Business Analytics

Career Profile (Salary, Job Titles, Grad School Data)

Business analytics is the practice of using data, statistical and quantitative analysis, and predictive modeling to help organizations make data-driven decisions. Data management, data visualization, predictive modeling, data mining, forecasting simulation, and optimization are some of the tools used to create insights from data. Yet, while business analytics leans heavily on statistical, quantitative, and operational analysis, developing data visualizations to present your findings and shape business decisions is the end result. For this reason, balancing your technical background with strong communication skills is imperative to do well in this field.

Analyst Pathways

  • Business Analyst

    Business analysts are professionals who work to identify and analyze business problems and opportunities and then design solutions to help organizations improve their operations, processes, and systems.

    Banks, insurance companies, and investment firms hire business analysts to work on projects related to risk management, product development, and customer acquisition. Companies like Goldman Sachs, JPMorgan Chase, and American Express are examples of financial services companies that hire business analysts.

    Technology companies like Google, Amazon, and Microsoft hire business analysts to work on projects related to product development, marketing, and customer analytics.

    Healthcare companies like UnitedHealth Group, CVS Health, and Anthem hire business analysts to work on projects related to healthcare operations, strategy, and product development.

    Retail companies like Walmart, Target, and Amazon hire business analysts to work on projects related to pricing, supply chain management, and customer analytics.

  • Data Analyst

    Data analysts collect, clean, and analyze data to help companies make informed decisions. They may also create visualizations and reports to communicate their findings to stakeholders.

  • Business Intelligence Analyst

    Business intelligence analysts use data to help organizations identify trends and patterns and make strategic decisions. They may also develop dashboards and reports that provide insights to key stakeholders.

  • Data Scientist

    Data scientists use advanced statistical and machine learning techniques to analyze large datasets and uncover insights. They may also develop predictive models and algorithms to help companies make data-driven decisions.

  • Marketing Analyst

    Marketing analysts use data to help companies understand their customers and develop effective marketing strategies. They may also analyze marketing campaigns and provide insights to improve performance.

  • Operations Analyst

    Operations analysts use data to identify inefficiencies and improve processes within organizations. They may also develop models to forecast demand and optimize supply chain management.

  • Management Consultant

    Management consultants work with companies to identify areas for improvement and develop strategies to achieve their goals. They may use data to inform their recommendations and help companies make data-driven decisions. Companies like McKinsey, Bain, and Boston Consulting Group (BCG) hire business analysts to work with clients on strategic projects. Business analysts at these firms typically work on a variety of projects across different industries.

  • Entrepreneur

    Business analytics graduates may choose to start their own business, using data to identify opportunities and make informed decisions.

 

Data Science vs. Business Analytics vs. Information Systems

Data Science

Data Science majors typically focus on the extraction of knowledge and insights from structured and unstructured data. This involves statistical analysis, machine learning techniques, and programming skills to process and analyze large datasets.

 

Skillset: Students in Data Science programs develop proficiency in programming languages such as Python or R, statistical analysis, data visualization, and machine learning algorithms.

 

Career Paths: Graduates of Data Science programs often pursue careers as data scientists, machine learning engineers, data analysts, or research scientists in industries such as technology, finance, healthcare, and consulting.

Business Analytics

Business Analytics majors emphasize the use of data analysis and quantitative methods to inform strategic business decisions. This involves understanding business processes, identifying key performance indicators, and utilizing data-driven approaches to optimize operations and improve outcomes.

 

Skillset: Students in Business Analytics programs develop skills in data modeling, predictive analytics, data visualization, and business intelligence tools. They also gain knowledge in areas such as operations management, marketing analytics, and supply chain optimization.

 

Career Paths: Graduates of Business Analytics programs often pursue careers as business analysts, data analysts, consultants, or analytics managers in various industries such as finance, marketing, operations, and consulting.

Information Systems

Information Systems majors focus on the design, implementation, and management of technology systems to support business processes and decision-making. This involves understanding both technical and business aspects of information technology and its integration within organizations.

 

Skillset: Students in Information Systems programs develop skills in database management, systems analysis, cybersecurity, enterprise architecture, and project management. They also learn about emerging technologies such as cloud computing, blockchain, and artificial intelligence.

 

Career Paths: Graduates of Information Systems programs often pursue careers as systems analysts, IT consultants, database administrators, cybersecurity specialists, or IT project managers in industries such as technology, healthcare, finance, and government.

How to Land Your First Analytics Job

Use Keywords

When using job search platforms, such as Handshake, utilize keywords to find appropriate positions in the industries you are interested in. Below are sample job titles.

1. Business Analyst

2. Data Analyst

3. Operations Analyst

4. Marketing Analyst

5. Sales Analyst

6. Financial Analyst

7. Pricing Analyst

8. Customer Service Analyst

9. Supply Chain Analyst

10. Business Intelligence Analyst

11. Consultant

12. Management Analyst

13. Market Research Analyst

14. Programming and Marketing Manager

15. Business Intelligence Consultant

16. Product Analyst

17. Retail Analyst

18. Business Technology Analyst

19. Advisory Analyst

20. Consulting Analyst

 

Use the Right Resources

A simple way to get started is by learning more about the companies that have hired Gies students in the past. Once you have identified your target companies, follow these companies on Handshake to be informed about opportunities and info sessions, in which you can network with recruiters and professionals. Below are employers who have hired Gies students.

Information coming soon!

 

Customize your Resume & Cover Letter

It's important to customize your application documents for the industry/position you will be pursuing. Highlight your relevant experience by incorporating desired skills and qualifications into your application documents. How does your experience align with the skills the employer is seeking?

Some of the important skills you may want to highlight:

Soft Skills

  • Problem-solving skills
  • Communication
  • Presentation skills
  • Attention to detail
  • Critical thinking
  • Innovation
  • Project management

Hard Skills

  • Data visualization (Power BI, Tableau, Looker, Qlik Sense, MicroStrategy)
  • Relational and cloud databases (ERP, BI &RDMS Tools: SAS, SAP, ASE/Business Objects, MS SQL Server,  DB2, IBM Cognos, AWS)
  • Programming languages (SQL, Python, MySql, Oracle)
  • Product management
  • Data warehousing
  • Statistical analysis tools (SPSS, SAS)
  • Quantitative analysis
  • Analytics reports & dashboard development
  • Business intelligence & reporting
  • Data cleaning
  • Data wrangling
  • Excel

Learn more about desired professional skills here.

Sample Interview Questions for Analyst Roles

These questions are designed to assess a candidate's experience, skills, and approach to problem-solving. It's important for candidates to provide specific examples from their work experience to demonstrate their qualifications for the role.

  1. Can you describe your experience with requirements gathering and documentation?
  2. How do you approach problem-solving and decision-making?
  3. What methodologies have you worked with in the past, such as Waterfall or Agile?
  4. Can you walk us through a project you recently completed, including the challenges you faced and how you overcame them?
  5. How do you ensure that stakeholders have a clear understanding of project requirements and progress?
  6. Have you worked on a project where you had to make trade-offs between competing requirements or constraints? How did you handle it?
  7. Can you describe your experience with data analysis and reporting?
  8. How do you prioritize tasks and manage your workload in a fast-paced environment?
  9. Can you give an example of how you have communicated complex technical concepts to non-technical stakeholders?
  10. How do you stay current with industry trends and best practices in business analysis?
  11. How would you work with a difficult stakeholder?
  12. What tools do you consider the most important for a business analyst to do their job well?
  13. Give an example of a time when you failed to meet a project deadline. How did you overcome the situation?

Technical Interviews

Data analyst interviews often include a technical interview to assess a candidate's technical skills and knowledge. The technical interview may include questions about SQL, data manipulation and cleaning, data visualization, statistical analysis, and other technical topics relevant to the job. It's important for candidates to provide specific examples from their academic or work experience to demonstrate their qualifications for the role.

One of the best tips is this: Work backwards when you create a study plan for your analyst interview. Here's how:

  1. Research the interview format by asking the recruiter for direction or searching for interview experiences and interview guides.
  2. Mine the job description for the exact skills the company is looking for, usually a mix of SQL, business sense, analytics, and visualization.
  3. Build your business sense by researching the company. Think about the business model, who the customers are, and the KPIs you would use to monitor the health of the company.
  4. Study and practice real interview questions. For SQL, you'd want to practice easy, medium and hard questions, and for business sense, you'd want to focus on business and analytics case studies.
  5. Conduct some mock interviews to simulate the process. It's difficult to do by yourself. Working with a peer, colleague or coach will help you determine where you struggle and what needs work.


Here are some example technical interview questions for a business analyst:

  1. Can you explain your experience working with SQL and querying data from databases?
  2. How have you worked with software development methodologies such as Agile or Waterfall?
  3. Can you describe your experience with data modeling and database design?
  4. Have you worked with any programming languages? If so, which ones and how have you used them?
  5. Can you describe your experience with project management software, such as Jira or Trello?
  6. How do you handle data validation and ensure data quality?
  7. Can you walk us through a specific project where you had to extract and analyze data from multiple sources?
  8. Can you describe your experience with data visualization tools, such as Tableau or Power BI?
  9. How have you worked with business intelligence software, such as SAP or Oracle?
  10. Can you explain a technical challenge you faced in a previous project, and how you solved it?


Here are some example technical interview questions for a data analyst:

  1. Can you write a SQL query to extract data from a database?
  2. How do you handle missing or incomplete data in your analysis?
  3. Can you explain a statistical concept like correlation or regression to a non-technical person?
  4. Can you describe your experience with data visualization tools like Tableau or Power BI?
  5. How do you clean and transform data for analysis?
  6. Can you explain a hypothesis test and how it can be used to make decisions?
  7. How have you used Excel or other spreadsheet software for data analysis?
  8. Can you describe a time when you had to work with a large dataset?
  9. How do you ensure the quality and accuracy of your data?
  10. Can you describe how you would approach a new data analysis project, from data acquisition to report delivery?
 

What to Study for Technical Interviews

Technical Skills

SQL is a must-have skill for analyst roles, and fortunately, practicing SQL is straightforward. You start with easy and medium difficulty problems and work your way up to advanced problems.

During the interview, your goal is to write clean code as efficiently as possible. One good way to practice this is benchmarking your progress. Time how long it takes to complete a problem, and see if you can continue to improve your speed in questions at that level. Most 30-minute technical screens for data analyst roles include 1-2 medium-level SQL questions; therefore, that should be a goal in your prep.

Beyond SQL, the job description will offer clues about other technical subjects to study. Some of the most common include:

  • Tableau
  • Data visualization tools
  • Microsoft Excel
  • Python or R
  • Algorithms
  • A/B testing and experimentation

Business Sense

Strong SQL skills will only get you so far; you also need to have solid business intuition. And that's a more difficult skill to study for.

In particular, you should know:

  • The most important KPIs to measure business health
  • The right metrics to choose for a business problem


Developing business sense starts with understanding the company. If you know the business model, understand how the company can make or lose money, and understand how the business acquires customers, defining metrics becomes easier.

Data Intuition

Data intuition can be defined as your ability to read numbers and make sense of them quickly. An analyst with good data intuition understands when a conversion rate is low, or if the numbers look off. In other words, they know where to look in the data to find a solution.

Like business sense, you can develop this skill by practicing analytics case and product metrics case questions. This will help you practice diving into data problems and communicating how you would approach the problem.

Additionally, it's helpful to brush up on statistics and probability concepts. For example, you should be able to talk confidently about causality vs correlation, P-values, confidence intervals, etc.

Technical Interview Resources

Here are some resources that can be helpful for students preparing for technical interviews for data analytics roles:

  • LeetCode: LeetCode is a popular platform for coding interview preparation. It offers a range of practice problems with solutions in various programming languages. You can also find specific interview questions for data analytics roles on the platform.
  • HackerRank: HackerRank is another popular coding platform with a focus on technical assessments and interviews. You can find data analytics questions and coding challenges on the platform.
  • Cracking the Coding Interview: Cracking the Coding Interview is a popular book by Gayle Laakmann McDowell that provides guidance on how to prepare for technical interviews. The book covers a range of interview topics, including data analytics, and provides sample questions and solutions.
  • Kaggle: Kaggle is a data science community that offers datasets, competitions, and resources for data scientists. Participating in Kaggle competitions and solving their data analytics problems can be a great way to gain practical experience and prepare for technical interviews.
  • DataCamp: DataCamp is an online learning platform that offers courses in data science, including data analytics. Their courses include coding exercises and quizzes that can help you practice your skills and prepare for technical interviews.