Business Analytics

Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data. The Business Analytics specialization is targeted towards aspiring managers, senior managers, and business executives who wish to have a well-rounded knowledge of business analytics that integrates the areas of data science, analytics and business decision making.

The courses in this Specialization will focus on strategy, methods, tools, and applications that are widely used in firms and these are about:

  • Data strategy at firms
  • Reliable ways to collect, analyze, and visualize data–and utilize data in organizational decision making
  • Understanding data modeling and predictive analytics at a high-level
  • Learning basic methods of business analytics by working with different tools and data sets
  • Learning to make informed business decisions via analytics across key functional areas in business such as finance, marketing, retail & supply chain management, and social media to enhance profitability and competitiveness.

Learning Outcomes

You will:

  • Learn to develop a taxonomy of data analytic problem-solving strategies based on types of data, volume of data, and the nature of the business decision-making context that can serve you for the long term in your field of practice.
  • Learn to identify business (functional) area gaps that can benefit from data-driven decision making, identify various stakeholders for a data analytic problem-solving approach, and develop a template for stakeholder interactions.
  • Learn to combine business domain knowledge from decision makers with detailed steps that coordinate with the data analytics life cycle.
  • Gain an understanding of the state-of-the-art data science techniques keeping in mind the corresponding business domains and functional situations where such techniques might find a home in organizations.


If data is the “new oil” of the 21st century then analytics is the enhanced engine for business performance. Students completing this course will be well prepared to be literate and conversant in nearly all aspects of analytics, to participate in analytics initiatives, and be in a position to help their employers become data-driven organizations.

Analytics is all around us and a part of almost anything we do, whether it’s going to a restaurant, shopping, driving a car, or even going to school. The same is true with most businesses–and even to a greater extent so. Businesses run on data, and data offers little value without analytics. The ability to process data to make predictions about the behavior of individuals or markets, to diagnose systems or situations, or to prescribe actions for people or processes drives business today. And increasingly many businesses are what’s called “data-driven”, proactively relying more on cold hard information and sophisticated algorithms than upon the gut instinct or slow reactions of humans.

Tomorrow’s successful executives less often will be those with the best instincts for business, but rather those with the best instincts for compiling and applying data in innovative ways.

In this course, students first will learn about the various styles of analytics from those focused on understanding and diagnosing the business and business functions, to those concerned with predicting events and prescribing actions. Then we will explore the differences between basic business intelligence and more advanced forms of analytics such as machine learning. Data visualization methods and metrics design will also be covered along with the range of valuable data sources that can be used for analytics. The first module will finish with a discussion of analytics collaboration, governance, and ethics.

In the next module, the class will probe dozens of real-world analytics problems and solutions in industries such as financial services, retail, manufacturing, energy, healthcare, entertainment and government. Students will learn about how analytics is and can be used to optimize supply chains, improve sales and marketing, and improve talent effectiveness, financial performance, and IT operations. This module will finish with delving into how analytics enables digital business.

The course will continue with identifying and learning about key analytics roles and organization structures, including chief data and analytics officers, data scientists, and analytics centers of excellence. Alternatives to direct hiring such as outsourcing and crowdsourcing will also be covered.

Finally, the course will scrutinize the range of analytics architectures, from the data warehouse to the data lake, and providing an understanding of key ancillary analytic concepts and technologies such as data quality, data integration, master data, metadata, purpose-build analytic database products, and cloud alternatives. The course will wrap up with a discussion of analytic trends and futures.

Never in human history has data played a more important role in our daily lives. And as the pace of data generation continues to accelerate, it has never been more important for professionals know how to convert these data into stories.

This course introduces students to the science of business analytics while casting a keen eye toward the artful use of numbers. The goal is to provide businesses and managers with the foundation needed to apply data analytics to real-world challenges they confront daily in their professional lives. Students will learn to identify the ideal analytic tool for their specific needs; understand valid and reliable ways to collect, analyze, and visualize data; and utilize data in decision making for their agencies, organizations or clients.

During week one, we build a foundation for students through an exploration of the evolving history of data visualization in society. Next, we provide students with an understanding a framework for organizing dataviz tools today (and tomorrow). We then examine the challenges (and opportunities) of visual communication before teaching students a framework for evaluating the quality of a data visualization.

In the next module, we set context by offering a perspective on just how much data we have today. We next teach – and prioritize – the methods used by analysts to access data before discussing the power of frameworks in focusing a story. At the end of the module, students are taught an approach to help you plan your data story. Students are then introduced to a real company facing real business challenges that serves as a case study to bring concepts to life for the remainder of the course.

In the third module, students learn to understand the relationship between visuals and the insight you seek. An approach to allocating time and effort when building a data story is discussed, as well as the three essential elements of good visual form. Next, students are taught three simple rules to creating inviting visualizations, which is the first of the three essential elements of good visual form.

The course concludes with an examination of techniques students can use to enrich content and connect to audience. A deep discussion of the attention to detail required of sophisticated execution is followed by a review of several tests students can use to improve dataviz design.

Finally, students are taught the McCandless Method of ensuring the impact of your dataviz through a five-step narrative, derived from an examination of data visualization presentations made by David McCandless, the British data-journalist and founder of the visual blog Information Is Beautiful.

It is widely anticipated that in the future, managers will increasingly use analytics to make business decisions. Concurrently, the number of tools and applications of analytics to turn data into insights is growing day by day. This course introduces students to this toolset in a gradual manner and with business applications in mind.

Students will gain an understanding of the basic methods of business analytics by working with different tools and data sets. The course will emphasize applications over the mathematics of the methods. Students will get to apply methods using Rattle, a menu driven software.

The course will begin with what is familiar to many business managers and those who have taken the first course in this specialization. The first set of tools will explore data description, statistical inference, and regression. We will briefly extend these concepts to other statistical methods used for predicting consumer behavior and forecasting. In the next segment, students will learn about tools used for identifying important features in the dataset that can either reduce the complexity, help identify important features of the data or further help explain behavior. The instructors will then explain data mining concepts that are used to make predictions and classifications. The final segment will be devoted to understanding why, and how, we can learn from data.
Successful use of business analytics and data mining requires both understanding of the business context where value is to be captured, and an understanding of exactly what the methods can do. Some examples include: Statistical tools – Multiple Linear Regression and Logistics regression are used to construct models for estimation and prediction. Business Example-1: A credit score should not be some arbitrary judgement of credit worthiness; a predictive statistical model that uses prior data can help prediction of repayment behavior. Similar applications abound in every area of business such as predicting which orders are most likely be delayed based on recent deliveries from the suppliers or which assets are most likely to increase in value, and predicting the choice behavior of airline customers.

Forecasting Time Series is intended to help managers do a better job of anticipating future events, hence a better job of managing uncertainty by using effective forecasting techniques.

Business Example-2: In making decisions under uncertainty, where time and resources are directly related, forecasting capability becomes critical. Predicting aggregate demand, responses to promotional offers, property values, wages, cost of inputs, and other variables that affect business fall within the scope.

Data Exploration and Dimension Reduction – Understanding and conquering the curse of dimensionality is important to make sense of data. This segment is to help managers appreciate that along with richness of data comes the challenge of making sense of relationships between data points and exploiting these relationships to construct simpler and powerful models.

Business Example-3: There has been a tremendous increase in the way of data generation via sensors, digital platforms, user-generated content etc. are being used in the industry. For example, sensors continuously record data and store it for analysis at a later point. In the way data gets captured, there can be a lot of redundancy. With more variables, comes more trouble! There may be very little (or no) incremental information gained from these sources. This is the problem of high unwanted dimensions. And to avoid this trouble, data exploration and dimension reduction comes to the rescue by examining and extracting lesser dimensions ensuring that it conveys similar information concisely.

Clustering Analysis – Data becomes more manageable when interrelationships can be easily understood. The goal of clustering is to segment data into a set of homogenous clusters by mining relationship among records to identify similar groups. Cases which include market segmentation analysis, and extracting insights for customer intelligence, are some of the techniques covered in this part of the course.

Business Example-4: Understanding the boundaries among clusters/groups for the purpose generating insight is used in a vast variety of business applications, from customized marketing to industry analysis. For instance, in retail businesses, data clustering helps with customer shopping behavior, developing sales campaigns, and customer retention. Other business that use cases include image segmentation, web page grouping, market segmentation, and information retrieval.

Classification Algorithms and Prediction – Data mining techniques can be used to augment classification and prediction toolkits. Prior observations are used to develop rules where the classification is known, which are then applied to the new data with the unknown classification to predict the class of new records (i.e. can a loan applicant repay on time (class-I), repay late (class-II), or declare bankruptcy (class-III)).

Business Example-5: A financial service provider is interested in knowing which customers are likely to default on loan payments. This enterprise is also interested in understanding what characteristics of customers may explain their loan payment behavior. A marketing director is interested in choosing the set of customers or prospects who are most likely to respond to a direct mail campaign. The same officer is also interested in knowing what characteristics of consumers are most likely to explain responsiveness to the campaign. Classification techniques are useful to help answer such questions.

Association rules, recommendation systems and market basket analysis – We study tools for recognizing what opportunities to recommend, identify cross-sell or upsell.

Business Example-6: There is need for identification of shopping patterns to increase the size of a sale. The goal is to make the consumer experience more intuitive and not overwhelming by targeting the right customers with the right products by predicting what individual users would enjoy. More specifically, rather than “what is the relationship between advertising and sales”, we are interested in knowing “what specific advertisement, or recommended product, should be shown to a given online/offline customer at this moment?”

Digitization has ushered in a new era of innovations and radical industry transformations. The greater availability of data and increased computational capabilities have spawned broad innovations across the enterprise – in areas of finance, marketing, management of human capital manufacturing processes, logistics and product design. Each module in these set of courses will review the applications of analytics and the changes that analytics can bring to the specific functional division of the firm. The main theme of these sets of courses is that subject matter expertise matters to framing the correct data problem, collecting data, analysis, performance metrics, and operationalization. The overview in each area will be followed by an in-depth project-based application and analysis exercise.