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Business analytics refers to the process of using data and analytical techniques to improve decision making in a business environment. It encompasses data management processes, such as data collection, validation, and organization, as well as analytical techniques, such as statistics, optimization, predictive modeling, forecasting, and visualization. The objective of business analytics concentration is to teach students advanced skills and techniques applied to business problems.
Applied Business Analytics and Decisions – Learn to make decisions supported by data and models. Students learn to model and manage business decisions with data analytics and decision models. The course generally covers descriptive analytics (e.g., data visualization, query, data slicing), predictive analytics (e.g., forecasting, classification, simulation), and prescriptive analytics (e.g., optimization).
Data Management – This course teaches the basic tools in acquisition, management, and visualization of large data sets. Students will learn how to: store, manage, and query databases via SQL; quickly construct insightful visualizations of multi-attribute data using Tableau; use the Python programming language to manage data as well as connect to APIs to efficiently acquire public data.
Research Design and Analytics – Problem areas to be covered include research design and implementation, commercial data sources, qualitative research (attitude measurement), survey methods, and the burgeoning uses of the internet and web-based data.
Spreadsheet Modeling and Applications – Emphasizes problem solving using spreadsheet software to formulate and solve practical optimization problems from such mathematical programming areas as linear, integer, and nonlinear programming, and multiple-objective decision making, and probabilistic modeling to support risk analysis in the context of spreadsheets
Business Application Development with Visual Basic for Excel This course demonstrates how to use VBA in Excel to automate repetitive and time consuming tasks, generate interactive reports, manipulate charts, filter databases, and run solver.
Applied Regression and Data Analysis – The course considers procedures for data collection, effective analysis, and interpretation for management control, planning, and forecasting. The course focuses primarily on multiple regression models, which includes weighted least squares, analysis of variance, and analysis of covariance.
Data Mining and Applied Multivariate Analysis – This course introduces business leaders to powerful methods for understanding and obtaining managerial insights from multivariate data. The methods include data reduction techniques – principle component analysis, factor analysis, and multidimensional scaling; classification methods – discriminate analysis and cluster analysis; and relational methods – multivariate regression, logistic regression, and neural networks.
Business Forecasting – Students acquire hands-on experience with building and applying forecasting models to actual data on sales, inventories, income, earnings per share, and other variables widely encountered in business.
Data Analytics – With the ongoing explosion in availability of large and complex business datasets (“Big Data”), Machine Learning (“ML”) algorithms are increasingly being used to automate the analytics process and better manage the volume, velocity and variety of Big Data. This course teaches how to apply the growing body of ML algorithms to various Big Data sources in a business context. The course will generally use R and Stata as the primary data analysis platforms and Microsoft Azure or Amazon Webs Services as cloud platform for execution and deployment of ML projects.
Artificial Intelligence for Business – Artificial Intelligence (“AI”) allows computers and machines to automate the business logic – to work and react like humans. This course aims to provide students with a conceptual introduction of AI, a broad understanding of AI’s basic techniques, how AI is applied to problems, future applications, of AI, and an awareness of the challenges, risks and ethical considerations of use of AI in business.
Applied Econometrics – This course is an introduction to econometric methods and their use in policy analysis. Most of the course focuses on multiple regression analysis, beginning with ordinary least squares estimation, and then considers the implications and treatment of serial correlation, heteroskedasticity, specification error, and measurement error. The course also provides an introduction to simultaneous equations models, time series analysis, models for binary dependent variables, and methods for longitudinal analysis.
Data Manipulation – This course aims to help students get started with their own data harvesting, processing, aggregation, and analysis. We need an automated way of gathering the data, parsing it, and summarizing it, before we can do more advanced analysis. Students often learn techniques of exploratory data analysis, using scripting, text parsing, structured query language, regular expressions, graphing, and clustering methods to explore data with R and Python.
Information Visualization – Topics include data and image models, multidimensional and multivariate data, design principles for visualization, hierarchical, network, textual and collaborative visualization, the visualization pipeline, data processing for visualization, visual representations, visualization system interaction design, and impact of perception. Emphasizes construction of systems using graphics application programming interfaces (APIs) and analysis tools.
Predictive Analysis – This open graduate course will provide a general overview of the principles, concepts, techniques, tools and services for managing, harmonizing, aggregating, preprocessing, modeling, analyzing and interpreting large, multi-source, incomplete, incongruent, and heterogeneous data (Big Data).
Data Analytics Certifications
CAP: Certified Analytics Professional – INFORMS is a membership-based association aimed at practitioners, researchers and instructors in analytics, as well as operations research and management sciences. The association reports about 12,500 members from nearly 90 countries, most of whom participate in various educational and networking opportunities via their INFORMS membership. The organization also sponsors the vendor-neutral Certified Analytics Professional (CAP) certification.
MCSE: Data Management and Analytics – Microsoft is part of the big data mix with its MCSE: Data Management and Analytics certification, which leans heavily toward SQL Server 2016 and emphasizes cloud environments and reporting. This credential replaces the Microsoft Certified Solutions Expert (MCSE): Business Intelligence, which retired on March 31, 2017.
MongoDB NoSQL Certifications – MongoDB is both an open-source, NoSQL document-oriented database and the name of the company providing that technology. Because of its document-oriented NoSQL model, MongoDB is well suited for managing large amounts of loosely structured data, as is so often the case in big data projects.
- MongoDB was deemed a leader in Forrester Wave: Big Data NoSQL, Q3 2016, and Gartner selected MongoDB as a challenger in its 2016 Magic Quadrant for Operational Database Management Systems. The database ranks fifth in overall database engine popularity as of March 2019.
- The MongoDB NoSQL certification program recognizes developers and operations professionals who can create and run applications on MongoDB. The program offers two associate-level credentials: MongoDB Certified DBA and MongoDB Certified Developer. The company plans to eventually roll out higher-level certifications. The current exams are based on MongoDB V4.0.
Oracle Business Intelligence Certification – Oracle has one of the largest certification programs in the world, and it has granted more than 1 million Oracle and Sun certifications. Oracle offers its Business Intelligence (BI) certifications across several applications and platforms, such as Business Intelligence Applications 7 for CRM, Business Intelligence Applications 7 for ERP, and Business Intelligence Foundation 11g. To narrow down the field, we focused on the Oracle Business Intelligence Foundation Suite 11g Certified Implementation Specialist credential. Candidates for this certification are intermediate-level professionals (architects, analysts, developers and administrators) who work with Oracle Business Intelligence Suite solutions, performing tasks such as installing, building, querying, configuring, and managing the platform and BI dashboards.
SAS Certified Data Scientist – SAS is a multibillion-dollar global corporation that specializes in business analytics software and services. The company’s well-honed certification program offers nine credentials across programming, information and data management, advanced analytics, and business intelligence. Three noteworthy SAS certifications are the SAS Certified Data Scientist, SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling, Business Intelligence Content Developer for SAS 9, and SAS Certified Big Data Professional Using SAS 9. We concentrated on the SAS Certified Data Scientist Using SAS 9. Candidates for the Data Scientist certification should have in-depth knowledge of and skills in manipulating big data using SAS and open-source tools, using complex machine learning models, making business recommendations, and deploying models. Candidates must pass five exams to earn the SAS Certified Data Scientist credential.
The Cloudera Certified Professional Data Engineer (CCP Data Engineer) offers CCA Spark and Hadoop Developer and CCA Data Analyst certifications. Given the company’s leadership status in software and services based on Hadoop, the CCP Data Engineer certification is worth your consideration.
Data Analysis Organizations
- SIGKDD, ACM Special Interest Group on Knowledge Discovery in Data and Data Mining is the leading professional society for Knowledge Discovery, Data Mining, and Data Science
- AAi: Advanced Analytics Institute, University of Technology, Sydney, the leading group in data analytics in Australia.
- American Statistical Association.
- DMG – An independent, vendor led group which develops data mining standards, such as the Predictive Model Markup Language (PMML).
- Data Mining Section of INFORMS, Institute for Operations Research and the Management Sciences.
- ICDM, IEEE International Conference on Data Mining
- IMLS, The International Machine Learning Society, coordinates annual International Machine Learning Conferences.
- INFORMS is the leading international association for professionals in operations research and analytics.
- International Institute for Analytics, dedicated to the advancement of analytics in everyday business practices.
- IIBA, International Institute for Business Analysis, an independent non-profit professional association.
- KDNet — European Knowledge Discovery Network of Excellence
- National Center for Data Mining (NCDM) at the University of Illinois at Chicago (UIC).
- SF Bay ACM Data Mining SIG, organizing meetings, talks, and providing networking opportunities for people interested in knowledge discovery, data mining and data engineering.
- SIGKDD, ACM Special Interest Group on Knowledge Discovery in Data and Data Mining, the leading professional society in the field, with annual KDD Conference, Explorations magazine, and other activities.
- SIGMOD, ACM Special Interest Group on Management of Data.
- Web Analytics Association (WAA), now known as “Digital Analytics Association”, promotes Web analytics education, advocacy, research, standards and communication.