Academic Foundations

    • Analyzing revenue, costs, and profitability

    • Understanding and evaluating business models

    • Applying strategic analysis frameworks (e.g., SWOT, Porter’s Five Forces, PESTLE)

    • Interpreting financial and operational data

    • Identifying innovation and competitive advantage

    • Working with case studies to solve real-world business problems

    • Communicating business insights clearly

    • Understanding financial accounting principles and terminology

    • Reading and interpreting financial statements (balance sheet, income statement, cash flow)

    • Analyzing business performance using financial data

    • Applying the concept of time value of money

    • Performing basic financial ratio analysis

    • Gaining awareness of limitations in conventional accounting systems

    • Strengthening quantitative and numerical analysis skills

    • Analyzing business requirements

    • Assess, research, analyze, and document stakeholder needs

    • Understanding key global economic concepts and their business impacts

    • Communicating complex ideas clearly and effectively

    • Critical thinking and analysis of global economic issues

    • Strengthening business writing and discussion skills

    • Understanding motivation, performance, and workplace behavior

    • Managing stress, conflict, and effective communication

    • Analyzing team dynamics and decision-making processes

    • Applying strategies to enhance employee engagement and collaboration

    • Understanding supply, demand, and price mechanisms

    • Analyzing costs, productivity, and market structures

    • Applying theories of competition, monopoly, and firm behavior

    • Interpreting economic factors affecting business decisions

    • Understanding government finance and budgeting principles

    • Analyzing public policies on health care, debt, and social insurance

    • Evaluating trade, taxation, and redistribution policies

    • Applying economic reasoning to public sector decision-making

    • Writing basic Python programs and algorithms

    • Using control structures, recursion, and file handling

    • Debugging and measuring algorithm performance

    • Navigating the terminal with shell commands

    • Applying programming for problem-solving

    • Implementing and analyzing fundamental algorithms

    • Understanding memory management and data structures

    • Applying object-oriented programming and software design principles

    • Performing algorithm complexity analysis

    • Using debugging tools and shell commands

    • Ensuring program correctness and file handling

    • Mastered core OOP principles: encapsulation, inheritance, polymorphism

    • Developed Java console applications from scratch

    • Built RESTful web services with Spring Boot and HTTP request handling

    • Applied common design patterns for clean, maintainable code

    • Practiced interface design, defensive programming, and code-smell detection

    • Implemented robust exception handling and systematic debugging

    • Implementing stacks, queues, lists, search trees, and hash tables

    • Applying efficient sorting algorithms

    • Analyzing time and space complexity

    • Using object-oriented programming for data structure design

    • Performing experimental evaluation of algorithms

    • Designing and analyzing efficient algorithms

    • Applying greedy, divide & conquer, and dynamic programming techniques

    • Understanding network flow algorithms

    • Performing complexity analysis and optimization

    • Gaining foundational knowledge of NP-completeness

    • Applying software development life cycle (SDLC) methodologies

    • Writing requirements, analysis, and design documentation

    • Implementing and testing software in iterative cycles

    • Collaborating on team-based software projects

    • Understanding project management fundamentals

    • Practicing version control and maintenance processes

    • Understanding data models and logical data representations

    • Working with file systems and database systems

    • Applying basic document retrieval techniques

    • Learning fundamentals of database administration and security

    • Gaining familiarity with data dictionaries and metadata management

    • Clear, concise technical writing

    • Persuasive communication strategies

    • Audience adaptation for diverse technical and non-technical groups

    • Critical thinking on social and ethical issues in technology

    • Confident presentation skills

    • Issue framing and structured argumentation

    • Collaborative writing and teamwork

    • Incorporating feedback to refine deliverables

    • Project scoping and concise summarization

    • Ethical persuasion and data-governance awareness

    • Exponential & logarithmic growth-decay functions

    • Trigonometric functions and periodicity analysis

    • Limits, continuity, and convergence concepts

    • Derivatives and sensitivity/rate-of-change measures

    • Logarithmic and implicit differentiation techniques

    • Mean Value Theorem and error-bound insights

    • Optimization via extrema identification

    • Curve sketching, parametric curves visualization

    • Newton’s method for numerical root finding

    • Introductory differential-equation modeling

    • Polar coordinates and multidimensional thinking

    • Riemann sums and numerical integration foundations

    • Fundamental Theorem of Calculus (linking integrals and derivatives)

    • Definite, indefinite, and improper integrals

    • Integration techniques and real-world applications

    • First-order separable differential equations and growth models

    • Sequences and series fundamentals

    • Convergence tests for infinite series

    • Power-series representations and interval of convergence

    • Applications of power series in analysis and modeling

    • Solving linear equations with matrix methods

    • Matrix operations and determinant computation

    • Fundamentals of vector spaces and linear transformations

    • Basis, dimension, and coordinate representations

    • Complex numbers in linear-algebra contexts

    • Eigenvalues, eigenvectors, and diagonalization

    • Inner products, norms, and orthogonality

    • Gram–Schmidt process and orthonormal bases

    • Least-squares solutions to over-determined systems

    • Application-oriented matrix and vector calculations

    • Core concepts of graph theory (vertices, edges, paths, cycles)

    • Tree structures, properties, and traversal algorithms

    • Proof techniques: basic and strong mathematical induction

    • Automata theory foundations: DFA, NFA, and regular languages

    • Formal reasoning and logical deduction frameworks

    • Modular arithmetic: congruences, inverses, and number-theoretic properties

    • Introduction to combinatorial counting on graphs and trees

    • Basic notions of computability and language classification

    • Advanced graph theory concepts and algorithms

    • Tree properties, spanning trees, and traversal techniques

    • Inclusion–exclusion principle for combinatorial counting

    • Generating functions for sequence analysis

    • Solving and modeling with recurrence relations

    • Optimization techniques on graphs

    • Matching theory and algorithms (e.g., bipartite matching, stable matching)

    • Linear-programming model formulation

    • Converting problems to standard form

    • Simplex method fundamentals

    • Revised and dual simplex variants

    • Duality theory and economic interpretation

    • Sensitivity / post-optimality analysis

    • LP applications using specialized software

    • Introduction to game-theoretic LP models

    • Network simplex algorithm for flow optimization

    • Basics of convex sets and polyhedral geometry

    • Data-acquisition basics (APIs, simple web scraping)

    • Core ETL and data-cleaning steps

    • Relational vs. NoSQL storage fundamentals

    • Querying large datasets with SQL/distributed tools

    • Building straightforward visualizations/dashboards

    • Intro to data privacy and security best practices

    • R syntax essentials and RStudio workflow

    • Data import from common formats (CSV, Excel, web)

    • Data cleaning and tidying with dplyr / tidyr

    • Exploratory visualizations using ggplot2

    • Basic data transformations and summaries

    • Handling categorical, numeric, and date-time types

    • Probability axioms and event operations

    • Discrete & continuous random variables; mean & variance

    • Key distributions: Binomial, Poisson, Normal, t, χ²

    • Sampling distributions and the Central Limit Theorem

    • Point estimation and basic estimator properties

    • Confidence intervals for means and proportions

    • Introductory hypothesis tests (z, t, χ²)

    • Simple applied examples linking data to inference

    • Multiple linear regression modeling and interpretation

    • Analysis of variance (ANOVA) for comparing group means

    • Analysis of covariance (ANCOVA) to adjust for continuous covariates

    • Checking model assumptions and diagnostics (normality, homoscedasticity, multicollinearity)

    • Variable selection and interaction‐term evaluation

    • Distinguishing observational vs. experimental study designs

    • Interpreting effect sizes, p-values, and confidence intervals

    • Communicating results with clear tables and plots

    • Simple, stratified, systematic, and cluster sampling

    • Ratio and regression estimators; variance estimation

    • Sample‐size determination using Power Study and precision targets

    • Designing surveys and handling non-response weighting

    • Randomized block and factorial experiments

    • Split-plot and other intermediate experimental layouts

    • Principles of randomization, replication, and blocking

    • ANOVA methods for analyzing designed experiments

    • Overview of real-world statistics career paths

    • Insights into day-to-day responsibilities across industries

    • Networking with guest professionals and alumni

    • Guidance on essential technical and soft skills employers seek

    • Exposure to emerging application areas for statisticians

    • Tips on internships, co-ops, and early career planning

    • Q&A sessions for personalized career advice

    • Reflection on professional ethics and workplace expectations

    • Install and navigate the SAS environment

    • Import data with the DATA step, handle custom formats, create derived variables, export results

    • Access external databases with PROC SQL / LIBNAME

    • Reshape data: sort, subset, merge, transpose, DO loops, arrays, attribute edits

    • Explore data with core PROCs (PRINT, MEANS, FREQ, UNIVARIATE, TABULATE, PLOT)

    • Use BY-group processing for segmented analyses

    • Capture results via the Output Delivery System (ODS)

    • Generate simulated datasets for testing and modeling

    • Close reading and textual analysis across poetry, fiction, drama, and non-print media

    • Key literary theories (e.g., feminist, post-colonial, ecocritical) for interpreting texts

    • Connections between literature, film, social media, and popular culture

    • Writing clear, thesis-driven essays with evidence-based arguments

    • Examining contemporary themes such as identity, globalization, and digital culture

    • Developing comparative skills to trace motifs across genres and media

    • Practicing peer review and constructive feedback on written work

    • Global health disparities and drivers

    • Health needs of vulnerable sub-populations worldwide

    • Comparative health-care systems across nations

    • Cross-border transmission pathways for disease

    • Case studies: SARS, avian flu, West Nile, BSE, drug-resistant malaria/TB

    • Economic and societal impacts of emerging infections

    • Interdependence of rich and poor nations’ health outcomes

    • Anticipating and mitigating future global health risks

    • Core principles of 2-D visual design (layout, typography, color)

    • Sequential art & basic animation workflows

    • Fundamentals of digital photography (exposure, composition, editing)

    • Vector-image creation and illustration techniques

    • Hands-on projects using industry-standard design software

    • Storytelling and concept development for new-media contexts

    • Critique methods and iterative design process

    • Collaboration and project management in creative teams

    • Applying design theory to contemporary digital-media practice