Every day, analytics assist businesses in quantifying the effects of their decisions. The ability to find meaningful patterns in data helps organizations make decisions to maximize their investments, gain market share, and appeal to the market, from marketing strategies to financial decisions. Businesses can quickly transform big data into accurate insights to drive their decisions thanks to the rise of machine learning and AI.
This course review features Prescriptive analysis offered by Wharton. The course tells how different actions can help you achieve your goals more quickly and more effectively. Advancements in market simulation are making prescriptive data analysis more precise and accessible to business users. Learn by recreating how a consumer market behaves in a mock environment.
What is Prescriptive Analytics?
Prescriptive analytics makes use of machine learning to help businesses decide a course of action based on a computer program’s predictions. Prescriptive analytics works with predictive analytics, which uses data to determine near-term outcomes. Specifically, prescriptive analytics factors information about possible situations or scenarios, available resources, past performance, and current performance, and suggests a course of action or strategy. It can be used to make decisions on any time horizon, from immediate to long-term. You can use prescriptive analysis to prevent fraud, limit risk, increase efficiency, meet business goals, and create more loyal customers.
Why you should take this Course?
The answer lies in the fact that this course brings quality details closer to you. This course will teach you everything you need to know, various management styles, having a focused mind, and guiding your company’s growth. You sill know numerous types of data-intensive businesses and government agencies can benefit from using prescriptive analytics, including those in the financial services and health care sectors, where the cost of human error is high. Take a look at a wide range of real instances and learn the best practice techniques that you can apply instantly to improve your performance.
With each step towards success in your career, you’ll be able to distinguish the Dos and Don’ts. With hundreds of learners, this course teaches unique practices simply and engagingly that you might not find anywhere. take a look at the below course effectivenss score provided by content experts on Takethiscourse:
|Content ||Engagement||Practice ||Career benefits|
| ★★★☆☆||★★★★☆ || ★★★★☆|| ★★★☆☆|
How Prescriptive Analytics Works?
Prescriptive analytics is based on artificial intelligence techniques such as machine learning, which is the ability of a computer program, without additional human input, to understand and advance from the data it collects while adapting as it goes. Machine learning enables the processing of massive amounts of data that are now available. When new or additional data becomes available, computer programs automatically adjust to make use of it, in a much faster and more comprehensive process than human capabilities could manage. Prescriptive analytics works with another type of data analytics, predictive analytics, which involves the use of statistics and modeling to determine future performance, based on current and historical data.
What you’ll learn from this Course:
- You will learn about key areas of customer analytics: descriptive analytics, predictive analytics, prescriptive analytics, and their application to real-world business practices including Amazon, Google, and Starbucks to name a few.
- This course provides an overview of the field of analytics so that you can make informed business decisions.
- Learn the main tools used to predict customer behavior and identify the appropriate uses for each tool.
- Model key ideas about customer analytics and how the field informs business decisions
- Decipher the history of customer analytics and latest best practices at top firms
Remarkably, this course offers a diverse range of interesting learning alternatives that helps you to understand the significance and relevance of this incredible initiative.
Primary outcome through feedback and comments allows any student to delve in with the course. Keeping this checklist in mind, the following section is a marker of the students’ sincere perspective of this single program.
- I enrolled in the Curriculum intending to become an expert in Business Analytics. So, to be honest, I made a calculated decision/question/probability about how much it would be beneficial and how much I would know by the time I finished the certification program. Now, I am very optimistic and can say with certainty that my fundamentals and foundation in Customer Analytics are very solid and that I can apply them to any problem or situation (Srinivasa R K J, ★★★★★).
- All of the professors did an excellent job of outlining potential points of interest and explaining concepts clearly. Though many statistical terms may be confusing at first, a quick Google search can help you understand them. Aside from that, there may be a few typographical/technological errors in the course (Dom A, ★★★★★).
- This is a very in-depth course. It also provided several case studies to help readers better understand the concepts. However, I believe that hands-on experience applying Customer Analytics to a problem in a step-by-step fashion would have solidified the knowledge gained from this course (Akshay K, ★★★★☆).
- As a beginner course in customer analytics, this is a good choice. If you’re new to analytics in general, this might be a good place to start. However, if you have some prior experience, you may not learn anything significant. This was supposed to be a five-week course, but I finished it in less than seven days (Apoorv K, ★★★☆☆).
- I took the course and discovered that it is a very basic customer analytics course. Glancing at the course function and context, I was expecting a little more advanced information (Clifford N D, ★★☆☆☆).
- I’d like to learn more about models, particularly predictive models, but everything I’ve read about them has been ambiguous. They generally discuss the demand curve, which is more of an economics lesson than a customer analytics lesson. I believe they should include examples and highlight the different types of models and how they are used (Efe E, ★☆☆☆☆).
Following methodology could be used to determine whether a local fire department must demand residents to evacuate a specific area if a massive fire is close to their homes, or whether a specific article will be prevalent with readers. Based on data about searches and social shares for related topics to forecasting environmental hazards, everything is under control with Wharton’s Customer analysis course. Takethiscourse summons learners from all over the world to transform their skills into action!