Introduction to Business Analytics

Course Description

Introduction to Business Analytics uses spreadsheets to identify trends and relationships in business data and how to apply them in a business environment. UWF states that the focus of the course is on the managerial application of the results rather than the algorithmic derivation of the results, and that visualization techniques are also shown.

Within the SCNS taxonomy, ISM is the Information Systems Management prefix. The University of West Florida publishes this at 3 semester hours through the Department of Business Administration, College of Business. It is offered at approximately 3 Florida institutions.

⚠⚠ Read UWF's framing sentence carefully, because it defines the course: the focus is on managerial application of results rather than algorithmic derivation. This is analytics for decision-makers, not for statisticians. If you want the derivations, that is a statistics or data science course, and this is not it.

That choice is defensible and worth understanding rather than resenting. The commonest failure in business analytics is not a wrong calculation — it is a correct calculation that answers the wrong question, or one nobody acts on because it was never explained. Teaching interpretation and communication first addresses the failure that actually happens.

⚠ It also means the burden of scepticism falls on you. A tool that produces a trend line without requiring you to derive it will happily produce one from data that does not support it. Knowing when a result is meaningless is the skill this course must give you, and it is harder than the arithmetic.

The spreadsheet focus is a genuine strength, not a limitation. Excel is where the overwhelming majority of business analysis actually happens, and an analyst who is genuinely fluent in it is more immediately useful than one who knows a fashionable tool shallowly.

⚠ The contact-hour figure is derived — the University of West Florida publishes none

UWF's catalog publishes a credit value in semester hours, the college and department, and prerequisites. It does not publish contact hours, a lecture and laboratory split, or terms of offering for any course. Every contact-hour value in a UWF guide in this repository is derived. The figure applies the standard lecture convention of 15 contact hours per credit, giving 45 hours for 3 semester hours. ⚠ Note that ISM courses are project-heavy, and scheduled contact hours understate the real time commitment considerably — budget for project work outside class. Confirm the meeting schedule and delivery mode with the department.

Learning Outcomes

Required Outcomes

• Describe the scope of business analytics and its role in decision-making.
• Distinguish descriptive, predictive, and prescriptive analytics.
• Prepare and clean business data for analysis in a spreadsheet.
• Identify and handle missing, duplicate, and erroneous data.
• Apply descriptive statistics to summarise business data.
• Interpret measures of central tendency and dispersion correctly.
• Construct and interpret frequency distributions and cross-tabulations.
• Use pivot tables to summarise and explore data.
• Identify trends and relationships in business data.
• Construct and interpret a regression line and its fit.
• Distinguish correlation from causation and explain the difference.
• Apply forecasting techniques to business time series.
• Recognise seasonality and trend in time series data.
• Apply what-if analysis, goal seek, and scenario tools.
• Construct effective data visualizations for a business audience.
• Select an appropriate chart type for a given message.
• Recognise misleading visualizations and avoid producing them.
• Interpret analytical results in managerial terms.
• Recognise when a result is not statistically meaningful.
• Communicate an analysis and a recommendation to a decision-maker.

Optional Outcomes

• Apply the material to a real organisation or a case study.
• Use AI-assisted tools appropriately and verify their output.
• Communicate technical findings to a non-technical audience.
• Work effectively in a project team with defined roles.
• Build a portfolio artefact suitable for showing an employer.

Major Topics

Required Topics

• Scope and role of business analytics
• Descriptive, predictive, prescriptive analytics
• Data preparation and cleaning
• Missing, duplicate, and erroneous data
• Descriptive statistics
• Central tendency and dispersion
• Frequency distributions and cross-tabs
• Pivot tables
• Trends and relationships
• Regression and goodness of fit
• Correlation and causation
• Forecasting techniques
• Seasonality and trend
• What-if analysis and scenarios
• Principles of data visualization
• Chart selection
• Misleading visualizations
• Managerial interpretation
• Statistical meaningfulness
• Communicating analysis and recommendation

Optional Topics

• Applied case studies and live organisations
• AI-assisted tooling and its verification
• Communicating findings to non-technical stakeholders
• Team project practice
• Portfolio development

Resources & Tools

• Microsoft Excel with the Analysis ToolPak enabled — free for UWF students through Microsoft 365. ⚠ Enable the ToolPak in week one; students lose time to not knowing it exists.
• The analytics text the instructor assigns — Albright and Winston's Business Analytics: Data Analysis and Decision Making and Camm's Business Analytics are the two most used.
• Power BI Desktop — free; not necessarily required here, but learning it alongside pays off immediately in the job market and it reads Excel data directly.
• Storytelling with Data (Cole Nussbaumer Knaflic) — the book, and a free blog; the single best resource on making a chart that communicates rather than decorates.
• Kaggle datasets (kaggle.com) — free; real messy data to practise on, which is materially different from the tidy data in a textbook exercise.
• Data.gov and Florida open data portals — free; genuine public datasets, and useful if you want a project with local relevance.
• The UWF library's business databases — included in enrolment; practitioner sources such as Gartner and industry reports are behind paywalls a student already has, and most never find out.
• LinkedIn Learning — frequently free through UWF or a Florida public library card; strong on specific tools, weak on judgement. Use it for the software, not the thinking.

Career Pathways

• Business or systems analyst (SOC 15-1211, computer systems analyst) — the most common destination for this major, and the role this prefix is most directly built for: translating business needs into system requirements.
• Data analyst and business intelligence analyst (SOC 15-2051, data scientist; SOC 13-1111, management analyst) — the fastest-growing destination, and the reason the analytics courses in this prefix matter.
• Information security analyst (SOC 15-1212) — strong demand and strong pay in Florida, particularly around defence contracting in the Panhandle and Space Coast. Note that this prefix teaches security management, not penetration testing — a real and separate career.
• Database administrator and data engineer (SOC 15-1242, 15-1243).
• IT project manager — commonly entered after several years as an analyst; PMP or CAPM certification is the usual credential.
• ERP functional consultant — SAP, Oracle, Workday, Microsoft Dynamics; a well-paid path that is under-advertised to undergraduates.
• ⚠ Florida employer landscape: defence and aerospace contractors (Lockheed Martin, Northrop Grumman, L3Harris, and the Navy presence around Pensacola), healthcare systems (AdventHealth, Orlando Health, BayCare, Baptist, Ascension Sacred Heart), hospitality and theme parks (Disney, Universal, the cruise lines out of Miami, Port Canaveral and Tampa — all of which run substantial IT organisations), financial services (Raymond James in St. Petersburg, Fidelity in Jacksonville), logistics, and state and county government.
• ⚠ Security clearance is a genuine career asset in this state. Northwest Florida's defence concentration means clearable candidates have access to roles others do not — worth knowing early, because the process is slow and starts with an employer sponsoring you.

AI Integration

Information systems is one of the fields where generative AI has changed working practice fastest, and pretending otherwise would not serve a student. The honest position is that these tools are genuinely useful, genuinely unreliable, and that the professional remains accountable for the output regardless of what produced it.

• Where AI tools help in this domain: drafting and explaining code and SQL, generating test data, summarising documentation and standards, producing first-draft requirements and process descriptions, writing spreadsheet and BI formulas, and explaining an unfamiliar error message — which is a large share of what stalls a beginner.
• ⚠ Where they fail, specifically: they invent plausible functions, libraries, and API endpoints that do not exist; they produce code that runs and is subtly wrong; they are confidently wrong about anything niche, recent, or organisation-specific; and they cannot know your data, your business rules, or your regulatory constraints unless you tell them.
• ⚠⚠ The failure mode that matters most here is a wrong answer that looks right. A query that returns rows is not a query that returns correct rows. Verify against known values before you trust an analysis, and be especially careful with joins, filters, date handling, and anything involving aggregation.
• ⚠⚠⚠ Never paste confidential, personal, or regulated data into a public AI tool. Organisational data, customer records, health information, and anything under FERPA, HIPAA, GLBA or PCI-DSS must not leave a controlled environment. This is a genuine and common way that early-career employees cause serious incidents, and "I did not know" is not a defence that helps anyone. Use synthetic or anonymised data for anything you take outside a sanctioned system.
• Academic integrity: the course policy governs, and policies differ. Some instructors encourage AI use with disclosure, some restrict it to specified tasks, some prohibit it. Read the syllabus and ask if it is unclear — assuming is how students end up in an integrity process.
• ⚠ The professional argument for learning without it first is real, not moralising. If you cannot read and evaluate the output, you cannot catch it when it is wrong — and an employer is paying for the judgement, not the typing. Use AI to go faster on things you understand; do the learning yourself.
• Cite and disclose what you used, when the policy asks. This is becoming a normal professional expectation as well as an academic one.

Special Information

⚠⚠ ISM3011 is the gateway to the entire prefix

• Every undergraduate ISM course at UWF except ISM3011 itself requires it. ISM3116, ISM3323, ISM4113, ISM4320, ISM4321, ISM4400, ISM4481 and ISM4483 all name it. Nothing else in the prefix opens without it.
• ⚠ That makes it a single point of failure in your schedule. Delaying or failing ISM3011 delays everything downstream, and there is no alternative route around it. Take it as early as you are eligible.
• Only ISM3323 offers an alternative — it accepts ISM 3011 OR COP 2253, a programming route. It is the sole exception in the prefix.
• ⚠ ISM3011 itself has an unusual prerequisite: not a course, but completion of 45 hours of college coursework. See the note on standing requirements below.

⚠⚠ The most valuable idea in this course is also the most misused

• Correlation is not causation — everyone can recite it and a great many business decisions ignore it anyway.
• ⚠ The practical version worth carrying: before acting on a relationship in data, ask what else changed at the same time, whether the effect could run the other way, and whether both could be driven by something you did not measure.
• Business data is observational, almost always. Customers were not randomly assigned to anything. That is precisely the condition under which confounding is most likely and most invisible.
• ⚠ A/B testing is the exception and is worth knowing about — it is the one common business setting where randomisation actually happens, and therefore where causal claims are defensible.

Where this course sits, and what comes after it

• It requires ISM3011 and is the analytics entry point in the prefix.
• UWF continues the analytics line in ISM4545 Business Analytics with AI and ISM4400 Decision Support and Data Integration Systems, and data management in ISM4481.
• ⚠ If analytics is your destination, add a database course and a statistics course beyond this prefix. The three together are what employers mean by an analyst; any one alone is thinner than it looks on a transcript.

Certifications worth knowing about

• ⚠ A degree and a certification do different jobs. The degree is the durable credential; certifications are current, specific, and expire. Employers in this field ask for both, and neither substitutes for the other.
• Analytics and data: Microsoft Power BI Data Analyst (PL-300), Tableau Desktop Specialist, Google Data Analytics, AWS and Azure data certifications. Several have free or heavily discounted student pricing — ask the College of Business.
• Security: CompTIA Security+ is the standard entry credential and is frequently a hard requirement for defence-adjacent work under DoD 8570/8140; CISSP and CISM are management-level and require documented experience.
• Project and process: CAPM, PMP, and the Scrum credentials.
• ⚠ Do not collect certifications instead of building things. A portfolio of real projects — a dashboard, a database, an analysis with a written recommendation — outperforms a list of badges in almost every hiring conversation in this field.

Transfer, articulation, and how Florida course levels work

In the Florida Statewide Course Numbering System the first digit is the level: 1 and 2 are lower division, 3 and 4 upper division, 5 and above graduate. ⚠ A lower-division course generally cannot satisfy an upper-division requirement, which matters in this prefix — ISM2000 and ISM3011 both introduce information systems, and only the second is upper division.

⚠⚠ Business programmes add a layer that SCNS does not. Many Florida business colleges are AACSB accredited, and AACSB programmes commonly limit how much upper-division business coursework may transfer in — frequently requiring a substantial share to be taken in residence. A course can articulate under SCNS and still not count toward the major. Check the receiving programme's residency rule before you rely on a transfer.

Many business programmes also impose an admission-to-the-major step with its own grade point requirement, and a minimum grade in each core course. Confirm both against your own catalog year.

Course format and position in the curriculum

• Lecture with substantial project and applied work. ⚠ ISM courses are consistently more time-consuming than their credit value suggests, because software work expands to fill the debugging available.
• ⚠ No "permission is required" marking appears anywhere in the ISM prefix, and no fee notices — enrolment is gated by ISM3011 and by standing.
• Ask about delivery mode. UWF offers substantial online provision in the College of Business, and an online section of a project-based course demands more self-management, not less.
• UWF publishes no contact hours or terms of offering. A course offered in one term only will delay a sequence by a full year if missed — confirm with the department.

ISM3116 is 3 semester hours at the University of West Florida.