📈 Business & Money
Data Science for Business Decision Making
This course equips business leaders, analysts, and product owners to apply data science to real decisions—from framing p...
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Sections
1. Foundations of Data Science in Business17 views
Establish core concepts, roles, and the analytics lifecycle in a business context.
15 topics (15 versions)
1.1The data-to-decisions value chain
6
1.2Analytics roles and team structures
1
1.3Decision types: strategic, tactical, operational
1
1.4Analytics maturity models
1
1.5Reasoning under uncertainty and risk
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1.6Balancing data and intuition
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1.7Tooling overview: Python, SQL, BI
1
1.8CRISP-DM and alternative lifecycles
1
1.9Business use case landscape
1
1.10Build vs. buy considerations
1
1.11Data culture essentials
1.12Stakeholder mapping and influence
1.13Metrics hierarchy and cascades
1
1.14Privacy and compliance overview
1.15Quick-win case walkthrough
1
2. Business Problem Framing and Decision Science11 views
Translate ambiguous goals into solvable analytics problems with clear success criteria.
15 topics (15 versions)
2.1Problem decomposition techniques
3
2.2Hypothesis trees and MECE thinking
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2.3Objectives, constraints, and assumptions
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2.4Decision trees and influence diagrams
2.5Priors and belief updating
1
2.6Cost of error and decision thresholds
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2.7Defining KPIs and guardrails
2.8Back-of-the-envelope sizing
2.9Choosing experiment vs. model vs. heuristic
2.10Opportunity sizing and prioritization
2.11Success criteria and acceptance tests
2.12Stakeholder alignment workshops
1
2.13Scoping analytics projects
1
2.14Risk registers and mitigations
1
2.15Decision cadence and governance
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3. Data Strategy, Governance, and Ethics3 views
Set policies, accountability, and ethical principles that enable trustworthy analytics.
15 topics (15 versions)
3.1Data strategy roadmap
2
3.2Ownership, stewardship, and roles
3.3Data catalog, lineage, and discovery
3.4Access controls and segregation of duties
3.5Privacy by design
3.6Responsible AI principles
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3.7Bias and fairness assessment
3.8Model risk management
3.9Regulatory frameworks: GDPR, CCPA
3.10Retention, minimization, and deletion
3.11Consent and transparency
3.12Ethics review boards and checklists
3.13Third-party data and vendor risk
3.14Security fundamentals
3.15Auditability and traceability
4. Data Acquisition and Quality Management1 views
Build reliable data foundations through sourcing, modeling, and quality controls.
15 topics (15 versions)
4.1Source system landscape mapping
1
4.2ETL vs. ELT patterns
4.3APIs and web scraping basics
4.4Batch vs. streaming ingestion
4.5Conceptual and logical data modeling
4.6Data quality dimensions
4.7Validation rules and tests
4.8Handling missing and noisy data
4.9Deduplication and entity resolution
4.10Reference and master data
4.11Feature stores overview
4.12Data lakes and warehouses
4.13Pipeline orchestration and scheduling
4.14Metadata and documentation
4.15Monitoring and observability
5. Exploratory Data Analysis and Business Understanding2 views
Use EDA to uncover patterns, validate assumptions, and generate business hypotheses.
15 topics (15 versions)
5.1Data profiling and sanity checks
2
5.2Univariate analysis
5.3Bivariate and multivariate relationships
5.4Segmentation and clustering basics
5.5Outliers and anomaly cues
5.6Feature engineering ideation
5.7Visualization best practices
5.8SQL for EDA
5.9Pandas workflows
5.10Feature selection intuition
5.11Correlation vs. causation cues
5.12Sampling strategies
5.13EDA for time series
5.14EDA for categorical data
5.15Storyboarding findings
6. Descriptive Analytics and BI for Operators10 views
Build metrics, dashboards, and reporting that drive day-to-day decisions.
15 topics (15 versions)
6.1Metrics and dashboard design
4
6.2Cohort analysis
6.3Funnel analytics
6.4Retention and churn views
6.5Segmentation in BI
1
6.6Attribution basics
1
6.7Geo analysis and mapping
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6.8Executive scorecards
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6.9Self-serve analytics enablement
1
6.10Dimensional modeling and star schema
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6.11Calculated fields and DAX
6.12Alerts, SLAs, and thresholds
6.13Embedded analytics
6.14Dashboard adoption tactics
6.15Common chart pitfalls
7. Statistical Thinking for Decision Making15 views
Apply core statistical concepts to quantify uncertainty and support decisions.
15 topics (15 versions)
7.1Probability foundations
4
7.2Business-relevant distributions
7.3Sampling and confidence intervals
7.4Hypothesis testing and errors
7.5Power and sample size
7.6Bayesian thinking
1
7.7Regression fundamentals
1
7.8Regularization concepts
1
7.9Multicollinearity and VIF
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7.10Heteroskedasticity and remedies
7.11Nonparametric methods overview
1
7.12Bootstrapping and resampling
1
7.13Missing data mechanisms
2
7.14Time dependence pitfalls
2
7.15Interpreting p-values responsibly
1
8. Experimentation and Causal Inference19 views
Design credible tests and causal analyses to estimate true business impact.
15 topics (15 versions)
8.1A/B test design fundamentals
3
8.2Randomization and stratification
1
8.3Metric selection and lift
1
8.4Sequential testing controls
1
8.5CUPED and variance reduction
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8.6Quasi-experimental designs
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8.7Difference-in-differences
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8.8Synthetic control methods
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8.9Propensity score techniques
2
8.10Uplift modeling
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8.11Interference and spillover
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8.12Sample ratio mismatch
1
8.13Stopping rules and peeking
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8.14Ethics of experimentation
2
8.15Experiment platforms and tooling
1
9. Predictive Modeling for Business Outcomes3 views
Build, evaluate, and interpret models aligned to business objectives and constraints.
15 topics (15 versions)
9.1Supervised learning overview
2
9.2Train/validation/test splits
9.3Cross-validation schemes
9.4Feature engineering patterns
9.5Tree-based methods
9.6Linear and logistic models
1
9.7Gradient boosting approaches
9.8Regularization and hyperparameters
9.9Calibration and thresholds
9.10Imbalanced class strategies
9.11Model interpretability tools
9.12Partial dependence and SHAP
9.13Model performance metrics
9.14Error analysis workflows
9.15Model documentation practices
10. Time Series and Forecasting4 views
Forecast demand and trends, account for seasonality, and communicate uncertainty.
15 topics (15 versions)
10.1Time series components
2
10.2Stationarity and differencing
10.3ARIMA and SARIMA
10.4Exponential smoothing methods
10.5Prophet and alternatives
10.6Hierarchical forecasting
1
10.7Promotion and seasonality effects
10.8Forecast accuracy metrics
10.9Intermittent demand methods
10.10Capacity and inventory linkage
10.11Forecasting at scale
10.12Machine learning for forecasting
10.13Scenario planning
10.14Forecast reconciliation
10.15Communicating forecast uncertainty
1
11. Customer and Marketing Analytics3 views
Leverage analytics to understand customers, optimize spend, and personalize experiences.
15 topics (15 versions)
11.1Customer lifecycle mapping
2
11.2Segmentation and personas
11.3Customer lifetime value modeling
1
11.4Churn prediction
11.5Next-best-action strategies
11.6Marketing mix modeling
11.7Digital attribution methods
11.8Pricing elasticity estimation
11.9Recommender systems basics
11.10Personalization pipelines
11.11A/B testing in marketing
11.12Creative and message analytics
11.13CRM and CDP integration
11.14Privacy in marketing data
11.15Customer feedback mining
12. Operations, Pricing, and Optimization16 views
Apply optimization and simulation to improve operations, pricing, and logistics.
15 topics (15 versions)
12.1Optimization problem types
2
12.2Linear and integer programming
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12.3Heuristics and metaheuristics
1
12.4Inventory models and policies
2
12.5Supply chain analytics
1
12.6Workforce planning
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12.7Pricing strategies and rules
12.8Revenue management levers
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12.9Capacity planning
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12.10Routing and logistics
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12.11Queueing theory basics
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12.12Simulation modeling
2
12.13Trade-off and sensitivity analysis
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12.14Constraint management
12.15Decision support systems
13. Data Storytelling, Visualization, and Influence2 views
Communicate insights clearly and drive action across technical and business audiences.
15 topics (15 versions)
13.1Narrative framing of insights
3
13.2Choosing the right chart
13.3Pre-attentive attributes
13.4Visual hierarchy and layout
13.5Decluttering techniques
13.6Annotations and callouts
13.7Designing for executives
13.8Story arcs and flow
13.9Slide design principles
13.10Live demos and dashboard walkthroughs
13.11Handling tough questions
13.12Writing effective data memos
13.13Data visualization ethics
13.14Communicating uncertainty
13.15Influencing without authority
14. Productizing Analytics: MLOps and Deployment2 views
Operationalize models with robust pipelines, governance, and cost-aware operations.
15 topics (15 versions)
14.1ML system design patterns
2
14.2Feature pipelines and stores
14.3Model serving architectures
14.4Batch scoring vs. real-time
14.5CI/CD for ML
14.6Model registry and versioning
14.7Reproducibility and lineage
14.8Monitoring drift and decay
14.9Data contracts and SLAs
14.10Testing ML systems
14.11A/B testing in production
14.12Rollbacks, canaries, and blue-green
14.13Cost and performance management
14.14Platform and tooling choices
14.15Documentation and runbooks
15. Measuring Impact, ROI, and Change Management6 views
Connect analytics to financial outcomes and sustain adoption across the organization.
15 topics (15 versions)
15.1ROI frameworks and formulas
4
15.2Business case development
1
15.3Cost-benefit and payback analysis
15.4North Star metric design
15.5Attribution of impact
15.6Sensitivity and scenario testing
15.7Post-implementation reviews
15.8Scaling wins across teams
15.9Adoption, training, and enablement
15.10Stakeholder communications
15.11Change management models
15.12Analytics operating model
1
15.13Budgeting and portfolio management
15.14Risk and issue management
15.15Career paths in data roles