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Private teachers in Boston

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14 private teachers in Boston

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14 private teachers in Boston

Trusted teacher: Short Bio Claire is a bilingual voice & piano teacher trained at Seoul National University (B.M., honors) and Boston University (M.M., scholarship). A performer across opera/oratorio/musical theatre, she has taught since 2013. Lessons are practical, technique-first, and music-forward so you feel changes quickly—without throat strain or keyboard tension. What I teach Voice (all levels): classical, musical theatre (healthy belt/mix), crossover/K-pop Piano (beg–int): reading & rhythm, clean technique, lead-sheet/chords, accompaniment for singers, basic improvisation Musicianship: ear training, sight-reading, basic theory Audition/Performance Coaching: repertoire picks, storytelling, self-taping Diction/Languages: EN & IT basics, IPA; Korean support for bilingual learners Teaching style & lesson flow Warm-up & quick check (5–10m) Targeted technique (10–20m) — voice or piano focus Repertoire/application (15–25m) — songs, arias, or pieces Take-home plan (2–5m) — 2–3 precise tasks + practice media Results & Alumni Highlights Students from my studio have achieved: Broadcast success: TV/radio announcers & on-air hosts Musical theatre contracts: lead/support roles; successful school → pro transitions Berklee Young Artist / youth program acceptances (plus summer & college pathways) Competition prizes in voice/music; finalist placements Conservatory admissions & scholarships (UG/PG) Choir/ensemble auditions won with confident stage presence Logistics Lengths: 30 / 45 / 60 minutes Ages: children • teens • adults Languages: English, Korean Online studio: 24-hour reschedule when possible; punctual start/end; repertoire tailored to you Call to action Tell me your goal + one song/piece you love. I’ll propose times and a clear plan for your first month.
Singing · Piano · Voice (music)
Trusted teacher: MsC in Engineering with top marks and research assistant of Econometrics for Italian top University. Business Expert in Risk Management. Academic Research in Quantitative Finance and Algorithmic Trading. Common discipline covered: Econometrics (with applications in R, Stata, SPSS, Eviews, Gretl), Statistics, Financial Mathematics, Quantitative Support for Master Degree Thesis (from Regressions to all statistical applications), Risk Management, Mathematics, Computer Science I help with assignments, exams, presentations, advanced research, dissertations, big programming projects and general skill enhancement. Proficient in all major statistical packages: R, SPSS, Stata, Matlab, EViews, Gretl Technical Skills (application and often implementation from scratch): 1) Econometrics: Multivariate Regression, Discrete variable models (i.e. Logit), Time series models (i.e. AR/MA, ARCH/GARCH), Vector AutoRegressive model (VAR), Cointegration (Engle-Granger, VECM), Long-memory process (Fractional Integration), Regime switching models (Hamilton Filter), Kalman Filter, Unobserved Components ARIMA model, Beveridge-Nelson decomposition (Hansen's approach), Copula methods, Metropolis-Hastings algorithm, Black-Litterman model (Meucci's approach), Hierarchical Risk Parity 2) Quantitative Trading (Mid-High Frequency Trading): Stat Arb & Pairs Trading models, Order Imbalance & Order Replenishment effects on intraday returns, Optimal Setup of Entry-Exit Trading Triggers for Quant Trading Strategies, Stat Arb Bertram Model, Data sampling rules for non equally-spaced data (time vs. volume clock for high freq data), Bid-Ask Bounce Bias & Sahalia Method for Microstructure Noise Estimation & Test, Hayashi-Yoshida Lead-Lag Index, D'Aspremont Method for Mean Rev Portfolios, Market Fragmentation in Financial Markets, High-Low prices & Pivot Points trading rule, Trend Following Strategy, Avellaneda-Stoikov Model for Optimal Trading Execution 3) Risk Management: P&L production & analysis for energy trading, VaR & Profit at Risk for energy trading, Merton approach for Credit VaR with/without credit rating migrations, EVT & Copula-based VaR, Stress Test models, Structured Credit Models for Regulatory Risk-Transfer, Additional Value Adjustments for Balance Sheet, Risk Aggregation, Model Risk, Interpolation Methods for multi-year PD Term Structure, Methods for Semidefinite-Positive Corr Matrix Adjustment 4) Financial Mathematics: Longstaff-Schwartz, HJM model (Glasserman's scheme), Greeks with Finite Difference Method, CPPI Products & Cushion Multiplier Setup 5) Machine Learning: Support Vector Machine, Decision Tree, Principal Component Analysis & Regression, XGBoost, Random Forest
Statistics · Economics for adults · Computer programming
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