course comprises two sections, each designed to help you gain an in-depth understanding of machine learning concepts, starting from the very basics. You'll learn about linear regression, the equation for the algorithm, and how to make simple linear regression models. Additionally, you'll dive into multiple linear regression, dummy variable concepts, and predictions over the year. With the help of this course, you'll be able to analyse datasets, develop predictive models, and extract valuable insights from them, using R. Learn Machine Learning with R Course Curriculum Section 01: Linear Regression and Logistic Regression Working on Linear Regression Equation Making the Regression of the Algorithm Basic Types of Algorithms predicting the Salary of the Employee Making of Simple Linear Regression Model Plotting Training Set and Work Section 02: Understanding Dataset Multiple Linear Regression Dummy Variable Concept Predictions Over Year Difference Between Reference Elimination Working of the Model Working on Another Dataset Backward Elimination Approach Making of the Model with Full and Null How is the course assessed? Upon completing an online module, you will immediately be given access to a specifically crafted MCQ test. For each test, the pass mark will be set to 60%. Exam & Retakes: It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable. Certification Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of __ GBP. £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery). CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Students or professionals looking to develop their data analysis and machine learning skills. Individuals interested in pursuing a career in data science or machine learning. Anyone interested in understanding how to extract insights from data. Programmers looking to learn machine learning implementation using R. Beginners interested in learning the basics of machine learning. Career path Data analyst: £30,000 to £50,000 Machine learning engineer: £45,000 to £85,000 Data scientist: £40,000 to £80,000 Business analyst: £30,000 to £55,000 Research analyst: £25,000 to £45,000 Certificates Certificate of completion Digital certificate - £9 You can apply for a CPD Accredited PDF Certificate at the cost of £9. Certificate of completion Hard copy certificate - £15 Hard copy can be sent to you via post at the expense of £15.
R isn’t just for statisticians hiding behind graphs — it’s a powerful tool for machine learning, and this course shows you exactly how to put it to work. Designed for curious minds with an interest in data prediction, modelling, and pattern recognition, this course guides you through the essentials of machine learning using R. You’ll explore key techniques like classification, regression, and clustering, all explained in plain English (with just the right amount of code). If you’ve ever wondered how machines “learn” from data — without trying to make them smarter than your laptop needs to be — this course breaks it down with clear logic and no unnecessary flair. Study entirely online, at your own pace, with structured content focused on helping you understand how algorithms behave, why they do what they do, and how to make them behave a little better. Whether you're dipping your toes or deepening your skills, it’s all here, minus the fluff and drama. Learning Outcomes: Understand the basics of machine learning and its implementation using R. Develop the skills to build simple and multiple linear regression models. Learn how to use R to analyse datasets and develop predictive models. Understand the concept of dummy variables and the backward elimination approach. Learn how to make accurate predictions using machine learning algorithms and extract valuable insights from data. If you're looking to expand your knowledge in data analysis and machine learning, then the "Learn Machine Learning with R" course is perfect for you. This comprehensive course comprises two sections, each designed to help you gain an in-depth understanding of machine learning concepts, starting from the very basics. You'll learn about linear regression, the equation for the algorithm, and how to make simple linear regression models. Additionally, you'll dive into multiple linear regression, dummy variable concepts, and predictions over the year. With the help of this course, you'll be able to analyse datasets, develop predictive models, and extract valuable insights from them, using R. â±â± Learn Machine Learning with R Course Curriculum Section 01: Linear Regression and Logistic Regression Working on Linear Regression Equation Making the Regression of the Algorithm Basic Types of Algorithms predicting the Salary of the Employee Making of Simple Linear Regression Model Plotting Training Set and Work Section 02: Understanding Dataset Multiple Linear Regression Dummy Variable Concept Predictions Over Year Difference Between Reference Elimination Working of the Model Working on Another Dataset Backward Elimination Approach Making of the Model with Full and Null How is the course assessed? Upon completing an online module, you will immediately be given access to a specifically crafted MCQ test. For each test, the pass mark will be set to 60%. Exam & Retakes: It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable. Certification Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery). CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Students or professionals looking to develop their data analysis and machine learning skills. Individuals interested in pursuing a career in data science or machine learning. Anyone interested in understanding how to extract insights from data. Programmers looking to learn machine learning implementation using R. Beginners interested in learning the basics of machine learning. Career path Data analyst: £30,000 to £50,000 Machine learning engineer: £45,000 to £85,000 Data scientist: £40,000 to £80,000 Business analyst: £30,000 to £55,000 Research analyst: £25,000 to £45,000 Certificates Certificate of completion Digital certificate - £9 You can apply for a CPD Accredited PDF Certificate at the cost of £9. Certificate of completion Hard copy certificate - £15 Hard copy can be sent to you via post at the expense of £15.
Overview This comprehensive course on Research Methods in Business will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Research Methods in Business 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 Research Methods in Business. It is available to all students, of all academic backgrounds. Requirements Our Research Methods in Business 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 • 56 lectures • 01:01:00 total length •Introduction to Research: 00:01:00 •What is Research?: 00:01:00 •Why Publish?: 00:01:00 •Reasons for Publishing Part 1: 00:01:00 •Reasons for Publishing Part 2: 00:01:00 •Defining a Management Problem: 00:01:00 •The Desired State: 00:01:00 •Identifying from Symptoms: 00:01:00 •Doing Scoping Research: 00:01:00 •Steps in Scoping the Research: 00:01:00 •Writing Notes and Mind Mapping: 00:01:00 •Structuring the Write-up of the Literature: 00:01:00 •Inform the Purpose Statement and Research Questions: 00:01:00 •Writing a Purpose Statement and Research Questions Part 1: 00:01:00 •Writing a Purpose Statement and Research Questions Part 2: 00:01:00 •Presuming That You Know What You Will Find: 00:01:00 •Quantitative Research Approaches Part 1: 00:01:00 •Quantitative Research Approaches Part 2: 00:01:00 •Quantitative Research Approaches Part 3: 00:01:00 •Quantitative Research Approaches Part 4: 00:01:00 •Qualitative Research Approaches Part 1: 00:01:00 •Qualitative Research Approaches Part 2: 00:01:00 •Qualitative Research Approaches Part 3: 00:01:00 •Mixed Method Research Approaches: 00:01:00 •Advantages of Mixed Methods: 00:01:00 •Limitations of Mixed Methods: 00:01:00 •Case Study Research: 00:01:00 •Types of Case Study Methods Part 1: 00:01:00 •Types of Case Study Methods Part 2: 00:01:00 •Advantages / Disadvantages: 00:01:00 •Ethical Considerations: 00:01:00 •Respect for Persons: 00:01:00 •Beneficence: 00:01:00 •Justice: 00:01:00 •Research Issues: 00:01:00 •Writing a Methodology and Methods: 00:01:00 •Effectively Written Methodology Part 1: 00:03:00 •Effectively Written Methodology Part 2: 00:01:00 •Effectively Written Methodology Part 3: 00:01:00 •Writing Data Collection Tools: 00:01:00 •Designing Questions: 00:02:00 •Observation Schedules: 00:01:00 •Qualitative Data Analysis: 00:01:00 •Grounded Theory: 00:01:00 •Thematic Analysis: 00:02:00 •Template Analysis: 00:02:00 •Using CAQDAS Part 1: 00:01:00 •Using CAQDAS Part 2: 00:01:00 •Quantitative Data Analysis: 00:01:00 •Using Descriptive Statistics: 00:01:00 •Inferential Statistical Tests: 00:02:00 •Comparing Findings to Literature and Writing the Final Paper @!: 00:01:00 •Discussion in a Quantitative Study: 00:01:00 •Discussion in a Qualitative Study: 00:01:00 •Writing Conclusions: 00:01:00 •Assignment - Research Methods in Business: 00:00:00
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Overview With the ever-increasing demand for Big Data Analytics in personal & professional settings, this online training aims at educating, nurturing, and upskilling individuals to stay ahead of the curve - whatever their level of expertise in Big Data Analytics may be. Learning about Big Data Analytics or keeping up to date on it can be confusing at times, and maybe even daunting! But that's not the case with this course from Compete High. We understand the different requirements coming with a wide variety of demographics looking to get skilled in Big Data Analytics . That's why we've developed this online training in a way that caters to learners with different goals in mind. The course materials are prepared with consultation from the experts of this field and all the information on Big Data Analytics is kept up to date on a regular basis so that learners don't get left behind on the current trends/updates. The self-paced online learning methodology by compete high in this Big Data Analytics course helps you learn whenever or however you wish, keeping in mind the busy schedule or possible inconveniences that come with physical classes. The easy-to-grasp, bite-sized lessons are proven to be most effective in memorising and learning the lessons by heart. On top of that, you have the opportunity to receive a certificate after successfully completing the course! Instead of searching for hours, enrol right away on this Big Data Analytics course from Compete High and accelerate your career in the right path with expert-outlined lessons and a guarantee of success in the long run. Who is this course for? While we refrain from discouraging anyone wanting to do this Big Data Analytics course or impose any sort of restrictions on doing this online training, people meeting any of the following criteria will benefit the most from it: Anyone looking for the basics of Big Data Analytics , Jobseekers in the relevant domains, Anyone with a ground knowledge/intermediate expertise in Big Data Analytics , Anyone looking for a certificate of completion on doing an online training on this topic, Students of Big Data Analytics , or anyone with an academic knowledge gap to bridge, Anyone with a general interest/curiosity Career Path This Big Data Analytics course smoothens the way up your career ladder with all the relevant information, skills, and online certificate of achievements. After successfully completing the course, you can expect to move one significant step closer to achieving your professional goals - whether it's securing that job you desire, getting the promotion you deserve, or setting up that business of your dreams. Course Curriculum Module 1_ Introduction to Big Data. Introduction to Big Data. 00:00 Module 2_ Hadoop and MapReduce. Hadoop and MapReduce. 00:00 Module 3_ NoSQL Databases. NoSQL Databases. 00:00 Module 4_ Data Storage and Retrieval. Data Storage and Retrieval. 00:00 Module 5_ Data Processing with Spark. Data Processing with Spark. 00:00 Module 6_ Data Analysis with Hadoop and Pig. Data Analysis with Hadoop and Pig. 00:00
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