Take a deep-dive into business analytics while earning your MBA.
In today’s data-driven business environment, business leaders must have expertise in data analysis to inform smart business decisions. Through our Certificate in Business Analytics, you’ll learn to integrate data-driven decision-making into the business management process while adding a valuable skill set to your toolkit. This certificate is an add-on to the MBA program and is open to all Full-Time MBA and Professional Evening MBA students.
Upon completion, you’ll:
- Understand how analysis fits into the broader agenda of management.
- Assess the value of data-driven decision-making as well as its limitations.
- Evaluate the cost of data and analysis, and when the benefits of improved business decisions make this cost worth bearing.
- Distinguish and work with different kinds of data in a business setting.
- Construct and demonstrate analysis using programming-based tooling within a business context.
- Classify different kinds of analysis/tools and be able to evaluate which is appropriate for different business problems. (e.g., Evaluate the benefits of programming-based tooling relative to point-n-click or excel-based approaches within a business context.)
- Design and create analysis based on advanced topics, e.g., Machine Learning, Data Mining, Big Data, SQL databases, and apply these tools to real business problems.
- Create your own analysis and demonstrate the application of the above content to practical scenarios.
The certificate consists of five (3-credit) classes, three of which are shared with the MBA program and two of which must be taken as additional credits beyond the credits required for the MBA degree.
Intro to analytics in python
Introduction to Python for Data Analytics (QSTIS834)
This course will introduce students to programming-based tools and techniques for becoming analytically-minded managers. The course covers both a hands-on introduction to the concepts, methods and processes of business analytics as well as an introduction to the use of analytics as the basis for creating a competitive advantage. We will cover variables, data types and data structures, DataFrames, conditionals, loops, and functions. We will also cover reading and writing raw files and the core APIs in analysis and visualization. With the basics under our belt, we will complement it with some of the most popular libraries for data analysis in Python, such as Pandas and Numpy for data manipulation, Matplotlib and Seaborn for visualization, and Jupyter Notebook for reporting. These packages will facilitate workflow and enhance the basic Python functionalities. Using them, one can effortlessly clean up a dataset, create elaborate plots, analyze and summarize the data, and produce presentable reports. Throughout the final project, we will learn to extract value from data by asking the right questions and using the appropriate analytical methods and tools. These methods comprise data preprocessing, explanatory analysis, and machine learning techniques. No prior programming experience is required. Learning basic programming in Python is part of successfully completing the class.
or the following two 1.5 credit courses:
Python for Data Science Bootcamp (QSTQM875)
In this course, students will learn the most essential aspects of python programming. The topics are tailored towards data analysis; no prior programming experience is required. We will cover variables, data types and data structures, data frames, conditionals, loops, and functions. We will also cover reading and writing raw files and the core APIs in analysis and visualization. With the basics under their belt, we will complement these concepts with some of the most popular libraries for data analysis in Python such as: Pandas and Numpy for data manipulation, Matplotlib and Seaborn for visualization, and Jupyter Notebook for analysis and reporting. These packages will facilitate workflow and enhance the basic Python functionalities; using them, one can effortlessly clean up a dataset, create elaborate plots, analyze and summarize the data, and produce presentable reports. During this module, students solidify their new skills by applying the concepts they have learned to the analysis of several datasets. They will be given the opportunity to live-code during the sessions and troubleshoot their code with classmates and the instructor. Students will walk out of this bootcamp with newly-forged Python coding skills, knowledge of several of the most important data science libraries and tools, and have the resources in hand for learning more. Please note that students in the MSDi and MSDT programs may not take this course for degree credit.
QSTQM876 – Deep Learning in Python (new course)
Foundational applied analytics
Decision Making with Data (QSTIS737)
This is an advanced python-based analytics course on data-driven decision-making in business environments. Business analytics professionals need to be able to i) uncover patterns in the data (descriptive analytics); ii) use the data to make predictions about future outcomes (predictive analytics); and iii) leverage this data to make optimal business decisions (prescriptive analytics). This course takes a holistic approach to analytics, touching on aspects of all three descriptive, predictive, and prescriptive pillars. We explore advanced business analytics topics, including data reduction, classification, decision analysis, and optimization. We link data models to strategy relying on statistical programming in Python and introduce novel techniques used in practice. Case studies and projects apply topics to practical business problems. 3 cr.
Choose three of the Following electives
Machine Learning for Business Analytics (QSTMK842)
This course introduces students to the foundational machine learning techniques that are transforming the way we do business. Machine learning relies on interdisciplinary techniques from statistics, linear algebra, and optimization to detect structure in large volumes of data and solve prediction problems. Students will gain a theoretical understanding of why the algorithms work, when they fail, and how they create value. They will also gain hands-on experience training machine learning models in Python and deriving insights and making predictions from real-world data. Prior programming experience (or IS833/IS834) is strongly recommended. Note: The course was previously offered under the title "Digital Marketing Analytics," but does not overlap with MK876; students may take both courses for credit.
Advanced Analytics For Managers: Data Mining (QSTIS841)
The widespread proliferation of IT-influenced economic activity leaves behind a rich trail of micro-level data about consumer, supplier and competitor preferences. This has led to the emergence of a new form of competition based on the extensive use of analytics, experimentation, and fact-based decision making. In virtually every industry the competitive strategies organizations are employing today rely extensively on data analysis to predict the consequences of alternative courses of action, and to guide executive decision making. This course provides a hands-on introduction to the concepts, methods and processes of business analytics. We will learn how to obtain and draw business inferences from data by asking the right questions and using the appropriate tools. Topics to be covered include data preparation, data visualization, data mining, text mining, recommender systems as well as the overall process of using analytics to solve business problems, its organizational implications and pitfalls. Students will work with real world business data and analytics software. Where possible cases will used to motivate the topic being covered. Prior courses in analytics, data management and statistics (such as IS823) will be helpful but are not required.
Big Data Analytics for Business (QSTIS843)
This programming-based analytics course will cover how to perform statistical analysis of large datasets that do not fit on a single computer. We will design a Hadoop cluster on Google Cloud Platform to analyze these datasets. Utilizing Spark, Hive, and other technologies, students will write scripts to process the data, generate reports and dashboards, and incorporate common business applications. Students will learn how to use these tools through Jupyter Notebooks and experience the power of combining live code, equations, visualizations, and narrative text. Employer interest in these skills is very high. Basic programming in python, and basic analytics are prerequisite.
Data Management (QSTIS889)
The ability to collect, organize, access, analyze and harness data is a source of competitive advantage for some and a competitive necessity for others. Getting an organization to the point where it has a data asset it can leverage is a non-trivial task. Many firms have been shocked at the amount of work and complexity that is required to pull together an infrastructure that integrates its diverse data sources and empowers its managers. This course will provide an introduction to the concepts and technologies that are involved in managing and supporting the data assets of your organization. We will cover data modeling, relational databases, including SQL, data warehousing and business intelligence.
Analytics Consulting: Data-Driven Business Solutions (QSTIS860)
This course will introduce concepts, methods, and processes of data mining and machine learning within projects that have been sponsored by partner companies. Through practice in this live setting, we will develop our analytical problem solving skills, and understand how to organize and manage agile analytical projects in the most realistic possible situation. We will learn how to collect, wrangle, and analyze both primary and secondary data sources in multiple business contexts and apply this knowledge to the client data.
Students can take QSTIS737, QSTIS834, QSTQM875, and QSTQM876 in any order, however, the suggested sequence is QSTIS834 or QSTQM875 and QSTQM876 followed by QSTIS737. It is suggested, though not required, that these courses are taken before QSTMK842, QSTIS841, QSTIS843, QSTIS889, QSTIS860.
You can apply to the Certificate in Business Analytics at any point in your program. If you have already taken courses in the list above, the credits will count toward the certificate.
Reach out to Chris Bishal, Assistant Director, Academic Development at firstname.lastname@example.org.