Quantitative Analytics

Exploratory Data Analysis (EDA)

• Univariate Analysis: Distributions, histograms, box plots, summary statistics
• Bivariate Analysis: Scatter plots, correlation matrices, cross-tabulation
• Multivariate Analysis: Multiple regression, MANOVA, factor analysis, canonical correlation
• Dimensionality Reduction: PCA, t-SNE, UMAP (for visualization and feature reduction)
• Data Quality Checks: Missing values, outliers, duplicates, data types
• Feature Engineering: Creating derived features, binning, encoding categorical variables

Statistical Concepts

• Descriptive Statistics: Mean, median, mode, standard deviation, percentiles
• Probability Distributions: Normal, binomial, Poisson, exponential
• Hypothesis Testing: t-tests, chi-square, ANOVA, p-values, significance levels
• Confidence Intervals: Understanding margin of error, sample size calculations
• Correlation vs Causation: Pearson, Spearman, confounding variables
• Statistical Power: Type I/II errors, power analysis
• A/B Testing: Experimental design, control groups, statistical significance

Key Statistical Formulas

• T-test: t = (x̄₁ - x̄₂) / √(s²(1/n₁ + 1/n₂))
• Chi-square: χ² = Σ(O - E)² / E
• Pearson correlation: r = Σ((xᵢ - x̄)(yᵢ - ȳ)) / √(Σ(xᵢ - x̄)² Σ(yᵢ - ȳ)²)
• Standard deviation: σ = √(Σ(xᵢ - μ)² / N)
• Confidence Interval: CI = x̄ ± (t* × SE)

Data Mining Techniques

• Pattern Recognition: Association rules (Apriori, FP-Growth)
• Anomaly Detection: Isolation Forest, statistical methods, clustering-based
• Time Series Analysis: Trends, seasonality, ARIMA, forecasting
• Segmentation: Customer segmentation, RFM analysis, cohort analysis

Advanced SQL Topics

• Window Functions: ROW_NUMBER(), RANK(), DENSE_RANK(), LAG(), LEAD(), running totals
• CTEs & Subqueries: Common Table Expressions, nested queries, correlated subqueries
• Joins: INNER, LEFT, RIGHT, FULL OUTER, CROSS, self-joins
• Aggregations: GROUP BY, HAVING, ROLLUP, CUBE, grouping sets
• Performance Optimization: Indexing strategies, query execution plans, partitioning
• Data Manipulation: CASE statements, COALESCE, NULLIF, string functions
• Date/Time Functions: SQL Server (DATEADD, DATEDIFF), PostgreSQL (INTERVAL, AGE, EXTRACT), MySQL (DATE_ADD, DATE_SUB), Standard SQL (EXTRACT)

Core Python Libraries

• Pandas: DataFrames, groupby, merge/join, pivot tables, time series, data cleaning
• NumPy: Arrays, vectorization, broadcasting, linear algebra operations
• Matplotlib/Seaborn: Data visualization, statistical plots, customization
• Scikit-learn: Preprocessing, model selection, evaluation metrics, pipelines
• SciPy: Statistical tests, optimization, interpolation
• Automation: File I/O, API calls, scheduling jobs, error handling

Supervised Learning

• Regression: Linear, Ridge, Lasso, ElasticNet, Polynomial
• Classification: Logistic Regression, Decision Trees, Random Forest, Gradient Boosting (XGBoost, LightGBM)
• Support Vector Machines: Linear and non-linear kernels
• Neural Networks: Basic architectures, backpropagation
• Ensemble Methods: Bagging, boosting, stacking

Unsupervised Learning

• Clustering: K-means, DBSCAN, Hierarchical, Gaussian Mixture Models
• Dimensionality Reduction: PCA, t-SNE, UMAP, autoencoders
• Association Rules: Market basket analysis, recommendation systems

Model Development & Evaluation

• Feature Engineering: Selection methods (RFE, LASSO), importance ranking, interaction terms
• Train/Validation/Test Split: Cross-validation, stratification, time-based splits
• Metrics:
  • Classification: Accuracy, Precision, Recall, F1, ROC-AUC, Confusion Matrix
  • Regression: RMSE, MAE, R², MAPE
• Key ML Formulas:
  • Precision: P = TP / (TP + FP)
  • Recall: R = TP / (TP + FN)
  • F1 Score: F1 = 2 × (P × R) / (P + R)
  • RMSE: √(Σ(yᵢ - ŷᵢ)² / n)
  • MAE: Σ|yᵢ - ŷᵢ| / n
  • R²: 1 - (SS_res / SS_tot)
• Hyperparameter Tuning: Grid search, random search, Bayesian optimization
• Overfitting Prevention: Regularization, early stopping, dropout
• Model Interpretability: SHAP, LIME, feature importance, partial dependence plots
• Model Monitoring: Performance degradation, data drift, retraining strategies

ML Operations Context

• Model Deployment: Batch vs real-time scoring, API development
• A/B Testing: Champion/challenger models, gradual rollout
• Business Impact: Cost savings calculation, ROI measurement
• Ethical Considerations: Bias detection, fairness metrics, model governance

Core AWS Services for Analytics

• S3: Data lake storage, bucket policies, lifecycle rules, versioning
• Redshift: Data warehousing, spectrum for querying S3, distribution keys
• Athena: Serverless SQL queries on S3 data
• Glue: ETL jobs, data catalog, crawlers, schema discovery
• EMR: Big data processing with Spark, Hadoop
• SageMaker: ML model training, deployment, notebooks
• Lambda: Serverless computing, event-driven processing
• QuickSight: BI dashboards, visualizations
• IAM: Access control, roles, policies

Data Pipeline Concepts

• ETL vs ELT: Extract-Transform-Load patterns for different use cases
• Data Quality: Validation rules, error handling, data lineage
• Orchestration: Airflow, Step Functions, scheduling dependencies
• Incremental Loading: Change data capture, timestamps, watermarks
• Data Formats: Parquet, ORC, Avro, JSON optimization

Agile Methodology

• Scrum Framework: Sprints, daily standups, sprint planning, retrospectives, sprint reviews
• Kanban: WIP limits, continuous flow, visual board management
• User Stories: Writing acceptance criteria, story points, estimation
• Backlog Management: Prioritization, grooming, epic breakdown
• Agile Roles: Product Owner, Scrum Master, Development Team interactions
• Metrics: Velocity, burndown charts, cycle time, throughput

Analytics Project Lifecycle

• Problem Definition: Business objectives, success metrics, constraints
• Data Collection: Source identification, access requirements, data assessment
• Analysis & Modeling: Approach selection, iteration, validation
• Insights & Recommendations: Actionable findings, impact quantification
• Implementation: Deployment, monitoring, handoff documentation
• Post-Deployment: Performance tracking, refinement, lessons learned

Independent Problem Solving

• Navigating organizational complexity and finding stakeholders
• Resourcefulness in finding data and documentation
• Breaking down ambiguous requirements into actionable tasks
• Risk identification and mitigation strategies
• Escalation protocols and when to ask for help

Building Business Cases

• Cost-Benefit Analysis: ROI calculation, NPV, payback period
• Impact Sizing: Market size estimation, addressable opportunity
• Assumptions & Risks: Sensitivity analysis, scenario planning
• Implementation Roadmap: Phased approach, resource requirements

KPIs & Metrics Development

• Operations Metrics:
  • Average Handle Time (AHT), First Call Resolution (FCR)
  • Service Level, Abandonment Rate, Agent Utilization
  • Quality scores, Compliance rates
• Banking Metrics Formulas:
  • AHT = (Total Talk Time + Total Hold Time + Total Wrap-up Time) / Total Calls
  • FCR = (Calls Resolved on First Contact / Total Calls) × 100
  • Service Level = (Calls Answered within Threshold / Total Calls Offered) × 100
  • NPS = % Promoters (9-10) - % Detractors (0-6)
• Customer Experience:
  • Net Promoter Score (NPS), Customer Satisfaction (CSAT)
  • Customer Effort Score (CES), Churn rate
  • Journey completion rates, Drop-off analysis
• Product Analytics:
  • Adoption rate, Feature usage, Retention cohorts
  • Conversion funnels, Time-to-value
• ML Model Performance:
  • Prediction accuracy, Model lift, Business impact
  • Cost savings, Efficiency gains

Data Storytelling

• Narrative Structure: Context → Complication → Resolution
• Audience Adaptation: Technical vs executive presentations
• Visualization Principles:
  • Chart selection (bar, line, scatter, heatmap)
  • Color usage, labeling, annotations
  • Reducing chart junk, emphasizing key insights
• Insights vs Observations: "So what?" test, actionability
• Executive Summaries: BLUF (Bottom Line Up Front), pyramid principle

Exploratory Analysis for Product Opportunities

• Identifying pain points in user journeys
• Benchmarking against industry standards
• Cohort analysis to understand behavior segments
• Root cause analysis techniques (5 Whys, Fishbone diagrams)
• Prioritization frameworks (RICE, ICE, Value vs Effort)

Working with Senior Leaders

• Executive Presence: Confidence, clarity, conciseness
• Meeting Preparation: Pre-reads, agenda setting, objectives
• Presenting Recommendations: Options analysis, pros/cons, recommended path
• Handling Questions: Bridging techniques, parking lot for tangents
• Follow-up: Action items, timelines, accountability

Cross-Functional Collaboration

• Engineering Teams: Technical feasibility, data requirements, API specs
• Product Teams: Feature prioritization, user stories, success metrics
• Design Teams: User research insights, prototype testing data
• Business Units: Requirements gathering, change management, training
• Influence without Authority: Building trust, finding win-wins, persistence

Presentation Skills

• Structuring slides for different audiences (technical vs business)
• Using appendix for detailed methodology and assumptions
• Rehearsing timing and transitions
• Managing Q&A sessions
• Creating compelling dashboards that tell a story

Consumer & Community Banking (CCB)

• Product Lines:
  • Personal banking (checking, savings accounts)
  • Credit cards, mortgages, auto financing
  • Investment advice, wealth management
  • Small business banking
• Customer Lifecycle: Acquisition, onboarding, engagement, retention, churn
• Servicing Operations:
  • Call centers, branch operations, digital servicing
  • Dispute resolution, fraud prevention
  • Account maintenance, payment processing

Operational Excellence in Banking

• Cost Reduction: Process automation, straight-through processing, self-service adoption
• Efficiency Metrics: Cost per transaction, Cost per account, Operating expense ratio
• Regulatory Compliance: KYC/AML, GDPR/privacy, audit trails
• Risk Management: Fraud detection, credit risk, operational risk

ML/AI Use Cases in Banking Operations

• Customer Service: Chatbots, intent classification, sentiment analysis, call routing
• Fraud Detection: Anomaly detection, transaction monitoring, pattern recognition
• Process Automation: Document processing (OCR), data extraction, RPA augmentation
• Predictive Analytics: Churn prediction, product recommendations, loan default prediction
• Operational Forecasting: Call volume prediction, staffing optimization

Understanding the Quad

• Four Key Functions:
  • Product: Feature roadmap, user experience
  • Engineering: Technical implementation, infrastructure
  • Design: User research, interface design
  • Data/Analytics: Insights, measurement, optimization
• How analytics informs product roadmap decisions
• Establishing communication networks across teams

Design Principles

• 1. Know Your Audience:
  • Executive: High-level KPIs, trends, anomalies
  • Manager: Performance tracking, comparisons, drill-downs
  • Analyst: Granular data, filters, ad-hoc exploration
• 2. Information Hierarchy:
  • Most important metrics top-left (Western reading pattern)
  • Group related metrics together
  • Progressive disclosure: summary → details
• 3. Visual Best Practices:
  • Limit colors (3-4 max for categories)
  • Use white space effectively
  • Consistent formatting (dates, numbers, percentages)
  • Clear labels, no jargon
  • Include context (targets, benchmarks, previous period)
• 4. Interactivity:
  • Filters should be prominent but not dominate
  • Drill-down paths should be intuitive
  • Tooltips for additional context
  • Reset button to return to default view

Chart Selection Guide

• Comparison: Bar chart (categorical), Column chart (time-based)
• Trend over time: Line chart
• Part-to-whole: Pie/donut (if <6 categories), Stacked bar, Treemap
• Distribution: Histogram, Box plot, Violin plot
• Correlation: Scatter plot, Bubble chart
• Magnitude: Bullet chart (vs target), KPI cards
• Ranking: Horizontal bar chart (sorted)
• Geographical: Filled map, Symbol map
• Multi-dimensional: Heatmap, Small multiples

KPI Dashboard Structure (Operations)

Common Mistakes to Avoid

• ❌ 3D charts, pie charts with many slices, dual axes with different scales
• ❌ Too many metrics (cognitive overload)
• ❌ Misleading axes (not starting at zero for bar charts)
• ❌ Red/green without alternative encoding (colorblind accessibility)
• ❌ Meaningless default sort (alphabetical when magnitude matters)
• ❌ No context (numbers without comparison)
• ✅ Simple, focused, actionable insights
• ✅ Clear data sources and refresh time
• ✅ Mobile-responsive design

Statistical Foundations

• Casella, G., & Berger, R. L. (2002). Statistical Inference (2nd ed.). Duxbury Press.
• Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to Linear Regression Analysis (5th ed.). Wiley.
• Wasserman, L. (2004). All of Statistics: A Concise Course in Statistical Inference. Springer.
• Gelman, A., & Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.

Machine Learning & AI

• Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Springer. https://web.stanford.edu/~hastie/ElemStatLearn/
• Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. https://www.deeplearningbook.org/
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer. https://www.statlearning.com/
• Breiman, L. (2001). "Random Forests." Machine Learning, 45(1), 5-32.
• Chen, T., & Guestrin, C. (2016). "XGBoost: A Scalable Tree Boosting System." Proceedings of the 22nd ACM SIGKDD. arXiv:1603.02754

Data Visualization & Communication

• Tufte, E. R. (2001). The Visual Display of Quantitative Information (2nd ed.). Graphics Press.
• Cairo, A. (2016). The Truthful Art: Data, Charts, and Maps for Communication. New Riders.
• Knaflic, C. N. (2015). Storytelling with Data: A Data Visualization Guide for Business Professionals. Wiley.
• Few, S. (2012). Show Me the Numbers: Designing Tables and Graphs to Enlighten (2nd ed.). Analytics Press.
• Wilke, C. O. (2019). Fundamentals of Data Visualization. O'Reilly. https://clauswilke.com/dataviz/

Python & Data Science Tools

• McKinney, W. (2017). Python for Data Analysis (2nd ed.). O'Reilly.
• VanderPlas, J. (2016). Python Data Science Handbook. O'Reilly. https://jakevdp.github.io/PythonDataScienceHandbook/
• Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (2nd ed.). O'Reilly.

Business Analytics & Product

• Davenport, T. H., & Harris, J. G. (2007). Competing on Analytics: The New Science of Winning. Harvard Business Press.
• Provost, F., & Fawcett, T. (2013). Data Science for Business. O'Reilly.
• Croll, A., & Yoskovitz, B. (2013). Lean Analytics: Use Data to Build a Better Startup Faster. O'Reilly.
• Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press.

Key Research Papers

• Lundberg, S. M., & Lee, S. I. (2017). "A Unified Approach to Interpreting Model Predictions." NIPS. arXiv:1705.07874
• Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why Should I Trust You?: Explaining the Predictions of Any Classifier." KDD. arXiv:1602.04938
• McInnes, L., Healy, J., & Melville, J. (2018). "UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction." arXiv:1802.03426
• Ke, G., et al. (2017). "LightGBM: A Highly Efficient Gradient Boosting Decision Tree." NIPS.

Cloud & Engineering

• Kleppmann, M. (2017). Designing Data-Intensive Applications. O'Reilly.
• AWS Documentation. https://docs.aws.amazon.com/
• Reis, J., & Housley, M. (2022). Fundamentals of Data Engineering. O'Reilly.