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Systems Analysis, Data Analytics, and Management Accounting Course
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Systems Analysis, Data Analytics, and Management Accounting Course

Master the three disciplines that drive modern business performance: systems analysis, data analytics, and management accounting. This course equips you with the tools to diagnose organisational problems, build predictive models, and produce financial reports that inform strategic decisions. From requirements elicitation to capital investment appraisal, every skill is grounded in real business application.

Dedika for businesses

What you will learn:

  • Model organisational systems, define requirements, and assess technical and operational feasibility.

  • Apply descriptive, diagnostic, and predictive analytics techniques to real business datasets.

  • Construct budgets, perform variance analysis, and evaluate product costs using multiple costing methods.

  • Build interactive dashboards and management reports that combine financial and operational insights.

  • Use regression, clustering, and time-series forecasting to support data-driven planning and control.

  • Evaluate capital investments, pricing strategies, and divisional performance using integrated analytical frameworks.

How you study in a practical way Systems Analysis, Data Analytics, and Management Accounting Course

How you practise Systems Analysis, Data Analytics, and Management Accounting Course

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Course content

8 Chapters • 37 LessonsDuration between 4 and 360 hours (you decide)

Chapter 1See details

Foundations of Systems Thinking

  • Lesson 1 • Introduction to Process Modeling

    Introduces flowcharts and basic process diagrams as tools for visualising system behaviour. Prepares students for more advanced modelling techniques in later chapters.

  • Lesson 2 • Core Concepts of Systems Theory

    Introduces open vs. closed systems, entropy, and emergence as applied to organisations. Establishes vocabulary used throughout all subsequent chapters.

  • Lesson 3 • Problem Identification and Scoping

    Teaches structured techniques for defining business problems before analysis begins. Prevents scope creep by establishing clear problem boundaries early.

  • Lesson 4 • Organisational Systems and Structures

    Maps how departments, processes, and data flows form an organisational system. Connects structural design to information needs and decision-making.

Chapter 2See details

Systems Analysis Methods and Tools

  • Lesson 1 • Data Flow and Entity Modeling

    Introduces data flow diagrams and entity-relationship models as complementary analysis tools. Links data structures to business processes identified in Chapter 1.

  • Lesson 2 • Functional and Non-Functional Requirements

    Distinguishes between what a system must do and how well it must perform. Teaches writing precise, testable requirement statements.

  • Lesson 3 • Use Case and User Story Development

    Teaches use case diagrams and agile user stories as user-centred specification tools. Bridges traditional and agile analysis approaches.

  • Lesson 4 • Requirements Elicitation Techniques

    Covers interviews, workshops, surveys, and observation as methods for gathering system requirements. Emphasises selecting the right technique for each stakeholder context.

  • Lesson 5 • Feasibility and Impact Assessment

    Evaluates technical, operational, and financial feasibility of proposed system changes. Produces a structured feasibility report as a deliverable.

Chapter 3See details

Data Foundations for Analytics

  • Lesson 1 • Database Structures and Data Storage

    Explains relational database design, normalisation, and the role of data warehouses. Connects storage architecture to retrieval efficiency for analytics.

  • Lesson 2 • Data Types and Measurement Scales

    Classifies data as nominal, ordinal, interval, or ratio and explains analytical implications of each. Grounds subsequent statistical and visualisation work in correct data handling.

  • Lesson 3 • Data Quality Assessment and Cleansing

    Identifies common data quality issues including missing values, duplicates, and inconsistencies. Applies systematic cleansing procedures to produce analysis-ready datasets.

  • Lesson 4 • Data Governance and Ethics

    Covers data ownership, access controls, privacy principles, and ethical use of personal data. Establishes responsible data practices required in all analytics work.

Chapter 4See details

Descriptive and Diagnostic Analytics

  • Lesson 1 • Root Cause Diagnostic Techniques

    Applies Pareto analysis, fishbone diagrams, and drill-down techniques to identify performance drivers. Bridges descriptive findings to actionable management insights.

  • Lesson 2 • Descriptive Statistics for Business

    Covers measures of central tendency, dispersion, and distribution shape applied to business datasets. Provides the statistical foundation for all subsequent analytical techniques.

  • Lesson 3 • Correlation and Trend Analysis

    Introduces correlation coefficients, scatter plots, and time-series trend identification. Connects pattern recognition to diagnostic questions about business performance.

  • Lesson 4 • Data Visualisation Principles

    Teaches chart selection, design principles, and visual encoding for accurate communication. Prevents common visualisation errors that mislead decision-makers.

  • Lesson 5 • Reporting and Analytical Storytelling

    Structures analytical findings into coherent narratives for non-technical audiences. Teaches the pyramid principle and executive summary writing for business reports.

Chapter 5See details

Predictive Analytics and Forecasting

  • Lesson 1 • Classification and Segmentation Methods

    Introduces logistic regression, decision trees, and cluster analysis for categorising business entities. Connects segmentation outputs to targeted management decisions.

  • Lesson 2 • Model Evaluation and Selection

    Teaches cross-validation, overfitting detection, and model comparison metrics. Ensures students select the most appropriate model for each business context.

  • Lesson 3 • Regression Analysis for Prediction

    Covers simple and multiple linear regression, model assumptions, and interpretation of outputs. Applies regression to cost estimation and revenue forecasting scenarios.

  • Lesson 4 • Time-Series Forecasting Methods

    Applies exponential smoothing, ARIMA concepts, and decomposition to business time-series data. Produces demand and revenue forecasts with quantified uncertainty ranges.

Chapter 6See details

Management Accounting Fundamentals

  • Lesson 1 • Cost Classification and Behaviour

    Distinguishes fixed, variable, semi-variable, and step costs and explains their behaviour across output levels. Establishes cost vocabulary essential for all subsequent accounting topics.

  • Lesson 2 • Cost-Volume-Profit Analysis

    Applies CVP relationships to calculate break-even points, margin of safety, and profit targets. Connects cost behaviour to pricing and volume decisions.

  • Lesson 3 • Performance Measurement Frameworks

    Introduces responsibility accounting, divisional performance metrics, and the balanced scorecard. Aligns financial and non-financial measures to strategic objectives.

  • Lesson 4 • Budgeting and Variance Analysis

    Covers master budget construction, flexible budgeting, and variance calculation for materials, labour, and overhead. Links budget deviations to corrective management actions.

  • Lesson 5 • Costing Methods and Systems

    Compares absorption costing, marginal costing, and activity-based costing for product cost determination. Evaluates the managerial implications of each costing approach.

Chapter 7See details

Integrating Analytics with Management Accounting

  • Lesson 1 • Analytical Budgeting and Forecasting

    Replaces static budget assumptions with data-driven forecasts using time-series and regression models. Produces rolling forecasts that adapt to changing business conditions.

  • Lesson 2 • Working Capital and Cash Flow Analytics

    Uses analytical tools to monitor receivables, payables, and inventory cycles. Connects operational efficiency metrics to cash flow forecasting.

  • Lesson 3 • Profitability and Segment Analysis

    Applies contribution analysis and customer profitability techniques using transactional data. Identifies high-value and loss-making segments for strategic resource allocation.

  • Lesson 4 • Integrated Management Reporting

    Designs management reports that combine financial results with operational and analytical insights. Applies storytelling principles from Chapter 4 to accounting report design.

  • Lesson 5 • Data-Driven Cost Analysis

    Applies regression and clustering to cost data to identify cost drivers and anomalies. Enhances traditional cost analysis with statistical rigour.

Chapter 8See details

Strategic Decision-Making and Advanced Applications

  • Lesson 1 • Capstone Integration Project

    Synthesises all course competencies in a comprehensive case study requiring systems analysis, data analytics, and management accounting outputs. Develops professional-grade deliverables for portfolio use.

  • Lesson 2 • Pricing Strategy and Decision Analysis

    Evaluates pricing approaches including cost-plus, target costing, and value-based pricing using analytical data. Applies decision trees and expected value to pricing under uncertainty.

  • Lesson 3 • Capital Investment Appraisal

    Applies net present value, internal rate of return, and payback methods to evaluate long-term investments. Incorporates risk analysis and sensitivity testing into investment decisions.

  • Lesson 4 • Strategic Performance and Value Creation

    Connects management accounting and analytics outputs to long-term value creation and competitive strategy. Evaluates organisational performance using economic value added and strategic scorecards.

  • Lesson 5 • Transfer Pricing and Divisional Decisions

    Analyses transfer pricing methods and their impact on divisional performance and goal congruence. Evaluates make-or-buy and outsourcing decisions using relevant cost analysis.

Certification

Your valid completion certificate

This course is for you:

  • Finance professionals: ready to add data modelling skills to their accounting toolkit.

  • Business analysts: seeking formal grounding in both systems thinking and financial reporting.

  • Operations managers: wanting to turn process data into measurable performance outcomes.

  • Career changers: moving into analytics or accounting from a non-technical background.

  • Recent graduates: building a competitive, cross-disciplinary skill set for business roles.

  • Accounting technicians: looking to expand their scope into data-driven planning and analysis.

What our students say

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