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I am a student from Utrecht University who is ready to offer private classes on a variety of maths-related subjects. Here is the list of what I can teach🧐 1) Mathematics🧮 - any topic from the school curriculum - calculus and beginnings of optimization - beginnings of linear algebra 2) Statistics🚀 - probability theory - statistical tools - elementary econometrics (regressions and STATA) 3) Economics📈 - Micro and Macro up to and including intermediate level - Labour and behavioral economics I have successfully finished two years of a challenging economics bachelor (overall GPA 8.58). Right now I am in my third year of the interdisciplinary PPE program at UU (current GPA 7.97). I have had a diverse experience with tutoring during the past few years - from online classes to student assistance at the university and to simply helping friends. I am quite confident that together we will overcome all the difficulties and achieve a new understanding of the subject as well as excellent grades😊

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Statistics is an increasingly important field as the wealth of available data increases rapidly. In order to perform meaningful data analysis, it is important to fully understand the mathematical principles and background behind the techniques. These classes will help in that regard. More general mathematics classes are available for all ages. Where necessary, homework will be provided in between classes. Lessons can also be tailored to the requirements of the student.

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Struggling with Math or Physics? Do you hate it but you need to pass your exams? I'm a Theoretical Physics Master's student and I offer Physics and/or Maths lessons at any level, from primary school to Bachelor's exam preparation. I have 5 years of experience of private teaching, and i love it! I'm resident in Utrecht (Nederland), but i can also provide online lessons with a digital blackboard. Feel free to contact me for any further question!

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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

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My tutorials are aimed at helping you have a better understanding of the basics of economics and increase your grades in your School or University courses. They are tailored to your doubts and topics you struggle most with in a "hands-on" approach. Taught topics include: -Probability theory -Common Probability distributions(Bernoulli, Polinomial, Normal,...) -Combinatorics -Correlation and Independence of variables -Means, Averages, Standard Deviations and how to calculate them -t-tests

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Teaching to me is the most incredible job and it is a two-way process where the teacher, as well, gets to learn so many new things from students! I'm currently working in a bank where I heavily use economics and statistics. I have done MSc. in Economics from Queen Mary University in London. I'll get incredible joy by even being a teacher in my favorite subject and helping students to gain valuable insight into the same. I'm attracted to teaching by a sense of service as I think I can use my intelligence and creativity to bring a change in somebody's life. Teaching is the one profession which creates all other professions :)

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15Students

Are you a university student, engineer, or professional who needs to actually use data — not just learn theory about it? This course is built around real problems and real code. We skip the textbook formulas and go straight to applying statistics and data science the way professionals do: with Python (pandas, NumPy, scikit-learn, matplotlib) and R (RStudio). What we cover, adapted to your level and goals: - Descriptive and inferential statistics (the ones that actually matter) - Data cleaning, exploration, and visualization - Regression, classification, and intro to machine learning - Time series and forecasting basics - R for statistical analysis and academic research Who this is for: - University students in statistics, economics, engineering, or biology - Professionals wanting to move into data analysis or data science - Researchers who need to process and present data properly I use Python and R professionally as a working engineer — everything I teach comes from real application, not just academic exercises.

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14Students

Do you have a statistics or probability exam in BA1/BA2? I can help you review the material in a clear, structured and exam-oriented way. I support higher education students (university and college), particularly in their first and second years of undergraduate studies, to understand important concepts, redo practical exercises, and practice with exam-style questions. My goal is simple: to help you understand the logic behind formulas, know when to use them, recognize correct reasoning in a statement, and gain autonomy when facing exercises. Subjects covered according to your program: • Descriptive statistics: mean, median, variance, standard deviation, quartiles, quantiles, coefficient of variation, box plots, histograms, graphs, interpretation of tables and data. • Univariate and bivariate statistics: analysis of one variable, analysis of two variables, scatter plots, covariance, correlation, regression line, coefficient of determination, interpretation of relationships between variables. • Probabilities: events, union, intersection, complement, conditional probabilities, independence, Bayes' theorem, probability trees, contingency tables. • Combinatorial probabilities: permutations, arrangements, combinations, draws with or without replacement, counting, classic exam situations. • Random variables: discrete and continuous variables, probability function, density function, distribution function, expectation, variance, standard deviation, variable transformation. • Probability laws: Bernoulli distribution, binomial distribution, normal distribution, standard normal distribution, Student's t-distribution, chi-square distribution, use of statistical tables according to your course. • Statistical inference: sampling, estimators, point estimation, confidence intervals, margin of error, degrees of freedom, confidence level. • Hypothesis testing: null hypothesis H0, alternative hypothesis H1, significance threshold, p-value, one-tailed or two-tailed test, test on a mean, test on a proportion, chi-square test, interpretation of results. • Exam preparation: reading statements, choosing the right method, identifying the formulas to use, typical exercises, past exams, guided corrections and problem-solving methods. Method of working : 1. We quickly identify the chapters that are causing problems; 2. I re-explain the theory with simple examples; 3. We redo the important exercises together; 4. I will show you how to recognize good reasoning in the exam; 5. We construct a clear method that can be reused independently. I don't just provide a correction: I explain the reasoning step by step so that you are able to redo the exercises without help. For students retaking the exam, I also offer more intensive support: level assessment, priority identification, review of fundamentals, and practice with typical exercises and past exams. The goal is to get straight to the point and work efficiently within the time available before the exam. I have been giving private lessons for over 7 years in mathematics, statistics, economics, and accounting. I have also tutored first and second-year Bachelor's students at Solvay/ULB in mathematics, statistics, and microeconomics as part of a university tutoring program. My professional experience in finance and business controlling at Deloitte has also allowed me to develop a very structured approach to numbers, analysis and problem-solving. Courses available in French or English, online or in person in Brussels or the surrounding area.

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1Students

Currently in Master 2 of Random Modeling at the University of Paris Cité, after a rigorous research Master focused on dynamic systems, I dedicate my expertise to Bachelor (L1, L2, L3) and Master (M1) students facing the demands of scientific and economic fields. Objective: Zero obstacles. A targeted support system to help you pass your exams and entrance exams.

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2Students

Demystifying probabilities and data processing Statistics and probability occupy an increasing place in the secondary school curriculum and directly prepare students for the requirements of higher education (economics, medicine, engineering, humanities). The sessions will include: Descriptive statistics (means, standard deviations, graphical representations). Conditional probabilities, probability laws and combinatorics. Analysis, interpretation of datasets and sampling logic. Targeted training based on typical exercises and exam problems.