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Take a step toward a brighter future! *** Course Curriculum: *** Diploma in E-Commerce Management at QLS Level 5 Module 1: Introduction to Electronic Commerce Module 2: Ecommerce Strategy and Implementation Module 3: Customer Service Module 4: Products, Brands, and Services Module 5: Content Planning and Production Module 6: Use of Social Networks Module 7: Marketing Module 8: Creating an Engaging User Experience Module 9: Transaction Management Module 10: Ecommerce Analytics Assessment Process After completing an online module, you will be given immediate access to a specially designed MCQ test. The results will be immediately analysed, and the score will be shown for your review. The passing score for each test will be set at 60%. You will be entitled to claim a certificate endorsed by the Quality Licence Scheme after you have completed all of the Diploma in E-Commerce Management at QLS Level 5exams. 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This course aims to enable participants to critically assess the advantages and disadvantages of employing email and content marketing strategies within affiliate marketing. Participants will gain insights into the strategic use of these methods, evaluating their impact on affiliate campaigns. By delving into the pros and cons, participants will be better equipped to make informed decisions, optimizing their affiliate marketing strategies through effective use of email and content marketing. After the successful completion of the course, you will be able to learn about the following, Grasp affiliate marketing concepts, including networks and programs. Assess the pros and cons of email and content marketing in affiliate strategies. Choose effective methods for email and content marketing campaigns. Understand permission marketing and monitor email campaigns diligently. Develop strategies for selecting and monitoring affiliates, and optimizing performance. Analyze data to optimize affiliate campaigns and implement best practices. Create impactful email campaigns with personalized content and effective management. Develop diverse content marketing campaigns with varied content types and goals. Use tools for monitoring and analyzing content marketing campaign effectiveness. Comply with legal and ethical considerations, including disclosure and anti-spam measures in affiliate marketing. This course aims to guide participants in choosing and implementing effective methods for executing email and content marketing campaigns. Through practical applications and case studies, participants will develop the skills needed to create impactful email campaigns with personalized content and manage them efficiently. Moreover, participants will learn to develop diverse content marketing campaigns, incorporating various content types and aligning them with specific goals. This practical knowledge will empower participants to execute successful campaigns in the real-world affiliate marketing landscape. This course empowers participants to master affiliate strategy development, monitoring affiliates, and optimizing campaign performance through data analysis. It also equips participants to craft impact email campaigns with personalized content and efficient management. The curriculum covers the creation of diverse content marketing campaigns, encompassing varied content types and objectives. Lastly, participants will attain a thorough comprehension of the legal and ethical aspects of affiliate marketing, ensuring compliance with disclosure and anti-spam measures by the course's conclusion. 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. Fundamentals of Affiliate marketing Self-paced pre-recorded learning content on this topic. Fundamentals of Affiliate Marketing Put your knowledge to the test with this quiz. Read each question carefully and choose the response that you feel is correct. All MSBM courses are accredited by the relevant partners and awarding bodies. Please refer to MSBM accreditation in about us for more details. There are no strict entry requirements for this course. Work experience will be added advantage to understanding the content of the course. The certificate is designed to enhance the learner's knowledge in the field. This certificate is for everyone eager to know more and get updated on current ideas in their respective field. We recommend this certificate for the following audience. Affiliate Marketing Specialist Email Marketing Strategist Content Marketing Manager Affiliate Program Coordinator Performance Optimization Analyst Campaign Analytics Specialist Compliance and Ethics Officer in Affiliate Marketing Average Completion Time 2 Weeks Accreditation 3 CPD Hours Level Advanced Start Time Anytime 100% Online Study online with ease. Unlimited Access 24/7 unlimited access with pre-recorded lectures. Low Fees Our fees are low and easy to pay online.
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Office Manager Business Analyst Retail Manager Project Manager Marketing Specialist Entrepreneur Certificates CPD Accredited Digital Certificate Digital certificate - Included Upon passing the Course, you need to order a Digital Certificate for each of the courses inside this bundle as proof of your new skills that are accredited by CPD QS for Free. CPD Accredited Hard Copy Certificate Hard copy certificate - Included Please note that International students have to pay an additional £10 as a shipment fee.
This course for absolute beginners provides you with the opportunity to systematically learn core statistical and probability concepts, descriptive statistics, hypothesis testing, regression analysis, analysis of variance (ANOVA), and advanced regression/ML methods such as logistics regressions, polynomial regressions, decision trees, and more.
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Certificates CPD Accredited Digital Certificate Digital certificate - Included CPD Accredited Hard Copy Certificate Hard copy certificate - Included If you are an international student, you will be required to pay an additional fee of 10 GBP for international delivery, and 4.99 GBP for delivery within the UK, for each certificate
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Course Curriculum Section 01: Introduction Introduction to Supervised Machine Learning 00:06:00 Section 02: Regression Introduction to Regression 00:13:00 Evaluating Regression Models 00:11:00 Conditions for Using Regression Models in ML versus in Classical Statistics 00:21:00 Statistically Significant Predictors 00:09:00 Regression Models Including Categorical Predictors. Additive Effects 00:20:00 Regression Models Including Categorical Predictors. Interaction Effects 00:18:00 Section 03: Predictors Multicollinearity among Predictors and its Consequences 00:21:00 Prediction for New Observation. Confidence Interval and Prediction Interval 00:06:00 Model Building. What if the Regression Equation Contains 'Wrong' Predictors? 00:13:00 Section 04: Minitab Stepwise Regression and its Use for Finding the Optimal Model in Minitab 00:13:00 Regression with Minitab. Example. Auto-mpg: Part 1 00:17:00 Regression with Minitab. Example. Auto-mpg: Part 2 00:18:00 Section 05: Regression Trees The Basic idea of Regression Trees 00:18:00 Regression Trees with Minitab. Example. Bike Sharing: Part1 00:15:00 Regression Trees with Minitab. Example. Bike Sharing: Part 2 00:10:00 Section 06: Binary Logistics Regression Introduction to Binary Logistics Regression 00:23:00 Evaluating Binary Classification Models. Goodness of Fit Metrics. ROC Curve. AUC 00:20:00 Binary Logistic Regression with Minitab. Example. Heart Failure: Part 1 00:16:00 Binary Logistic Regression with Minitab. Example. Heart Failure: Part 2 00:18:00 Section 07: Classification Trees Introduction to Classification Trees 00:12:00 Node Splitting Methods 1. Splitting by Misclassification Rate 00:20:00 Node Splitting Methods 2. Splitting by Gini Impurity or Entropy 00:11:00 Predicted Class for a Node 00:06:00 The Goodness of the Model - 1. Model Misclassification Cost 00:11:00 The Goodness of the Model - 2 ROC. Gain. Lit Binary Classification 00:15:00 The Goodness of the Model - 3. ROC. Gain. Lit. Multinomial Classification 00:08:00 Predefined Prior Probabilities and Input Misclassification Costs 00:11:00 Building the Tree 00:08:00 Classification Trees with Minitab. Example. Maintenance of Machines: Part 1 00:17:00 Classification Trees with Miitab. Example. Maintenance of Machines: Part 2 00:10:00 Section 08: Data Cleaning Data Cleaning: Part 1 00:16:00 Data Cleaning: Part 2 00:17:00 Creating New Features 00:12:00 Section 09: Data Models Polynomial Regression Models for Quantitative Predictor Variables 00:20:00 Interactions Regression Models for Quantitative Predictor Variables 00:15:00 Qualitative and Quantitative Predictors: Interaction Models 00:28:00 Final Models for Duration and TotalCharge: Without Validation 00:18:00 Underfitting or Overfitting: The 'Just Right Model' 00:18:00 The 'Just Right' Model for Duration 00:16:00 The 'Just Right' Model for Duration: A More Detailed Error Analysis 00:12:00 The 'Just Right' Model for TotalCharge 00:14:00 The 'Just Right' Model for ToralCharge: A More Detailed Error Analysis 00:06:00 Section 10: Learning Success Regression Trees for Duration and TotalCharge 00:18:00 Predicting Learning Success: The Problem Statement 00:07:00 Predicting Learning Success: Binary Logistic Regression Models 00:17:00 Predicting Learning Success: Classification Tree Models 00:09:00
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