Business Data Analytics Certificate
Online, 10-week option
Want to earn this certificate quickly? We're now accepting applications for an accelerated, 10-week online program in Summer 2020. Learn more →
Get the skills you need to interpret data and make data-driven business decisions in our Business Data Analytics certificate program. Our certificate will equip you to be a strategic, data-driven leader, while also guiding you through the software, languages, and methodologies for modeling and analyzing data.
Upon completion of the certificate program, you will be able to leverage your knowledge to identify new business opportunities, assess a current business situation, formulate strategic recommendations, and help your company gain a competitive advantage through data analysis. You’ll also be prepared to manage teams of developers, as you will be exposed to languages and tools, including R, SQL, Tableau, and data modeling software.
About the Certificate Program
The Certificate in Business Data Analytics is a graduate-level, five-course certificate that prepares professionals to derive value from data and understand the business implications. It provides an essential foundation for business professionals who need to acquire skills and credentials in business analytics.
It is an excellent specialization for students who are currently pursuing a Quinlan graduate degree, professionals with a business degree, or any professional who needs to understand current business data analytics methodologies. Successful certificate program students come from a variety of backgrounds and undergraduate degrees.
The certificate requires completion of five courses. There are three required core courses and two electives, which may be selected to focus on specific application areas.
*Must be taken before 494 and 796*
This course uses database systems as the focus for studying concepts of data modeling and data manipulation. Procedures for creating, managing, sorting, and processing data are discussed. Concepts of relational database methods are covered as well as the issues that arise in managing information in a database and using it to support business processes.
Outcome: Understanding the development and use of business database systems.
Data Mining involves the search for patterns in large quantities of data. The fundamental techniques used in data mining include, but are not limited to, clustering, decision trees, neural networks, and association analysis.
Outcome: The student will be able to build models using an industry-standard package and interpret the results.
The components and design issues related to data warehouses and business intelligence techniques for extracting meaningful information from data warehouses are emphasized. Oracle tools will be used to demonstrate design, implementation, and utilization issues.
Outcome: students will learn how data warehouses are used to help managers successfully gather, analyze, understand and act on information stored in data warehouses.
Elective Application Courses (Select 2)
Business analytics is the practice of using methodically collected data to drive decisions about business and in business applications. The goal of the course is to introduce students to the current approaches, tools, and techniques involved in this practice. Because many topics and concepts in business analytics are best learned through hands-on work, time will be spent obtaining, processing, analyzing and visualizing data that pertain to different business cases. Students will use R, arguably the most popular analytical software used by data scientists. During this course, students will learn to use R, as well as gain and help improve business insight through data-driven analytics.
Outcomes: Explain the key factors differentiating business intelligence from business analytics. Frame a problem in a business analytics context to drive insightful decisions and gain the competitive edge.
This course focuses on practical methods for in-database data preparation and manipulation to extract analytical insights out of a large or a big data repository. It uses an open source database called PostgreSQL and provides extensive hands-on programming practice towards using advanced SQL, procedural extension to SQL, writing user defined functions, using in-database analytical functions, and manipulating strings, numbers, dates, etc. within a database. The concept of big data, distributed computing frameworks like Apache Hadoop and massively parallel processing databases are also covered.
The amount of data that our world generates is growing at a torrid pace. Sifting through & making sense of these humongous mountains of data is crucial to ensuring business growth, success and to making scientific discoveries & advancements. Data visualization plays an important role in this process.
Outcome: Students will be able to process & visualize large amounts of data in order to enable efficient & effective analysis using industry standard software.
This course will focus on the R language. Students will learn to use R for data analysis and processing. They will write functions and scripts for repeatable analysis, build models, and perform statistical analysis.
This course focuses on how to effectively use a computer programming language to support decision making in business. Examples include using Visual Basic for Applications (VBA) to create applications within Microsoft Excel or using Python for manipulating and analyzing data. In addition to covering the concepts of programming using the specified language, this course covers developing user interfaces, working with external data and debugging code. By the end of this course, the student will be able to build custom procedures and create user-defined functions in the programming language used.
This course will develop a vocabulary and framework for discussing, critiquing, assessing, and designing visual displays of quantitative data. This entails awareness of human perception and cognition, the use of best design practices in visualization of quantitative data, and interacting and storytelling with data.
The fundamentals of managerial statistics are presented. Topics may include descriptive statistics, random variables, probability distributions, estimation, hypothesis testing, regression, and correlation analysis. Excel is used to assist in the analysis of these problems.
Outcome: Students will be able to demonstrate understanding of statistical thinking and data analysis technique for decision-making purposes.This course explores applications for statistics in managerial functions.
Techniques of forecasting and model building are introduced. Methods covered are simple and multiple regression, introduction to time series components, exponential smoothing algorithms, and AIRMA models - Box Jenkins techniques. Business cases are demonstrated and solved using the computer.
Outcome: To be able forecast business and economic variables to enhance business decisions.
Perhaps one of the biggest challenges facing organizations is bridging the gap between those who have technical expertise in information systems and those who are managerial decision makers. This course builds on the decision strategy course to help address that challenge. Outcomes of this class include: understanding the sources and limitations of data, understanding how databases organize data sets and the use of SQL to extract data, increased facility with spreadsheets, and ability to deal with the issues that arise between those who provide data and those who use data to make business decisions.
This course develops an understanding of the marketing research process and the role of survey research in it.
Outcome: Students formulate research problems and a design research study, including the development of a questionnaire, selection of an appropriate sample and data analysis.
This course develops an understanding of the development and use of databases for marketing, retrieval of appropriate data and analysis of that data to increase marketing effectiveness.
Outcome: The student will perform database manipulation and analysis of data. Analysis includes at least univariate analysis, cross-tabulation, creation of new variables, regression analysis and recency-frequency-monetary analysis.
In this course the students will study how to use data analytics to learn about customer needs and improve targeting individual consumers. The course will encourage students to apply scientific methods and models to predict and respond to customer choices. This is the key part of learning Big Data. The term Big Data is viewed in the broad sense as it relates to various aspects of the consumer behavior, which may be captured, measured, and transformed to the digital form.
Through applications of statistical models to the analysis of the real-world databases, the students will learn how firms may use customer data to serve customers better.
This course is designed with marketing managers in mind. As profession marketing is evolving, it is no longer based primarily on the conceptual content. Marketers get exposed to thousand times the volume of data she(he) saw five years ago. More data cannot lead to better decision making unless managers learn how to use that data in meaningful ways. In this course, the students will be introduced to the analytical decision models that assist modern managers in making marketing decisions related to the targeting, product design, communications, etc.
Outcomes: The objectives of this course are the following: 1. To learn analytical techniques and decision models for enhancing marketing decision making in the modern organizations 2. Improve skills to viewing marketing processes and relationships systematically and analytically 3. To learn power of decision models applied in the real managerial contexts 4. To provide students with toolkit that may be used to assess and measure return on marketing investments in organizations
A study of the design, development, and use of decision models for analysis of supply chain problems. This course provides an example-driven approach to learn about important supply chain models, problems, and solution methodologies. The objectives of this course are to develop valuable modeling skills that students can appreciate and use effectively.
Outcomes: Students will have developed an understanding of the issues involved in the use of decision support tools for analysis of supply chain problems.
The purpose of this course is to help students understand feasible econometric techniques in order to mine information to understand economic and financial patterns and to forecast. A rigorous exposition of the theory behind econometric techniques will help students understand the issues raised in different published papers. Topics of econometric techniques covered in this course include panel data analysis, time-series models, discrete choice models, and methods to identify causality between variables. Practical applications will prepare students to use these methods in their own projects.
This course introduces the student to economic and business practices of a foreign country using the analysis of data, and on-site experiences. We will focus on business strategies, impediments, and challenges in light of the culture, politics, history and institutions of a selected country. We will interact with a variety of local people such as small business owners, firm managers, economists, journalists, and students, in order to inform our understanding and analysis.
Outcome: Students will gain knowledge and analytical skills that can assist them in facing the challenges of conducting business in global locations.
Courses are scheduled to enable completion of the certificate in 9 to 12 months. Sessions meet once per week for ten weeks. Courses are offered at our Water Tower Campus in Chicago (weekday evenings or Saturdays), with some courses also available online.
- A Completed Application Form →
- Official Transcripts
- Two Letters of Recommendation (optional)
- A Statement of Purpose
- Professional Resume
All academic programs in the Quinlan School of Business are on the quarter system, and new students begin during all quarters.
|Quarter||Application Deadline||Quarter Starts|
|Fall||July 15||Late August|
|Winter||Oct. 1||Early November|
|Spring||Jan. 15||Late February|
|Summer||April 1||Late May|