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Online Business Analytics Curriculum

The online Business Analytics curriculum capitalizes on the University of Chicago’s expertise and resources in teaching analytical skills that will enable students to accelerate their careers. Students will learn by analyzing real business problems individually and in virtual teams while benefitting from the knowledge and experience of their instructors and peers.

The Business Analytics certificate consists of two components: business intelligence and business analytics. The business intelligence courses develop students’ ability to work with data to evaluate and understand what has and is happening within the business. The business analytics courses develop students’ analytical skills to drive impactful insights from data by using standard statistical software tools.

Program Structure

The Business Analytics certificate consists of four required online courses:

Business Intelligence Courses

  • Exploring Data to Evaluate Business Practices: Databases and Reporting
  • Exploring Data to Evaluate Business Practices: Exploratory Data Analysis and Visualization

Business Analytics Courses

  • Data Analysis for Evidence Based Decision Making
  • Data Mining for Evidence Based Decision Making

The total cost of all four courses in the Business Analytics certificate is $7,000. The certificate may be completed in two academic quarters.

Business analytics courses

The courses include online course content, activities, and assessments. In addition, classes will meet virtually in a weekly one-hour synchronous session where students interact with the course instructor and each other. This combination of content accessible anytime combined with weekly synchronous sessions provides flexibility, while assuring that students enjoy the benefits of interactive learning experience.

 

Click a course title to read the course description and register for the course.

Courses

Business Analytics Exploring Data to Evaluate Business Practices: Databases and Reporting
04/3/2017 to 05/5/2017

BUAN 18100 | Exploring Data to Evaluate Business Practices: Databases and Reporting

This course introduces students to the basic database structures with an aim to make them comfortable with extracting, transforming, and loading data in and out of databases. The focus is on working with databases effectively to ask the right business questions, identify significant problems, finding related answers and solutions, and in general, to drive insights to evaluate business outcomes.

Synchronous Sessions: 1 hour weekly (specific date/time TBD).

Prerequisite(s):
none
Instructor:
Ashish Pujari

Ashish Pujari

Ashish Pujari

Ashish Pujari has over 15 of experience in software engineering, IT strategy, and consulting. As a Director of Technology at IRI, Ashish leads the design and development of business analytics solutions for retail and CPG clients. He has successfully engineered highly scalable platforms for retail assortment optimization, pricing and trade analytics, and statistical modeling. Prior to joining the retail industry, Ashish was a Lead Technology Architect in finance, brokerage, banking, insurance, and communications domains for clients in Europe, North America, and Asia. He specializes in big-data analytics, cloud computing, algorithm development, application and database design, and decision management and visualization technologies. He also has vast experience with Agile methodologies and offshore development best-practices. During his early career, Ashish was a core team member in the first reference implementation of a BPML engine. Ashish has also contributed to open source projects and is an active member of several technology communities and user groups. He earned a Bachelor’s in Electrical Engineering from the National Institute of Technology, Rourkela and is currently pursuing a Master’s of Science in Analytics at the University of Chicago. His research interests include parallel and distributed systems and machine learning.

Course Code: BUAN 18100
Section: 17S1
Location: Online
Dates:
Apr 03 to May 05
Tuition: $1,750.00
Days/Times:



Unlimited slots available

Online Registration Close Date:
March 27, 2017
Online registration closes at 11:59 PM the day before the posted close date.

Business Analytics Data Analysis for Evidence Based Decision Making
04/3/2017 to 05/13/2017

BUAN 19100 | Data Analysis for Evidence Based Decision Making

This course focuses on a number of practical business problems and ways basic data analysis techniques could be used to formulate solutions. Examples include A/B and multivariate testing, diagnostic analysis, prediction problems, and forecasting. Students also learn selecting appropriate analytical model for analysis as well as evaluating model performance.

Synchronous Sessions: 1 hour weekly (specific date/time TBD).

Prerequisite(s):

BUAN 18100; BUAN 18200

Instructor:
Yuri Balasanov

Yuri Balasanov

Yuri Balasanov, PhD

Dr. Balasanov earned his Master’s degree in Applied Mathematics and Ph. D. in Probability Theory and Mathematical Statistics from the Moscow State University, Russia. His primary expertise and research interests are in the area of stochastic modeling and advanced data analysis with applications in many fields including finance and economics, marketing, biology, medical studies, etc. Yuri has led many research teams running data analysis projects and developing well known statistical software. He has published multiple articles with his academic and applied research and presented at scientific conferences across the world.

Yuri spent significant part of his career in major institutions of financial industry as quantitative researcher, quantitative trader and head of trading research, Chief Investment Officer and risk manager. He is the founder and the President of Research Software International. He combines his work in the industry with years of teaching experience at the Moscow State University, Department of Mathematical Statistics and the University of Chicago, Graduate Program on Financial Mathematics and Graduate Program on Analytics.

Course Code: BUAN 19100
Section: 17S7
Location: Online
Dates:
Apr 03 to May 13
Tuition: $1,750.00
Days/Times:



Online Registration Close Date:
March 23, 2017
Online registration closes at 11:59 PM the day before the posted close date.

Business Analytics Data Mining for Evidence Based Decision Making
05/15/2017 to 06/24/2017

BUAN 19200 | Data Mining for Evidence Based Decision Making

This course teaches students to develop insights from data by using data-mining techniques. Examples include classification of customers or products into segments or groups, making recommendations, and analyzing relationships. Students also learn ways to validate analytical solutions they develop.

Synchronous Sessions: 1 hour weekly (specific date/time TBD).

Prerequisite(s):

BUAN 18100; BUAN 18200

Instructor:
Anil Chaturvedi

Anil Chaturvedi

Anil Chaturvedi, PhD

Dr. Chaturvedi has over 25 years of professional experience at companies such as AT&T Bell Labs, Kraft Foods, Capital One, and Accenture. He has provided consulting services to Bank of America, Fannie Mae, Johnson & Johnson, and Proctor & Gamble.  His general research interests include the areas of multivariate analysis methods for Big Data – multi-linear models, information mining, and business insights.  He has co-authored a book "Mathematical Tools for Applied Multivariate Analysis" with the Late Professor Paul Green (University of Pennsylvania) and Professor J. Douglas Carroll (Rutgers University). He has patented and published analytical methods for Direct Marketing and Information Mining, Market Segmentation, New Product Development, Product Positioning, Customer Loyalty, Consumer Promotion Mix optimization, and Brand Strategy. He completed his PhD from Rutgers University, and PGDM from IIM Ahmedabad, India.

 

Course Code: BUAN 19200
Section: 17S7
Location: Online
Dates:
May 15 to Jun 24
Tuition: $1,750.00
Days/Times:



Unlimited slots available

Online Registration Close Date:
May 5, 2017
Online registration closes at 11:59 PM the day before the posted close date.