Business Analytics with Excel: Elementary to Advanced

Go to Course: https://www.coursera.org/learn/business-analytics-excel

Introduction

# Course Review: Business Analytics with Excel: Elementary to Advanced In today's data-driven world, possessing a robust understanding of data analytics is not just beneficial but essential for effective decision-making in any business. If you’re looking to equip yourself with the necessary skills to interpret and manipulate data using one of the most widely used tools in business, the Coursera course "Business Analytics with Excel: Elementary to Advanced" is an outstanding choice. ## Course Overview This course focuses on teaching students the analytical frameworks and statistical methods crucial for making informed business decisions. Offering a blend of theoretical concepts and practical applications, it provides hands-on experience in Excel modeling, which is invaluable for modern-day business analysts. The course covers a wide array of topics including linear and integer optimization, decision analysis, risk modeling, and Excel functions, all tailored toward solving real-world business problems. By the end of this course, students will not only understand how to utilize Excel for business analytics but will also develop a strong analytical mindset to approach business challenges. ## Syllabus Highlights ### 1. Introduction to Excel: Basics and Best Practices The course starts with the fundamentals of Excel, ensuring that students with varying levels of experience can follow along. The content covers essential spreadsheet design principles and builds a foundation for more advanced modeling techniques later on. It’s refreshing to see a course that values building skills on a solid foundation, especially in something as complex as data analytics. ### 2. What-If Analysis in Excel What-If Analysis is crucial for testing hypotheses and making data-driven decisions. This module delves into advanced Excel functions while emphasizing their practical application in real business contexts. The knowledge gained here is essential for analyzing different scenarios and their potential impacts on business outcomes. ### 3. Decision Analysis through Regression and NPV This segment covers essential statistical techniques like regression analysis, spotlighting their role in understanding relationships between variables. Students learn how to analyze which regression model best suits their data, an important skill for predictive analysis in business environments. ### 4. Linear Programming Linear programming (LP) emerges as a pivotal method in optimization. This module introduces students to the concept of spreadsheet optimization and applies it to various organizational challenges such as labor scheduling and inventory control. The strategic applications of LP covered here are vital for decision-making in various industries. ### 5. Transportation and Assignment Problems By tackling more specialized LP applications, this module equips students with the skills to solve logistical challenges—balancing costs and efficiency is a critical skill in any business setting. Students learn to leverage Solver, an Excel tool, making complex problem-solving efficient and manageable. ### 6. Integer Programming and Nonlinear Programming Lastly, this module explores the nuances of integer and nonlinear programming, showcasing how to address challenges where certain constraints of LP are either imposed or relaxed. This complex modeling is essential for more advanced business scenarios where precision is key. ## Recommended For I highly recommend "Business Analytics with Excel: Elementary to Advanced" for: - **Business Professionals**: Those seeking to enhance their analytical capabilities and use data to inform business strategies. - **Students**: Ideal for undergraduate or graduate students pursuing degrees in business, finance, or related fields who wish to bolster their technical skills. - **Career Changers**: Individuals shifting to data-heavy roles will find this course particularly beneficial as it lays a solid groundwork in analytics. ## Conclusion In summary, "Business Analytics with Excel: Elementary to Advanced" is a comprehensive course that effectively bridges the gap between data analytics theory and practical application. The progressive structure allows students to build on their knowledge while applying what they learn to real-world situations. The skills acquired through this course will not only enhance your career prospects but will also empower you to make insightful and data-driven decisions in any business environment. Do consider enrolling in this course if you’re serious about advancing your analytics skills using one of the most powerful tools available—Excel.

Syllabus

Introduction to Excel: Basics and Best Practices

The purpose of this course is to expose you to a variety of problems that can be solved using management science methods and modelled in Excel. In this course, we start from the basics of spreadsheet design and work our way up to broader mathematical optimization modelling. Many airlines, banks, and technology companies could not operate today as they do without the skills and techniques taught in this course. In this first module, we begin by introducing a relatively simple example of a mathematical model which we will use as our platform to build off of for more complicated applications later in the course.

We are now ready to introduce more complexity to our spreadsheet models. Since everyone comes from different Excel backgrounds, we will review some basic functions and features as well as more advanced techniques. This module covers more of the modelling process and includes some of the less-well known, but particularly helpful, Excel functions and tools that are available. Remember though that this course's objective is not to be a "how-to" of Excel. Instead, the focus and intent is to use these features to provide insights into real business problems.

In this module the modeling concept of estimating relationships between variables by curve fitting, or regression analysis, is used to solve realistic business problems. Different regression curves are introduced and a mathematical analysis of which curve is best to help defend the model is presented. This allows not only an understanding of the techniques of modelling but also the rational behind which model to use.

In this module we introduce spreadsheet optimization, one of the most powerful and flexible methods of quantitative analysis. The specific type of optimization presented here is linear programming (LP) which is used in all types of organizations to solve a wide variety of problems. As you will see through the examples presented in this course, LP is used in problems of labor scheduling, inventory management, advertising, finance, transportation, staffing, and many others. The goal of this module is to introduce you to the basic elements of LP, the types of problems it can solve, and how to model an LP problem in excel.

This module provides even more examples of problems that can be modeling using linear programming (LP), in particular Transportation and Assignment problems. The basic transportation problem is concerned with finding the best (usually the least cost) way to distribute the good from sources such as factories, to final destinations such as retail outlets. The assignment problem involves finding the best (usually the least cost) way to assign individuals or pieces of equipment to projects or jobs on a one-to-one basis. Using Solver, we will take advantage of the special structure of these LP problems to find the best solutions to complex business problems in an efficient way.

This module presents yet another subset of important mathematical linear programming models that arise when some of the basic assumptions of an LP model are made more or less restrictive. For example, restricting the decision variables to be whole numbers leads to the process of Integer Programming. Restricting the decision variables to be either 0 or 1 leads to binary programming. Lastly, we will see how the skills in this course can be used to solve more complex problems that involve nonlinear models.