Admission Requirements:

• Academic Qualification: Completion of 12th grade from a recognized board with a minimum of 65%.
• Age Requirement: Must be at least 17 years old at the time of application.
• Entrance Exam: This may be required depending on the university's policy.
• Language Proficiency: English proficiency may be required for international students.
• Educational Background: SSC & HSC should be from a Business stream.

The University of Georgia does not require any kind of standardized test for specific programs.

English Language and Entrance Requirements:

• English Language Proficiency:
  • B1 Level (70%) or equivalent is required.
  • International certificates accepted to confirm English proficiency include:
    • TOEFL iBT: Minimum score of 70
    • IELTS: Minimum score of 4.5
    • PTE General: Minimum level B1
    • FCE: Minimum level B
    • CPE: Satisfactory pass
    • CAE: Satisfactory pass
• Unified National Entrance Examinations:
  • Georgian citizens must pass the Unified National Entrance Examinations to enter the program.
  • Foreign high-school graduates who received complete general education abroad or its equivalent, or who lived abroad for the last two years, can enter the program without taking the Unified National Entrance Examinations according to Georgian education legislation (Order №224/N of the Minister of Education and Science of Georgia, December 29, 2011).
• University of Georgia English Test:
  • For students who do not have recognized English proficiency certificates, the University of Georgia will conduct a B1 level test. A score of 70% or higher will be considered proof of English language competence.

Program Learning Outcomes:

1. Business Management & Analytics: Describes key functional areas of business management and discusses the fundamental concepts and tools of modern business analytics.
2. Organizational Analysis: Analyzes organizational activities and business environments using SWOT and PESTEL analyses.
3. Mathematical & Statistical Tools: Appropriately applies mathematical, statistical, and probabilistic tools for solving business problems, including data analysis and visualization techniques.
4. Business Problem Solving: Utilizes key tools of business analytics to identify business challenges and proposes solutions, including graphical representations of business processes for decision-making.
5. Financial Accounting: Constructs financial sources and evaluates profitability by analyzing company financial ratios in compliance with financial accounting standards.
6. Research & Practical Project: Develops and presents research or practical projects using appropriate technologies.
7. Teamwork & Leadership: Demonstrates leadership and/or cooperation principles while working effectively in teams.
8. ICT Skills: Utilizes modern information and communication technologies in business analytics tasks.
9. Social Responsibility & Ethics: Evaluate the role of social responsibility and corporate ethical norms in business practices.
10. Professional Development: Identifies personal professional interests and recognizes the need for continuous learning to advance to the next level of education.

Career Opportunities in Business Analytics:

1. Business Analyst
   • Role: Gathers and interprets data to help businesses improve processes, products, and services.
   • Skills: Data analysis, SQL, Excel, communication, problem-solving.
   • Industries: All sectors, including finance, healthcare, retail, tech, and manufacturing.
2. Data Analyst
   • Role: Interprets raw data and turns it into actionable insights for business decisions.
   • Skills: Data mining, statistical analysis, SQL, R, Python, Excel.
   • Industries: E-commerce, finance, technology, sports, marketing.
3. Data Scientist
   • Role: Uses machine learning and statistical modeling to analyze large datasets and extract predictive insights.
   • Skills: Machine learning, programming (Python, R), deep statistical knowledge, data visualization.
   • Industries: Technology, healthcare, finance, retail, marketing.
4. Operations Analyst
   • Role: Works with data to optimize internal operations, improve efficiency, and reduce costs.
   • Skills: Process optimization, data modeling, statistical analysis, project management.
   • Industries: Manufacturing, logistics, consulting, finance.
5. Financial Analyst (with Business Analytics focus)
   • Role: Focuses on forecasting, budgeting, and analyzing financial data to provide strategic insights.
   • Skills: Financial modeling, data analysis, Excel, forecasting, data visualization.
   • Industries: Banking, investment, corporate finance, consulting.
6. Marketing Analyst
   • Role: Understands consumer behavior, evaluates market trends, and analyzes marketing strategies.
   • Skills: Marketing knowledge, data analysis, Google Analytics, and data visualization.
   • Industries: Retail, e-commerce, tech, advertising, media.
7. Product Analyst
   • Role: Gathers data on customer usage, preferences, and feedback to guide product development strategies.
   • Skills: Data analysis, product lifecycle knowledge, communication, data visualization.
   • Industries: Tech, e-commerce, consumer goods.
8. Supply Chain Analyst
   • Role: Uses data to optimize logistics, inventory, and procurement processes.
   • Skills: Supply chain management, statistical analysis, problem-solving, data visualization.
   • Industries: Retail, manufacturing, logistics, pharmaceuticals.
9. Business Intelligence (BI) Analyst
   • Role: Analyzes data to provide actionable insights through dashboards and reporting tools.
   • Skills: BI tools (Power BI, Tableau), SQL, data visualization, communication.
   • Industries: Any industry relying on data for decision-making.
10. Consultant (Business Analytics)
    • Role: Advises businesses on leveraging data to improve operations, reduce costs, and drive strategy.
    • Skills: Analytical skills, problem-solving, industry knowledge, and communication.
    • Industries: Consulting firms, finance, tech, healthcare.
11. Machine Learning Engineer
    • Role: Develop models and algorithms to automate data analysis and prediction.
    • Skills: Programming (Python, R), machine learning algorithms, data analysis, statistics.
    • Industries: Technology, finance, healthcare, marketing.
12. AI/Data Engineer
    • Role: Builds and maintains infrastructure and systems for handling large datasets.
    • Skills: Data architecture, programming (Python, Java), database management, cloud services.
    • Industries: Tech, e-commerce, finance, healthcare.

Growth & Demand:

• The demand for business analysts, data scientists, and analytics professionals is increasing due to the growing reliance on data for decision-making.
• Companies are hiring specialized roles that integrate analytics with business strategy, product management, and marketing.
• Skills in machine learning, AI, and big data technologies are highly sought after, leading to senior roles in the analytics field.

Career Pathways:

• Entry-Level: Data Analyst or Junior Business Analyst, gaining expertise in tools and business knowledge.
• Mid-Level: Senior Business Analyst, Data Scientist, or Manager of Business Analytics, leading teams and projects.
• Advanced-Level: Chief Data Officer (CDO), Director of Business Analytics, or Chief Analytics Officer (CAO), driving company-wide data strategies.

Teaching Methods in Business Analytics Program:

• Lecture-Seminars: Traditional lectures combined with seminar discussions to engage students and deepen understanding.
• Explanatory Method: Detailed explanation of key concepts and theories to ensure clarity.
• Demonstration Method: Showing practical examples or live demonstrations of concepts and tools used in business analytics.
• Interactive Lectures: Encouraging student participation during lectures through questions, discussions, and problem-solving.
• Discussion/Debates: Facilitating group discussions and debates to help students critically evaluate different viewpoints and approaches.
• Team Work: Collaborating with peers on projects or assignments to develop teamwork and communication skills.
• Case Study and Analysis: Analyzing real-world business problems through case studies to apply theoretical knowledge.
• Problem Solving Exercises: Engaging in practical exercises to solve business-related problems using analytical tools.
• Action-Oriented Learning: Learning through doing, where students actively engage in tasks or projects to reinforce their learning.
• Project-Based Learning: Working on long-term projects that simulate real business scenarios, allowing students to apply what they’ve learned.
• Literature Review: Research existing literature to understand trends, best practices, and developments in business analytics.
• Written Work Methods: Preparing written assignments, reports, and research papers to develop communication and analytical writing skills.
• Work Process Simulation: Simulating real business workflows to understand processes and optimize strategies.
• Practical Studies: Hands-on practice and fieldwork, where students apply concepts in real or simulated environments.

These methods collectively support an immersive, practical, and collaborative learning experience in the field of business analytics.