Business Analytics Course: Breaking it Down

business analytics courseObtaining your education in business is challenging while at the same time fun and exciting.  You can major in business in your undergraduate college and continue to pursue a master’s in business.  When you do these adventures, you will undoubtedly come across studying business analytics. Becoming a business analyst can be a very rewarding career.

The term business analytics refers to the skills, technologies, applications and practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. A business analytics course focuses on developing new insights and understanding of business performance based on data and statistical methods. In contrast, business intelligence traditionally focuses on using a consistent set of metrics to both measure past performance and guide business planning, which is also based on data and statistical methods.

Business analytics makes extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling, and fact-based management to drive decision making. It is therefore closely related to management science. Analytics may be used as input for human decisions or may drive fully automated decisions. Business intelligence is querying, reporting, OLAP, and “alerts.”

In other words, querying, reporting, OLAP, and alert tools can answer questions such as what happened, how many, how often, where the problem is, and what actions are needed. Business analytics can answer questions like why is this happening, what if these trends continue, what will happen next (that is, predict), what is the best that can happen (that is, optimize).

What does a Business Analytics Course look like? 

Below are three sample course descriptions for various courses under the umbrella of “Business Analytics” at the University of Connecticut.

  • Business Process Modeling and Data Management (3 credits)

Managing and improving a business process adds to the bottom line, and data is a core business asset derived from multiple business processes. The need to manage both efficiently and use them effectively has assumed paramount importance. In all business domains –financial, marketing, operations, etc., pertinent and available data is a bedrock for actionable business intelligence, predictive modeling and other data mining techniques, which is a key element of business productivity and growth. This course introduces market-leading techniques that help to identify and manage key data from business processes. It provides the essential tools required for data mining and business process re-engineering. It combines lecture, class discussion and hands-on computer work in a business-oriented environment. The course covers the following:

Model business processes; How to manage data for various business applications; Show how to retrieve data and create reports in the form you need; Implement a database using a DBMS tool (Oracle or MS Access); Learn how to lead data management, business intelligence and business process engineering projects.

Course includes: Assessment and Analysis; Data Storage, Planning, and Design; Process Modeling

Click here for a foundational course in business finance!

  • Predictive Modeling (3 credits)

Technology advancements now allow companies to capture and store large amount of data (or facts) in databases and data warehouses. With so much raw data, organizations urgently need tools that allow them to effectively sift through these enormous datasets and extract actionable information and knowledge (meaningful patterns, trends, and anomalies) from such data sets to help them optimize businesses. Predictive modeling is the process of developing models to better predict future outcomes for an event of interest by exploring its relationships with explanatory variables from historical data. It is used extensively in businesses to identify risks and opportunities associated with a set of conditions.

The course introduces the techniques of predictive modeling and analytics in a data‐rich business environment. It covers the process of formulating business objectives, data selection, preparation, and partition to successfully design, build, evaluate and implement predictive models for a variety of practical business applications (such as direct marketing, cross selling, customer retention, delinquency and collection analytics, fraud detection, machine failure detection, insurance underwriting). Predictive models such as classification and decision trees, neural networks, regressions, association analysis, link analysis, and others will be studied. It is practically oriented with a focus of applying data analytic tools to help companies answer business questions such as who is likely to respond to a new advertisement, what customers are most likely to be default on a loan/payment, what transactions are most likely to be fraudulent, and what combinations of products are customers most likely to purchase at the same time.

The primary approach will entail ‘learning-by-doing’ with the use of the state-of-the-art software such as SAS JMP®, SAS Enterprise Miner®, and a variety of open source software.

Course includes: Data Visualization; Predictive Models; Model Assessment, Scoring and Implementation

  • Data Analytics using R (3 credits)

Data analytics has emerged as one of the most important new areas with high demand in industry. R, an open source domain specific language (DSL) focused on data analytics, has grown in importance and usage in corporations, because it is free to use, and is being constantly improved to include the latest statistical techniques. Organizations using R span a wide range of industries and include companies such as Google, Facebook, Bing, The New York Times, Orbitz, etc. R code is always at the cutting edge, because the source code is open source, and it receives frequent new contributions and improvements from experts around the world. This course helps students develop proficiency in data analytics using R for statistical inference, regression, predictive analytics, and data mining. It combines lectures, hands-on exercises, business case discussions, and student presentations in a professional environment. As a student of data analytics, you will benefit from learning R because (a) it is a core skill in high demand, and (b) because doing data analytics using R will enhance your understanding of analytics.Specifically, we will cover the following topics: R basics — data frames, packages, etc., Formal Inference – standard errors; t-distribution; confidence intervals; Multiple Regression – assumptions and diagnostics; model fitting; comparing models; interpreting coefficients; multicollinearity; Generalized linear models – Logistic regression; Poisson and quasi-poisson regression; ordinal regression models; survival analysis; Time series analysis – graphical exploration; autoregressive (AR) and autoregressive moving average (ARMA) models; Data mining: clustering, association rules; Text mining: analyzing twitter and social network data.

What analytics skills are needed for the business professional?

A course on business analytics can improve a business professional’s skill set in a number of ways.  The 4 key analytics skills needed by business professionals are:

  • DTD framework: Understanding and hands-on experience of the basic “Data to Decisions” framework
  • Basic “applied” stat techniques: Hands-on experience with basic statistical techniques: Profiling, Correlation analysis, Trend analysis, Sizing/Estimation, Basic Segmentation
  • Working effectively with analysts: Ability to work effectively with Data Scientists/Analyst
  • Advanced “applied” stat techniques (intro): High level understanding of advance techniques: Time Series, Predictive Analytics – Regression and Decision Tree, Segmentation. (

What analytics skils are needed for the data analyst/scientist?

The skills needed for analytics for business professionals are a bit different from that of a data analyst/scientist. The 6 key analytics skills used by successful analyst/data scientist are:

  • DTD framework: Understanding and hands-on experience of the basic “Data to Decisions” framework
  • SQL skills: Ability to pull data from multiple sources and collate: experience in writing SQL queries and exposure to tools like Teradata TDC -0.9%, Oracle ORCL NaN% etc. Some understanding of Big Data tools using Hadoop is also helpful.
  • Basic “applied” stat techniques: Hands-on experience with basic statistical techniques: Profiling, Correlation analysis, Trend analysis, Sizing/Estimation, Segmentation (RFM, product migration etc.)
  • Working effectively with business side: Ability to work effectively with stakeholders by building alignment, effective communication and influencing
  • Advanced “applied” stat techniques (hands-on): Hands-on comfort with advance techniques: Time Series, Predictive Analytics – Regression and Decision Tree, Segmentation (K-means clustering) and Text Analytics (optional)
  • Stat Tools: Experience with one or more statistical tools like SAS, R, SPSS, Knime or others. (

Whatever your background may be, a course on business analytics gives you the breadth you need in the business world.  Try Udemy’s course on learning business process analysis now!