Kannst du Hilfe in Mathe, Physik, Chemie, Informatik oder für's Ingenieurwesen für dein Studium brauchen? Oder brauchst du Nachhilfe in Sprachen für die Schule? Dann kann ich dir als 1er-Mathe-Informatik-Master, mit meinem Lehramtstudium in Physik und als langjähriger Tutor von vielen Mathevorlesungen an der LMU in München bestimmt helfen.

Dabei ist es egal, ob du Mathe, Physik, Chemie oder Informatik im Haupt-oder Nebenfach studierst und ob du Unterstützung bei Beweis- oder bei Rechenaufgaben brauchst.

In Mathe ist von den Themen/Vorlesungen her fast alles möglich, z.B.:
Lineare Algebra, Analysis, Algebra, Stochastik, Differentialgleichungen, Logik, Numerik, Funktionentheorie

In Informatik können wir zum Beispiel machen:
Theoretische Informatik, Formale Sprachen und Komplexität, Algorithmen und Datenstrukturen, Datenbanksysteme, Betriebssysteme, Programmierung (Java, C/C++, C#, Python, Haskell, Rust, Prolog, Skala) und Web-Programmierung (Html, CSS, JavaScript, NodeJS, VueJS, AngularJS, ReactJS, PHP, SQL, MongoDB )

In Physik geht zum Beispiel:
Mechanik, Elektromagnetismus, Optik, Thermodynamik, Quantenmechanik, Statistische Physik

Und für's Ingenieurwesen:
Technische Mechanik, Elektrotechnik, Strömungslehre, Baustatik

Preis:
- 430 Euro für ein 460min-Lernpaket
- 35 Euro für eine einzelne Stunde


Für die Online-Nachhilfe können wir Teams, Zoom oder Telegram verwenden. Wenn wir beide die Kamera anmachen, ist das gefühlt fast wie Präsenz-Nachhilfe . Zusätzlich verwende ich das Miro-Online-Whiteboard, wo man zusammen was schreiben kann.

Ansonsten schau auf meiner Website vorbei und kontaktier mich dann. 👨‍💻

Erfahrung mit Kindern und Erwachsenen, Motivation zum selbstreguliertem Lernen.

Ich heiße C. und gebe seit vielen Jahren hauptberuflich selbstständig Unterricht in Mathe und Statistik. Mir macht es Spaß Inhalte vermitteln zu dürfen und hoffentlich etwas dazu beitragen zu können, trotz aller Umstände in Schulen und Universitäten, den Druck beim Lernen etwas nehmen und das Lernen entspannter gestalten zu können. Im besten Fall auch an mathematischen und Statistischen Inhalten Spaß zu vermitteln und zu zeigen, dass diese Fächer mit den richtigen Kontexten auch interessant sein können. :)

Mit individueller Nachhilfe für Studenten und Abiturienten konnte ich schon einigen zum Bestehen ihrer kritischen Klausuren in den Angstfächern verhelfen. Weiterhin kann ich Statistische Auswertungen mit R und Python durchführen und entsprechend interpretieren. Geduld ist selbstverständlich.

Hohe Erfolgsquote, sehr geduldig, lange Unterrichtserfahrung, Betreuung von Diplomanten und Dissertanten
Tutorien für Studenten
Einführung in die Programmierung
Coaching in R
Lessons can be done in English

Relevant Units covered in Undergraduate Studies.• Applied Mathematics-1( Complex Variables, Vector Algebra, Calculus Taylors theorem, expansion of functions
in power series, partial derivatives of first and higher orders, total differentiation concept of commutative partial derivatives, Eulers theorems of homogeneous functions, deduction from Euler’s theorems ,errors, approximations, maxima and minima functions of two variables.)
• Applied Mathematics-2( Exact differential Equations, Linear equations & reducible to linear (Bernoulli equations), Linear Diff. Eqn. of nth order with constant coefficients, complimentary function & particular integral when the function of the
integral on the R.H.S. are exponential, Sin(ax + b), Cos(ax + b).Cauchys Linear equation( Homogenous eqn.). The Legendre Linear equation, Variation of parameters & method of undetermined coefficients. Elementary application of above diff. Eqn. in solving engineering problems from Electrical Engg., Chemical Engg., Mechanical Engg., and Civil Engg. Integral Calculus: Rectification of plane curves, Double and Triple integrals, Their geometrical interpretation & evaluation. Evaluation of double integrals by change of order and change to polar. Application of double and triple integrals to areas, volumes & mass. Beta & Gamma Functions.)

• Applied Mathematics 3(Fourier Series and Integrals: Orthogonal and orthonormal functions, expression of a function in a series of orthogonal functions,s ine and cosine functions and their orthogonality properties. Fourier series, Drichlet conditions, periodic functions, even and odd functions, half range sine and cosine series, Parseval's relation. Complex form of Fourier series, introduction to Fourier integral, relation with Laplace transform. Laplace Transforms: Function of bounded variable ( statement only ), Laplace transforms of 1, at, exp( at ), sin( at ), cos( at ),sinh(at), cosh(at), erf(t), shifting properties, expressions with proofs for L { t f(t) }, L { f(t)/t }, Laplace of an integral and derivative)

• Applied Mathematics 4(Complex Variables: Regions and paths in the Z plane. Path/Line integral of a function. Inequality conditions for a path integral to be independent of the path joining two points. Contour Integral, Cauchy's theorem for analytical functions with continuous derivatives. Matrices: Brief revision of vectors over real field, inner product, normal, linear independence, orthogonality. Characteristic values and vectors, and their properties for Hermitian and real Symmetric matrices. Vector Calculus: Scalar and Vector point functions, directional derivative, level surfaces, gradient, surface and volume integrals, definition of curl, divergence. Use of operator. Conservative, irrotational, solenoidal fields. Green's theorem for plane regions and properties of line integral in a plane.)

• Applied Mathematics 5(Probability and topics in Statistics: Statistical experiments with random outcomes, Sample space, probability defined on the basis of sample space and on the basis of events and their combinations. Theorem on probabilities, conditional probability. Bayes theorem. Random variable, probability distribution for discrete and continuous random variables. Density function and distribution functions. Expected values, variance , moments, moment generating functions, Bernoulli's trials, Binomial , Poisson, normal distributions for detailed study with proof, Other common distributions, T , F, Beta, Gamma, X with indication of the applications, Central limit theorem, Bivariate probability and frequency distributions, Correlations, Regression, lines of Regression. Introduction to random samples, use of random numbers, stochastic processes, Time series , queuing theory. Optimization Techniques- Problem formulation, Simplex Method, Revised Simplex Method, Duality & Sensitivity. Unconstrained optimization of several variables• Numerical methods for unconstrained optimisation : Random search & Univariate method, Fletcher Reverse method, Newtons method.)
• Discrete Mathematics ( Logic : Propositions and logical operations, Truth tables, Equivalence and implication, Laws of logic, Mathematical induction and quantifiers. Set theory : Method of proof for set, Venn diagram, set membership tables, definitions, Laws of set theory, Partition of sets. Permutations, combinations and discrete probability. Introduction to permutations and combinations, Generation of permutation and combination, Discrete probability, Conditional probability. Relations and diagraphs., Paths and the relations and diagraphs, Properties of relations, Equivalence relations, Computer representation of relations and diagraphs, Manipulation of relations, Transitive closure, Warshall’s algorithm.Function and pigeon hole principle Definition, Types of functions: injective, surjective, bijective, Composition, identity and inverse, Pigeon hole principle.Graphs , Posets, Hasse Diagram, Lattices, Finite Boolean Algebra, Groups & their Applications Introduction to Rings & Fields.)

Units covered in Postgraduate Studies.
• Advance Financial Mathematics (Access Grid Room -University of Wollongong): Brownian motion, Black-Scholes equation for pricing Digital options and Power options, Reflection principle and barrier options, Pricing options using Monte Carlo Simulations, Monte Carlo estimation methods for hedge ratio, Finite-difference methods for Vanilla options and Asian Options, C++ Programming.
• Financial Econometrics 2 (Monash University):Modeling asset return volatility, volatility modeling for measuring risk and pricing derivatives, continuous time stochastic Processes for pricing financial Derivatives, High Frequency data Analysis, Generalized Method of Moments in Financial Models.
• COMPUTATION IN Stochastics (Monash University): Stochastic differential equations, Taylor expansion of stochastic differential equations, Evaluation of option values. European option. American option, Optimization methods using C++.
• STOCHASTIC CALCULUS AND MATHEMATICAL FINANCE (Dr. Fima Klebaner- Monash University): Ito integrals and Ito’s formula. Stochastic Differential Equations and Diffusions, Calculation of expectations and PDE’s, Feynman-Kac formula. Martingales and Semi martingales. Change of Probability Measure and Girsanov Theorem. Fundamental Theorems of Asset Pricing. Change of Numeraire. Application to options.
• Stochastic Processes II - Random Walks & Markov Chains (Monash University): Simple Random Walks Discrete-time martingales. Markov chains, both continuous and discrete time.
• Applied Statistics: Sample Survey, Clustering, Classification, Principal Component Analysis and Time Series Analysis. (79/100).
• Game Theory and Applications (RMIT University): Strategic Form of Games, Incomplete Information, Cooperative Games.
• Nonparametric Curve Estimation (AMSI - Dr. Aurore Delaigle-University of Melbourne): Kernal Density Estimation, kernel Regression, Spline Regression, Wavelet Analysis and Bootstrapping.
• Financial Time Series (Access Grid Room- University of South Australia): Spectral decomposition, Box-Jenkins models, Forecasting techniques, Smoothing of time series, GARCH and other volatility models, Stochastic Differential Equations.
• Statistical Inference: Statistical Inference at the level of Lee Bain and Max Engelhardt (2000).

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

A very experienced tutor. I am a chartered psychologist and member of the British Psychological Society. Have taught psychology at university level for over 12 years, and generally understand students fears and concerns about the subjects, especially as regards data analysis on SPSS. My doctorate thesis was in the fields of health psychology and social psychology, and I have just published a book about health psychology as it relates to adolescents and their decision making. I have also published various research papers over the years in international journals such as 'psychology & health', 'British Journal of health psychology', and the 'journal of applied social psychology'

As a Bio informatician & Electronics and Communication Engineer I have strong Combination of Statistics, Mathematics & Computer science. I am predominantly interested in joining a team which creates and drives new methodologies and technologies of future
My subsequent area of interest is Financial Engineering, Quantitative Finance (Discrete& stochastic modeling of financial market) and data mining. I have sound Knowledge and experience of Hypothesis Testing, Regression Analysis and Random signal analysis. Time Seriese Analysis, Stochastic Process and Markovian Process Categorical Data Analysis.Class Prediction by K Nearest neighbor method, Support vector machine and neural network.Classification like Linear Disciminant Analysis, Quadratic Discriminant Analysis.Factor Analysis like Principal Component Analysis, Independent Componenet Analysis.Linear Algebra, Matrix analysis, Statistical Signal processing, Digital Signal Processing etc.