Johnny Chew recently accepted an offer from SUSS Business School to major in Business Analytics. He is now planning his course schedule for the upcoming academic year. The academic year at SUSS is divided into two semesters, and Johnny has a wide range of courses to choose from. He has shortlisted twenty potential courses that align with his interests and are beneficial for future job skills. His interest in each course is rated on a scale of 3 to 5, while the relevance to job skills is rated between 1 and 10, as indicated in the fifth and sixth columns of Table 1. Johnny is allowed to take at most five courses in each semester. In determining his course schedule, Johnny needs to consider the following: • Johnny can only take a course if he has completed or is concurrently taking all courses that are prerequisites for the course. The prerequisites for all twenty courses are shown in the fourth column of Table 1. • In the upcoming Jan semester, Johnny must take at least three of the following five courses: Quantitative Methods (course 1), Business Application & Modeling (course 2), Economic Theory (course 3), Data Mining I (course 6), and Business Communications (course 20) • If Johnny takes Text Mining (course 8), he will not be allowed to take Natural Language Processing (course 14), because these two courses cover fairly similar contents. • Johnny would like to take at least one course in Digital Marketing (course 12 and/ or 13) and at least one course in Supply Chain Management (course 10 and/ or 11). Course Index Subject Semester Prerequisities Interest Level Job skill level 1 Quantitative Methods Jan 5 7 2 Business Applications & Modeling Jan 5 6 3 Economic Theory Jan & July 4 3 4 Modern Finance Jan 4 4 5 Fintech July 4 3 4 6 Data Mining I Jan 3 8 7 Data Mining II July 2,6 3 8 8 Text Mining Jan 1,3 5 7 9 Statistical Method July 1 4 5 10 Supply Chain Management I Jan 4 4 11 Supply Chain Management Ii July 1,10 4 3 12 Digital Marketing I Jan 3 4 13 Digital Marketing II July 9,12 3 4 14 Natural Language Process July 3 5 7 15 Information System I Jan 4 6 16 Information System II July 15 4 6 17 Financial Leadership July 4 4 3 18 New Product Development July 10,12,17 3 7 19 Web 3.0 Organizations Jan 4 3 3 20 Business Communications Jan 5 5 Suppose that Johnny’s overall objective is to maximize his total interest level. Formulate a discrete optimization model that can be used to determine Johnny’s optimal course schedule. You answer should include the following: • Determine the objective of the proposed model; • Define the necessary decision variables; • List all the constraints and give the explanation.

Constraints: Semester Limit: Johnny can only take up to five courses every semester. This may be expressed for each semester j as: Σ(X_ij) <= 5 for all j (Jan, July) Required January Course: Σ(X_1j) >= 3 for j = Jan where the summation iterates only over courses 1 (Quantitative Methods), 2 (Business Application & Modeling), 3 (Economic Theory), 6 (Data Mining I), and 20 (Business Communications). Prerequisite Course: A course can only be taken if all of its requirements have been fulfilled or are being taken concurrently. This may be described using the following constraints: X_ij <= Σ(X_kj) for all courses i, j (Jan, July) where k is a prerequisite of course i Text Mining vs. Natural Language Processing: X_8j + X_14j <= 1 for j = Jan/July (any semester) Minimum Course Constraints: Johnny takes at least one course in Digital Marketing and one in Supply Chain Management. =X_10+ X_(11 )≥1 ( Digital Marketing Constraint) =X_12+ X_(13 )≥1 ( Supply Chain Management Constraint) Based on the above information, Construct a spreadsheet model for your formulated optimization model in Q1(a) using the Excel solver to solve the optimal solutions.

A person can learn the skills of running a small business from this course.Group of answer choicesfalseTrue

a) Hello everyone, my name is Ravini Ravikumar. I am a current third year Bachelor of Commerce student, double majoring in Business Analytics and Finance. I am excited to join this course on predictive analytics and data analytics techniques. I have gained background in business analytics mainly through the various coursework where I worked on various projects involving data collection, analysis, and visualisation. For example, through a data visualisation and communication course where we had to create a data story based on an SDG. Through this project, I gained practical experience in handling large datasets and applying Tableau and R-code for visualisations. As well in another course, where we used r-code and predictive techniques. My personal learning objectives for this course include deepening my understanding of advanced predictive analytics techniques, learning how to apply these methods in real-world scenarios, and enhancing my skills in using tools like R.b)A compelling example of predictive analytics in action is its application in personalised marketing within the retail sector. Retail companies collect vast amounts of data on customer transactions, online behaviour, and demographic information. By leveraging predictive analytics, these companies can create highly personalised marketing strategies to enhance customer engagement and increase sales.For instance, a retail company might use supervised learning techniques such as classification and regression models to predict which customers are most likely to purchase a specific product. The response variable, in this case, could be the purchase likelihood of a customer, while the predictors might include past purchase history, browsing behaviour, customer demographics, and response to previous marketing campaigns. A commonly used model for this purpose is the logistic regression model, which helps in classifying customers into categories such as 'likely to buy' and 'unlikely to buy'.In practice, the company might collect data from its CRM system, e-commerce platform, and social media channels. It would use historical data. The results of the predictive model enable the marketing team to target high-potential customers with personalised offers and recommendations. For example, customers predicted to have a high likelihood of purchasing new electronics might receive targeted ads, discounts, or personalised emails.One of the potential drawbacks of this approach is the risk of overfitting the model to historical data, which might not accurately reflect future customer behaviour. Additionally, the model relies on the quality and relevance of the input data. To mitigate these risks, it's crucial to continuously monitor and update the model with new data and refine the predictive features based on emerging trends.

What are the main and sub-headings of your proposed course for a 16-hour course on strategic human resource management for senior managers of a number of businesses that is to be presented as part of the MBA training course?

According to Hogan and Warrenfelz, competencies concerned with analyzing issues, making decisions, and strategic thinking fall into the _____ category.Multiple Choicebusiness skillsinterpersonal skillsintrapersonal skillsleadership skills