Mathematical Skills
Below is an overview of mathematical skills relevant to Digital Business Engineers, ordered by priority according to MoSCoW (Must, Should, Could, Won't). For each skill, concrete applications or problems are mentioned where this knowledge is needed in business contexts.
Must (Essential – highest priority)
Data Analysis and Statistical Fundamentals
Understanding basic statistical concepts for business intelligence and data-driven decision making.
Applications
- Analyzing business metrics and KPIs for performance measurement
- Identifying patterns and trends in customer behavior or sales data
- Performing A/B testing to optimize business processes or user experiences
Probability and Risk Assessment
Applying probability concepts in business planning and risk management.
Applications
- Calculating expected ROI for different business initiatives
- Estimating project timeline probabilities and resource allocation
- Modeling business risks and performing sensitivity analysis
Financial Mathematics
Understanding calculations related to business finances, investments, and budgeting.
Applications
- Calculating NPV (Net Present Value) and ROI for business cases
- Creating financial forecasts and budget projections
- Understanding depreciation and asset valuation
Linear Interpolation and Forecasting
Calculating intermediate or future values based on existing data points.
Applications
- Forecasting sales or user growth based on historical data
- Estimating resource needs based on scaling factors
- Creating graduated pricing models or tiered service levels
Basic Algorithmic Thinking
Understanding algorithm efficiency and process optimization.
Applications
- Analyzing business process efficiency and identifying bottlenecks
- Optimizing workflows and decision processes
- Evaluating technology solutions for scalability
Should (Important – second priority)
Understanding Big-O notation and time complexity
Big-O notation is used to describe the efficiency of algorithms and data structures. This helps in optimizing the performance of business applications, particularly in data processing systems.
Applications
- When choosing the right algorithm or data structure for handling business data
- Identifying potential performance issues in database queries or data processing pipelines
- Evaluating the scalability of proposed technical solutions
Data Visualization and Graphing
Translating numerical data into visual representations for better understanding and communication.
Applications
- Creating business dashboards and performance visualizations
- Presenting complex data relationships in an accessible manner
- Designing effective reporting mechanisms for stakeholders
Business Process Modeling
Mathematical representation of business processes for analysis and optimization.
Applications
- Mapping and analyzing business workflows using process modeling notations
- Identifying critical paths and dependencies in business operations
- Simulating process changes before implementation
Regression Analysis
Using statistical methods to examine relationships between variables.
Applications
- Predicting customer behaviors or market trends
- Identifying factors that influence business performance
- Developing pricing models based on multiple variables
Could (Optional – useful in specific/advanced cases)
Machine Learning Fundamentals
Understanding basic principles of machine learning for business applications.
Applications
- Implementing basic predictive models for business forecasting
- Utilizing classification algorithms for customer segmentation
- Applying recommendation systems for product offerings
Network Theory and Graph Algorithms
Using graph theory to solve problems related to connections and relationships.
Applications
- Optimizing supply chain or distribution networks
- Analyzing social networks or organizational structures
- Modeling data relationships in complex business systems
Queuing Theory
Mathematical study of waiting lines and service times.
Applications
- Optimizing customer service resources and staffing levels
- Modeling system capacity requirements for digital services
- Improving throughput in business processes with bottlenecks
Won't (No focus – outside curriculum scope)
Advanced Calculus and Differential Equations
Complex mathematical analysis not typically required for business engineering.
Complex Number Theory
Rarely needed in business application development.
Advanced Machine Learning Mathematics
Deep mathematical foundations of ML algorithms beyond practical application.
Cryptographic Mathematics
Specialized mathematics for security systems, beyond the typical scope of business engineering.