Analytics FAQ

Q1: 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:

  • Industry-experienced faculty
  • Practical, hands-on learning
  • Strong job placement rates
  • Flexible online format
  • Real-world capstone projects

Q2: Is Business Analytics a good fit for me?

Business 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:

  • Prefer routine, day-to-day work without variation
  • Are not interested in data-based problem-solving
  • Dislike analytical thinking or quantitative reasoning

Q3: Do I need a strong math background?

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

Example: Understanding Linear Regression

  • You need to understand that we’re finding the best line through data points
  • Why this line helps us make predictions about new data
  • Applied approach: You can build effective models using software tools without knowing the mathematical derivations
  • The math behind it: Understanding calculus and linear algebra explains how the computer finds the best line, but you don’t need this to use regression effectively

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:

  • Essential: Basic statistics understanding and logical thinking
  • Helpful but not required: Linear algebra, calculus
  • Reality: You can successfully complete analytics projects and build your career without advanced mathematics

Q4: 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:

  • ✅ Analytics Focus: Problem-solving with well-developed packages
  • ✅ Tool Utilization: Leveraging R, Python, Tableau, SAS functions
  • ✅ Student Success: Complete predictive analysis projects without writing loops
  • ❌ Not Required: Writing code like software engineers

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

Q5: 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

  • Primary Focus: Solving business problems using data
  • Goal: Support decision-making and improve business outcomes
  • Questions: What happened? Why did this happen? What should we do?
  • Approach: Business-oriented with moderate technical depth
  • Tools: Excel, SQL, Python, Power BI/Tableau, dashboards
  • Output: Actionable insights, KPIs, business recommendations
  • Audience: Business users, managers, decision-makers

📊 Data Analytics

  • Primary Focus: Understanding and exploring data in technical depth
  • Goal: Generate insights, identify patterns, and build predictive models
  • Questions: What trends exist? What can we predict? How accurate is the model?
  • Approach: Technical/methodological with higher statistical rigor
  • Tools: SQL, Python/R, advanced statistics, ML libraries, Jupyter notebooks
  • Output: Statistical reports, model predictions, pattern detection
  • Audience: Analysts, technical teams, data scientists

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.

Q6: 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:

  • Excel: Widely used for modeling, scenario analysis, and quick calculations

Database Querying:

  • SQL: Core skill for accessing and manipulating structured data (essential)

Data Visualization:

  • Power BI: Common in business settings, integrates well with Microsoft ecosystem
  • Tableau: Strong in design and advanced visualizations

Programming Languages:

  • Python: Most popular for analytics and data science; strong in automation, ML, and dashboard development
  • R: Preferred in research and academia; excellent for statistical analysis and specialized visualizations

Why These Tools Matter:

  • Industry Demand: High job market requirements across sectors
  • Versatility: Each serves different analytical needs and contexts
  • Learning Value: Skills transfer between similar tools
  • Career Growth: Exposure to multiple tools increases opportunities

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.

Q7: What career opportunities are available after graduation?

Below is a snapshot of where graduates from B.S. in Analytics, M.S. in Business Analytics, and M.S. in Data Analytics programs typically land. Roles marked ★ often prefer—or sometimes require—a master’s degree.

Career Paths by Functional Area:

Business & Strategy

  • Business Analyst, Business Intelligence (BI) Analyst★, Product Analyst
  • Notes: Translate data into business actions; BI roles lean toward the M.S. in Business Analytics

Data & Reporting

  • Data Analyst, Reporting Analyst, Data Visualization Developer
  • Notes: Core roles for B.S. grads; master’s degree can fast-track you to senior analyst positions

Marketing & Customer Insights

  • Marketing Analyst, Customer/CRM Analyst, Growth Analyst
  • Notes: Heavy use of A/B testing and dashboarding; often require storytelling skills

Operations & Supply Chain

  • Operations Research Analyst★, Supply Chain Analyst
  • Notes: Optimization and simulation skills valued; many roles in manufacturing, logistics, and retail

Advanced Modeling & AI

  • Data Scientist★, Machine Learning Engineer★, Quantitative Analyst (FinTech)★
  • Notes: Strong fit for M.S. in Data Analytics; requires solid programming (Python/R) and statistics

Analytics Engineering

  • Analytics Engineer★, Data Engineer (entry-level)
  • Notes: Emerging bridge role: turns raw data into clean, analysis-ready datasets (dbt, SQL)

Governance & Risk

  • Risk Analyst, Compliance Data Analyst, Fraud Analyst
  • Notes: Growing demand in finance, healthcare, and government sectors

Industries Hiring Our Graduates:

  • Technology & Software
  • Finance and Banking / FinTech
  • Healthcare & Life Sciences
  • Retail & E-commerce
  • Manufacturing & Supply Chain
  • Consulting & Professional Services
  • Government & Public Sector
  • Energy & Utilities (data-driven grid management)
  • Sports & Entertainment (ticketing, performance analytics)

Career Advancement Tip: Keep a portfolio (GitHub, Tableau Public, Power BI) showcasing projects that match your target roles—employers increasingly treat it as a second résumé.