Analytics Programs

I chair Franklin’s B.S. in Analytics and M.S. in Business Analytics. Both are online, both teach analytics, and their graduates compete for many of the same jobs. The difference is where you start and what the degree is built around.

Which one fits

B.S. in Analytics ↓ M.S. in Business Analytics ↓
Level Bachelor’s, 120 credit hours Master’s, 32 credit hours
Built for Career starters and working adults entering the field Working professionals and career changers who already hold a degree
Pace Up to 90 credits can transfer in As few as 12 months, with a graduate certificate earned at the midpoint
Shape Broad foundation — programming, mathematics, data security and governance — plus a focus area Narrow and applied — analytics aimed directly at business decisions
Specialization Three focus areas, 24 credit hours each No tracks; one applied sequence ending in a client capstone
AI AI-augmented analytics framework in DATA 300; Business and AI (BSAD 305) in the Business Analytics focus Introduced in the first term (BUSA 605) and carried through the capstone
The roles each curriculum points at Hands-on analyst and engineering work — Data Analyst, BI Analyst, Data Engineer, Statistician Business-facing and advisory work — Management Analyst, Analytics Consultant, financial analysis
Where these degrees lead →Every course mapped to the skills it teaches and the job roles it supports, with national salary and growth figures and what employers actually ask for in job postings.

B.S. in Analytics

Franklin’s B.S. in Analytics prepares career starters and working adults to turn data into decisions — building job-ready skills in Excel, SQL, Python, Tableau and Power BI, 100% online. Choose from three focus areas — Applied Data Science, Business Analytics or Healthcare Analytics — and tailor the degree to the career you want.

What you’ll be able to do

Graduates can apply analytics methods and tools in problem-solving, use business domain knowledge and data for decision-making, present insights to a broad audience, analyze potential biases and ethical implications, and apply best practices to optimize performance or protect sensitive data.

That last one is why cybersecurity sits in the core rather than in an elective — handling data responsibly is part of the job, not an add-on.

The analytics core

Ten courses every student completes, whichever focus area they choose:

Course What it adds
COMP 101 – Problem Solving With Computing Structured thinking and programming foundations
MATH 254 – Mathematics for Data Analytics Calculus, linear algebra and probability for modeling
BUSA 250 – SQL for Business SQL for querying and exploration
DATA 300 – Introduction to Analytics Problem framing, wrangling, statistics, storytelling, AI-augmented analytics
DATA 310 – Data Visualization Tableau and Power BI, dashboards
BUSA 350 – Principles of Analytics Modeling Machine-learning fundamentals and model interpretation, in Dataiku, without programming
DATA 400 – Principles of Machine Learning Python; algorithms, prediction models, evaluation
DATA 450 – Advanced Topics in Analytics Big-data platforms, interpretation, simulation, optimization
CYSC 250 – Cybersecurity for the Professions Data security, governance, risk, privacy and ethics
DATA 495 – Analytics Capstone A real-world project end to end, plus a portfolio

Three focus areas

Each adds 24 credit hours on top of the core, and is what tilts the same foundation toward one kind of work.

Applied Data Science — the technical track: analytics programming, data engineering, cloud, warehousing and IT project management. COMP 204, DATA 250, DATA 430, CLOUD 200, MIS 310, ITEC 430, DATA 415. Points at Data Scientist, Data Engineer and Data Analyst roles.

Business Analytics — the business track: accounting, economics, finance, management, marketing, and Business and AI. ACCT 202, ECON 210, FINA 301, MGMT 312, MKTG 300, BSAD 305. Points at Business Analyst, Financial Analyst and Management Analyst roles.

Healthcare Analytics — the health track: healthcare management, medical terminology, health data and informatics, health information systems, and the legal side. HCM 210, HIM 150, HIM 200, HIM 320, HIM 350, HIM 470, HIM 485, HCM 442. Points at Healthcare Data Analyst and Health Informatics Analyst roles.

Credit you already have

Up to 90 of the 120 credit hours can transfer inThat is 75% of the degree. You need to complete a minimum of 30 credit hours at Franklin to graduate. Most of our students arrive with credit already earned somewhere else, so it is worth finding out what yours is worth before you plan anything.Transferring credit at Franklin →

Looking at the master’s instead? See M.S. in Business Analytics.

M.S. in Business Analytics

Franklin’s M.S. in Business Analytics prepares working professionals and career changers to turn data into decisions — building job-ready skills in SQL, Python, Tableau, and AI in as few as 12 months, 100% online. 32 credit hours, no GRE or GMAT.

What makes it different

The curriculum

Course What it adds
MATH 601 – Introduction to Analytics Statistics, inference, regression, data ethics
DATA 630 – Applied Database Management SQL, relational design, data warehousing
DATA 605 – Data Visualization & Reporting Tableau, dashboards, data storytelling
DATA 610 – Big Data Analytics & Data Mining Python, data mining, machine learning
BUSA 603 – Marketing Management & Analytics AI-based marketing analytics, campaign and ROI analysis
BUSA 604 – Financial Decision Modeling Excel, financial modeling, forecasting, Monte Carlo
BUSA 605 – Business Analytics Strategy AI-driven strategic framing, analytics strategy, case studies
BUSA 695 – Capstone in Business Analytics An applied AI project with a real client, plus a portfolio

Six of the eight are aimed at a business decision rather than at a technique for its own sake. That is the difference from the bachelor’s in one line.

Looking at the bachelor’s instead? See B.S. in Analytics.

May I know more about the Business Analytics Program at Franklin University?

Our Business Analytics Program has achieved national recognition, ranking #8 by TechGuide among online programs. Watch my comprehensive Program Orientation for detailed insights into curriculum, career outcomes, and program benefits.

Key Program Highlights:

Admission

Franklin’s requirements are on the university’s admission page, and they are the authority — not this page.

If your transcript is borderline, email me at jiang.li2@franklin.edu before you rule yourself out. Work experience counts for something, and I would rather look at your case than have you not apply.

Where these degrees lead

Graduates of both programs go into the same broad set of analyst occupations. The difference is where you enter and how fast you move, not which door you walk through.

Rather than summarize that here, the Curriculum to Careers page carries the detail: each course mapped to the skills and tools it teaches and the job roles it supports, the national salary and growth figures for those occupations, and what employers actually ask for in job postings. It is the single source for those numbers, so they only have to be right in one place.

Common questions

Is analytics a good fit for me?

Analytics is ideal if you have:

✅ Intense curiosity to answer business questions
✅ Passion for exploring hidden patterns in data
✅ Enjoyment in solving new business problems
✅ Interest in data-driven decision making

❌ Not recommended if you:

Do I need a strong math background?

Short answer: Not really. Here’s what you need to know:

Example: Understanding Linear Regression

Our Philosophy: We teach analytics as an applied discipline. You learn to solve real business problems using proven methods and tools, rather than deriving mathematical formulas.

Mathematical Foundation Needed:

Do I need strong programming skills?

Absolutely not! Our approach focuses on problem-solving using existing tools rather than software engineering.

Analytics Programming vs. Software Engineering:

Our Philosophy: We teach you to be analytics problem-solvers, not programmers.

What is Business Analytics and how does it differ from Data Analytics?

Both are applied Data Science fields, but with different emphases and technical depth:

🏢 Business Analytics

📊 Data Analytics

Key Difference: Business Analytics emphasizes applying data insights to business contexts for stakeholder decision-making, while Data Analytics focuses on technical precision and advanced statistical modeling for deeper analytical exploration.

Common Foundation: Both programs teach data manipulation, statistical analysis, and insight generation - the difference is in application focus and technical depth.

What analytics tools should I learn?

Most Important Principle: Ability to learn new tools > mastery of specific tools

🎯 Key Philosophy: Analytics tools evolve rapidly. The software popular today may be different in five years. Focus on methodology and adaptability rather than tool-specific expertise.

Methodology > Tools: Understanding the data mining process and algorithm intuition allows you to solve problems across platforms.

Recommended Tool Exposure by Category:

Spreadsheet Tools:

Database Querying:

Data Visualization:

Programming Languages:

Why These Tools Matter:

Strategy: Gain exposure to tools from each category to position yourself effectively in the job market. The goal is understanding when and why to use each tool, not becoming an expert in all of them.