Program 03 · Course 03 · Core Professional Course

Big Data Analytics for Finance & Business Assurance

From Transaction Data to Evidence-Based Professional Decisions.
Large-scale financial and transaction data diubah menjadi analytical evidence, insight, professional judgment, dan solution design yang dapat dipertanggungjawabkan.

OBEBloom B3–B6FinanceBusiness Assurance
Program Position

Program 03 Core Learning Path

Course 01 → Course 02 → Course 03 → AI & Predictive Analytics → Professional Judgment / Impact Challenge

Course Learning Outcomes

6 CLO · Bloom B3 Apply to B6 Create

CLO-03-01 · B4 AnalyzeIdentify and evaluate financial/business data sources, structures, quality, governance, and analytical limitations.

OBE Parent: PLO-03-01
Institutional CPL: INCA-CPL01 / INCA-CPL02

CLO-03-02 · B3 ApplyPrepare, clean, integrate, and transform large financial and transaction datasets for reliable analysis.

OBE Parent: PLO-03-02
Institutional CPL: INCA-CPL02

CLO-03-03 · B4 AnalyzeAnalyze large-scale financial and transaction data to identify patterns, drivers, anomalies, relationships, and risk indicators.

OBE Parent: PLO-03-02
Institutional CPL: INCA-CPL01 / INCA-CPL02

CLO-03-04 · B5 EvaluateDevelop and interpret visual analytics that communicate financial and business implications to relevant stakeholders.

OBE Parent: PLO-03-02
Institutional CPL: INCA-CPL02 / INCA-CPL06

CLO-03-05 · B5 EvaluateEvaluate alternative professional decisions using analytical evidence, uncertainty, risk, and data limitations.

OBE Parent: PLO-03-04
Institutional CPL: INCA-CPL03 / INCA-CPL04

CLO-03-06 · B6 CreateDesign an evidence-based analytical solution for a real or realistic finance/business assurance problem.

OBE Parent: PLO-03-05 / PLO-03-06
Institutional CPL: INCA-CPL05 / INCA-CPL06

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Learning Resources

Textbook Wajib & Bacaan Rekomendasi

Referensi berikut digunakan sebagai landasan sebelum peserta masuk ke delapan modul pembelajaran. Textbook wajib membangun kemampuan accounting analytics dan technical data analysis, sedangkan bacaan rekomendasi memperluas data strategy, architecture, risk/fraud analytics, visualization, statistical learning, dan professional judgment.

WAJIB Required Textbooks

  1. Richardson, V. J., Teeter, R. A., & Terrell, K. L. (2025). Data Analytics for Accounting (4th ed.). McGraw Hill.Primary course reference — accounting analytics, audit, financial analysis, managerial analytics, and assurance-oriented decision making.
  2. McKinney, W. (2022). Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter (3rd ed.). O'Reilly Media.Technical reference — data acquisition, cleaning, transformation, analysis, and reproducible analytical workflow.

RECOMMENDED Recommended Reading

  • Marr, B. (2025). Data Strategy: How to Use Data and Artificial Intelligence to Transform Your Business (3rd ed.). Kogan Page.Data strategy, AI-enabled business transformation, governance, value creation, and executive decision making.
  • Kleppmann, M. (2017). Designing Data-Intensive Applications. O'Reilly Media.Data architecture, reliability, scalability, consistency, and distributed data systems.
  • Baesens, B., Van Vlasselaer, V., & Verbeke, W. (2015). Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques. Wiley.Fraud, anomaly, risk analytics, and evidence-based detection techniques.
  • Knaflic, C. N. (2015). Storytelling with Data: A Data Visualization Guide for Business Professionals. Wiley.Visualization, analytical communication, and stakeholder-oriented presentation of evidence.
  • James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An Introduction to Statistical Learning: with Applications in Python. Springer.Statistical learning, predictive analytics, classification, regression, and model-based professional insight.
  • Provost, F., & Fawcett, T. (2013). Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. O'Reilly Media.Data-analytic thinking and translation of analytics into business and professional decisions.

Dosen dapat memetakan reading assignment ke setiap module dan assessment. Penambahan referensi ini tidak mengubah CLO, MLO, assessment evidence, grading, achievement, atau attainment rules yang sudah ada.

Learning Modules

8 Modules

Module 01Big Data in Finance & Business Assurance
Module 02Financial Data Sources, Architecture, Governance & Ethics
Module 03Data Acquisition, SQL & Transaction Data Extraction
Module 04Data Quality, Cleaning, Transformation & Integration
Module 05Exploratory Financial Analytics & Visualization
Module 06Transaction Pattern, Anomaly & Risk Analytics
Module 07Evidence-Based Financial Insight & Professional Judgment
Module 08Solution Design, Communication & Responsible Analytics
Assessment & Evidence Architecture

Achievement calculation and direct outcome evidence

AssessmentWeightEvidence Type
Data Concept & Governance Test10%Knowledge / Concept Test
Data Preparation Lab15%Practical Lab / Simulation
Financial Analytics Lab20%Practical Lab / Simulation
Visualization & Insight Assignment15%Assignment / Portfolio
BDAF-001 Professional Finance & Assurance Case25%Professional Case
Final Professional Review / Viva15%Professional Review / Viva
Bloom Cognitive Profile

B1–B2 supporting · B3 strong · B4 very strong · B5 strong · B6 culminating

INCA Graduate Profile

Innovative · Courageous · Resilient

Problem contexts: Industry · Society · Government. Peserta menghasilkan analytical evidence, mempertahankan professional judgment, menerima challenge/feedback, dan memperbaiki solusi.

OBE Audit Trail

Institutional CPL → Program PLO → Course CLO → Module MLO → Evidence

Setiap evidence harus dapat ditelusuri ke MLO, CLO, Program PLO, dan INCA Institutional CPL.

Credential Rule

Completion ≠ Achievement ≠ Attainment

Completion menunjukkan aktivitas selesai. Achievement menunjukkan performance seperti score/grade. Attainment menunjukkan CLO benar-benar tercapai berdasarkan direct evidence dan predefined criteria. Final grade saja tidak cukup untuk credential eligibility.