Complete the following.a) Introduce yourself to your fellow participants. Share with us any experience you have with predictive analytics and data analytics techniques, and what your personal learning objectives are for this course.b) Find a real-life example of predictive analytics and/or data analytics techniques and briefly describe it (e.g. supervised or unsupervised, classification or regression, prediction or inference, state the response variable, the predictors, the model, results, potential drawbacks). You can include plots to support your description. Your example does not need to be limited to a business context.

a) Introduce yourself to your fellow participants. Share with us any experience you have with predictive analytics and data analytics techniques, and what your personal learning objectives are for this course.

b) Find a real-life example of predictive analytics and/or data analytics techniques and briefly describe it (e.g. supervised or unsupervised, classification or regression, prediction or inference, state the response variable, the predictors, the model, results, potential drawbacks). You can include plots to support your description. Your example does not need to be limited to a business context.

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.

0:00[MUSIC] With this course, you'll become an expert on strategic business analytics. As we'll explain later, we'll help you develop four sets of skills needed to leverage value from data. Analytics, IT, Business, and Communication.Play video starting at ::25 and follow transcript0:25We'll discuss a wide variety of examples from the business world. Different functions such as marketing or supply chain, and different sectors such as the telecommunications sector, the retail sector, the public sector, and so on. In order to analyze the related data. We'll present different techniques, use the strategic business analytics. How to find groups of similar and dissimilar observations. How to find relationships between causes and consequences. How to make forecasts, and how to make all those techniques relevant for your business.Play video starting at :1:4 and follow transcript1:04Actually, we will pay special attention to the business aspect of those questions.Play video starting at :1:9 and follow transcript1:09What is the actual problem we want to solve? And how can we make our conclusions really convincing, actionable, and efficient?Play video starting at :1:19 and follow transcript1:19At the end of each module, there will be a recital of the different techniques for helping you to wrap up the tools and techniques presented during this module.Play video starting at :1:31 and follow transcript1:31There will be then a quiz to complete. For the last module, we will require you to produce a business level output of your analysis that will be evaluated by the other partic

What is predictive analytics?Question 4Answera.Analysis that looks at existing data to determine trends and patterns in the workforceb.Analysis that looks at historical data to understand what happened in the pastc.Analysis that forecasts future outcomes based on existing and historical data setsd.Analysis that generalizes data to recommend actions