Throughout the specialization, you will apply the skills you learn to business problems and data sets. You will have opportunities to build conceptual models of business and simple database models, practice data extraction using SQL, apply predictive and prescriptive analytics to business problems, develop models for decision making, interpret the software output, and finally present results and share findings. We will use basic Excel and the software tool Analytic Solver Platform (ASP), which is a plug-in for Excel. Learners participating in assignments will be able to get free access to the software.
Gain Real-World Business Analytics Skills
Leverage data to solve complex business problems
About This Specialization
Follow the suggested order or choose your own.
Designed to help you practice and apply the skills you learn.
Highlight your new skills on your resume or LinkedIn.
- Intermediate Specialization.
- Some related experience required.
Introduction to Data Analytics for BusinessUpcoming session: Jul 31 — Sep 4.
About the CourseThis course will expose you to the data analytics practices executed in the business world. We will explore such key areas as the analytical process, how data is created, stored, accessed, and how the organization works with data and creates the environment in which analytics can flourish. What you learn in this course will give you a strong foundation in all the areas that support analytics and will help you to better position yourself for success within your organization. You’ll develop skills and a perspective that will make you more productive faster and allow you to become a valuable asset to your organization. This course also provides a basis for going deeper into advanced investigative and computational methods, which you have an opportunity to explore in future courses of the Data Analytics for Business specialization.
Predictive Modeling and AnalyticsUpcoming session: Jul 31 — Sep 4.
About the CourseThis course introduces some of the most widely used predictive modeling techniques and their core principles. By taking this course, you will form a solid foundation of predictive analytics, which refers to tools and techniques for building statistical or machine learning models to make predictions based on data. Two common examples are used throughout the course so that you will see how different predictive models work on the same dataset and get a sense of their relative strengths and weaknesses. You will also learn how to build these predictive models using the software tool XLMiner, which is a plug-in for Excel. Even though we do not cover every predictive models, the fundamental ideas you will learn from this course apply to models not covered in the course as well.
Business Analytics for Decision MakingUpcoming session: Jul 31 — Sep 4.
- 4 weeks of study, 3-4 hours per week
About the CourseIn this course you will learn how to create models for decision making. We will start with cluster analysis, a technique for data reduction that is very useful in market segmentation. You will then learn the basics of Monte Carlo simulation that will help you model the uncertainty that is prevalent in many business decisions. A key element of decision making is to identify the best course of action. Since businesses problems often have too many alternative solutions, you will learn how optimization can help you identify the best option. What is really exciting about this course is that you won’t need to know a computer language or advanced statistics to learn about these predictive and prescriptive analytic models. The Analytic Solver Platform and basic knowledge of Excel is all you’ll need. Learners participating in assignments will be able to get free access to the Analytic Solver Platform.
Communicating Business Analytics ResultsUpcoming session: Jul 31 — Sep 4.
About the CourseThe analytical process does not end with models than can predict with accuracy or prescribe the best solution to business problems. Developing these models and gaining insights from data do not necessarily lead to successful implementations. This depends on the ability to communicate results to those who make decisions. Presenting findings to decision makers who are not familiar with the language of analytics presents a challenge. In this course you will learn how to communicate analytics results to stakeholders who do not understand the details of analytics but want evidence of analysis and data. You will be able to choose the right vehicles to present quantitative information, including those based on principles of data visualization. You will also learn how to develop and deliver data-analytics stories that provide context, insight, and interpretation.
Advanced Business Analytics CapstoneUpcoming session: Sep 18 — Oct 23.
About the Capstone ProjectThe analytics process is a collection of interrelated activities that lead to better decisions and to a higher business performance. The capstone of this specialization is designed with the goal of allowing you to experience this process. The capstone project will take you from data to analysis and models, and ultimately to presentation of insights. In this capstone project, you will analyze the data on financial loans to help with the investment decisions of an investment company. You will go through all typical steps of a data analytics project, including data understanding and cleanup, data analysis, and presentation of analytical results. For the first week, the goal is to understand the data and prepare the data for analysis. As we discussed in this specialization, data preprocessing and cleanup is often the first step in data analytics projects. Needless to say, this step is crucial for the success of this project. In the second week, you will perform some predictive analytics tasks, including classifying loans and predicting losses from defaulted loans. You will try a variety of tools and techniques this week, as the predictive accuracy of different tools can vary quite a bit. It is rarely the case that the default model produced by ASP is the best model possible. Therefore, it is important for you to tune the different models in order to improve the performance. Beginning in the third week, we turn our attention to prescriptive analytics, where you will provide some concrete suggestions on how to allocate investment funds using analytics tools, including clustering and simulation based optimization. You will see that allocating funds wisely is crucial for the financial return of the investment portfolio. In the last week, you are expected to present your analytics results to your clients. Since you will obtain many results in your project, it is important for you to judiciously choose what to include in your presentation. You are also expected to follow the principles we covered in the courses in preparing your presentation.