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معلم موثوق
يتميز هذا المعلم بمعدل استجابة سريع، مما يدل على خدمة عالية الجودة لطلابه.
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منذ ديسمبر 2017
أستاذ منذ ديسمبر 2017
Econometrics & Statistics (R, SPSS, Eviews, Gretl, Stata) for master thesis and assignments
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من 86.09 $
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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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عند المعلم :
  • Utrecht, Netherlands
  • Copenhagen, Denmark
  • Bruxelles, Belgium
  • Boston, MA, USA
  • Miami, FL, USA
  • Paris, France
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من أنا؟
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.

Covering university courses all around Europe: Italy, United Kingdom, France, Germany, Spain, Portugal,...
المستوى التعليمي
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.

Covering university courses all around Europe: Italy, United Kingdom, France, Germany, Spain, Portugal,...
الخبرة / المؤهلات
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
السن
الكبار (18-64 سنة)
الكبار (65 سنة فأكثر)
مستوى الطالب
مبتدئ
متوسط
متقدم
المدة
60 دقيقة
الدرس يدور باللغة
الإنجليزية
الإيطالية
الجاهزية في الأسبوع العادي
(GMT -04:00)
نيويورك
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عند المدرّس و عبر الانترنت
Mon
Tue
Wed
Thu
Fri
Sat
Sun
00-04
04-08
08-12
12-16
16-20
20-24
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.

Covering university courses all around Europe: Italy, United Kingdom, France, Germany, Spain, Portugal,...


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
إقرأ المزيد
Expert offers private lessons in statistics and econometrics for master thesis and assignments. Regression ARIMA ARMA GARCH ARCH TOBIT LOGIT PROBIT and many other econometrics models. I have a strong experience that can help you to find your solution
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Adam
This course is designed for beginners who want to get started with programming in Python, one of the most popular and accessible languages today. No previous experience is necessary. In one session, students will learn the basics of programming and will be able to create their first Python applications. With clear explanations, practical exercises, and real-world examples, this course is an ideal gateway to exploring computer science and coding.

Online Course: Introduction to Python - Programming Basics and Simple Applications

Duration :
- 60 minutes: intensive format to learn fundamental concepts.
- 90 minutes: In-depth format with more time for practical examples and interactive questions.
---

Educational objectives
At the end of the course, participants will be able to:
1. Understand the basics of programming, including the concepts of variables, data types, conditional structures, and loops.
2. Write and run simple Python scripts.
3. Manipulate inputs and outputs to interact with the user.
4. Solve practical problems with short, functional programs.

---

Course syllabus
1. Introduction (10-15 min)
- Presentation of Python: Why this language?
- Installation and configuration of a programming environment (IDLE, Visual Studio Code, or Jupyter Notebook).
- First program: *"Hello, World!"*.

2. Python Basics (20-30 min)
- Variables and data types (integers, strings, lists).
- Mathematical operations and data manipulation.
- Conditional structures (if/else) and loops (for/while).

3. Practical exercises (20-35 min)
- Write a program to calculate the sum of two numbers provided by the user.
- Create a simple application, like a currency converter or password generator.
- Bonus for 90 min format: Fixed a more complex issue involving lists or loops.

4. Q&A and conclusion (5-10 min)
- Summary of the concepts covered.
- Suggestions for resources to continue learning.
- Feedback on the course.

---

Teaching methodology
- Interactive and engaging: the course combines theoretical explanations with practical exercises.
- Personalized approach: Sessions are tailored to the needs of participants. Students are encouraged to ask questions at any time.
- Learning by doing: examples and exercises are designed to help you assimilate concepts quickly.

---

Target audience
This course is ideal for:
- Complete beginners in programming.
- Students or professionals wishing to acquire skills in Python for their personal or professional projects.
- Anyone curious to discover a powerful and versatile tool for solving problems.

---

Benefits of this online course
- Teaching by an experienced and passionate trainer.
- Interactive sessions adapted to your learning pace.
- Access to educational materials and exercises to continue practicing after the course.

Join us now to discover programming and its applications using Python!

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Reda
Microeconomics:
This course revolves around these notions:
- CONSUMER CHOICE THEORY: marginal utility, indifference curve
- THE CONSUMER'S BALANCE: budget constraint
- THE THEORY OF DEMAND: substitution and income effect, elasticity
My courses are aimed at students from business and management schools who have difficulty in the following subjects:
- Microeconomics
- Macroeconomics
- Statistics
- Probabilities
- Financial analysis
- Accounting
- Taxation
Macroeconomics:
The objective of the course is to present the fundamental concepts of macroeconomics in order to lay the foundations for understanding economic phenomena on the one hand, and on the other hand helping to make a judgment in order to evaluate economic policy recommendations. In summary, the macroeconomist pursues four major objectives:
1. the determination of the aggregates making it possible to explain the behavior of the groups of agent: it is the object of the
macroeconomic accounting;
2. the study of the relationships between these variables in order to determine the existence of stable relationships over time: this is the subject of macroeconomic laws;
3. analysis of the main imbalances that may appear between the aggregates: price increase, unemployment, deficit in public finances, deficit in the trade balance with foreign countries: this is the subject of macroeconomic modelling;
4. the study of the means to correct these imbalances and to achieve certain set goals (price stability, full employment, external balance, etc.): this is the object of economic policy.
Ability to move or video
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Amine
✓ Tools

RStudio • SQL • SPSS • SAS • Jamovi • JASP • Excel

✓ Statistical Methods & Tests

Student's t-test • ANOVA • MANOVA • ANCOVA • Regression (linear & logistic) • Correlation • Chi-square • Nonparametric tests • PCA • MCA • Exploratory factor analysis • Classification / Clustering • Mediation • Moderation • Interpretation

✓ Data analysis & decision support

- Data preparation, structuring and validation using SAS, R and SQL

- Descriptive, exploratory and multivariate statistical analyses on business data

- Production of performance indicators and actionable analyses to support decision-making

✓ Selection and implementation of methods

- Preparation and structuring of databases

- Hypothesis testing and univariate, bivariate and multivariate analyses (ANOVA / ANCOVA)

- Linear and logistic regressions

- Factor analyses (PCA / MCA)

- Mediation and moderation models

- Classification / clustering

1) Academic support

- Lectures, tutorials, projects and assignments in statistics

- Help in understanding and interpreting the results

- Preparation for exams and academic presentations

2) Statistical analysis

- Descriptive statistics (univariate and bivariate)

- Multivariate analyses

- Data exploration and outlier detection

3) Statistical tests

- Correlations (Pearson, Spearman, Cohen's Kappa)

- t-tests (one and two samples, independent or paired)

- Chi-square, binomial tests

- z-scores and associated indicators

4) Statistical modeling

- Linear regressions (simple and multiple)

- Logistic regression

- Interpretation of coefficients, diagnostics and validation of models

5) ANOVA & ANCOVA

- One- or multi-factor ANOVA

- Repeated measures ANOVA

- Fixed and random effects

- Post-hoc tests and effect sizes

6) Factor analyses

- ACP / PCA (scree plot, factor scores, matrices)

- Exploratory factor analysis

- Factorial rotations

- Validation and interpretation of structures and clusters

✓ Reporting & communication

- Clear, structured and concise reporting of results

- Visualizations tailored to decision-makers

- Support for strategic and operational decision-making
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ضمان المدرس المناسب
فصول مماثلة
arrow icon previousarrow icon next
verified badge
Adam
This course is designed for beginners who want to get started with programming in Python, one of the most popular and accessible languages today. No previous experience is necessary. In one session, students will learn the basics of programming and will be able to create their first Python applications. With clear explanations, practical exercises, and real-world examples, this course is an ideal gateway to exploring computer science and coding.

Online Course: Introduction to Python - Programming Basics and Simple Applications

Duration :
- 60 minutes: intensive format to learn fundamental concepts.
- 90 minutes: In-depth format with more time for practical examples and interactive questions.
---

Educational objectives
At the end of the course, participants will be able to:
1. Understand the basics of programming, including the concepts of variables, data types, conditional structures, and loops.
2. Write and run simple Python scripts.
3. Manipulate inputs and outputs to interact with the user.
4. Solve practical problems with short, functional programs.

---

Course syllabus
1. Introduction (10-15 min)
- Presentation of Python: Why this language?
- Installation and configuration of a programming environment (IDLE, Visual Studio Code, or Jupyter Notebook).
- First program: *"Hello, World!"*.

2. Python Basics (20-30 min)
- Variables and data types (integers, strings, lists).
- Mathematical operations and data manipulation.
- Conditional structures (if/else) and loops (for/while).

3. Practical exercises (20-35 min)
- Write a program to calculate the sum of two numbers provided by the user.
- Create a simple application, like a currency converter or password generator.
- Bonus for 90 min format: Fixed a more complex issue involving lists or loops.

4. Q&A and conclusion (5-10 min)
- Summary of the concepts covered.
- Suggestions for resources to continue learning.
- Feedback on the course.

---

Teaching methodology
- Interactive and engaging: the course combines theoretical explanations with practical exercises.
- Personalized approach: Sessions are tailored to the needs of participants. Students are encouraged to ask questions at any time.
- Learning by doing: examples and exercises are designed to help you assimilate concepts quickly.

---

Target audience
This course is ideal for:
- Complete beginners in programming.
- Students or professionals wishing to acquire skills in Python for their personal or professional projects.
- Anyone curious to discover a powerful and versatile tool for solving problems.

---

Benefits of this online course
- Teaching by an experienced and passionate trainer.
- Interactive sessions adapted to your learning pace.
- Access to educational materials and exercises to continue practicing after the course.

Join us now to discover programming and its applications using Python!

---
verified badge
Reda
Microeconomics:
This course revolves around these notions:
- CONSUMER CHOICE THEORY: marginal utility, indifference curve
- THE CONSUMER'S BALANCE: budget constraint
- THE THEORY OF DEMAND: substitution and income effect, elasticity
My courses are aimed at students from business and management schools who have difficulty in the following subjects:
- Microeconomics
- Macroeconomics
- Statistics
- Probabilities
- Financial analysis
- Accounting
- Taxation
Macroeconomics:
The objective of the course is to present the fundamental concepts of macroeconomics in order to lay the foundations for understanding economic phenomena on the one hand, and on the other hand helping to make a judgment in order to evaluate economic policy recommendations. In summary, the macroeconomist pursues four major objectives:
1. the determination of the aggregates making it possible to explain the behavior of the groups of agent: it is the object of the
macroeconomic accounting;
2. the study of the relationships between these variables in order to determine the existence of stable relationships over time: this is the subject of macroeconomic laws;
3. analysis of the main imbalances that may appear between the aggregates: price increase, unemployment, deficit in public finances, deficit in the trade balance with foreign countries: this is the subject of macroeconomic modelling;
4. the study of the means to correct these imbalances and to achieve certain set goals (price stability, full employment, external balance, etc.): this is the object of economic policy.
Ability to move or video
verified badge
Amine
✓ Tools

RStudio • SQL • SPSS • SAS • Jamovi • JASP • Excel

✓ Statistical Methods & Tests

Student's t-test • ANOVA • MANOVA • ANCOVA • Regression (linear & logistic) • Correlation • Chi-square • Nonparametric tests • PCA • MCA • Exploratory factor analysis • Classification / Clustering • Mediation • Moderation • Interpretation

✓ Data analysis & decision support

- Data preparation, structuring and validation using SAS, R and SQL

- Descriptive, exploratory and multivariate statistical analyses on business data

- Production of performance indicators and actionable analyses to support decision-making

✓ Selection and implementation of methods

- Preparation and structuring of databases

- Hypothesis testing and univariate, bivariate and multivariate analyses (ANOVA / ANCOVA)

- Linear and logistic regressions

- Factor analyses (PCA / MCA)

- Mediation and moderation models

- Classification / clustering

1) Academic support

- Lectures, tutorials, projects and assignments in statistics

- Help in understanding and interpreting the results

- Preparation for exams and academic presentations

2) Statistical analysis

- Descriptive statistics (univariate and bivariate)

- Multivariate analyses

- Data exploration and outlier detection

3) Statistical tests

- Correlations (Pearson, Spearman, Cohen's Kappa)

- t-tests (one and two samples, independent or paired)

- Chi-square, binomial tests

- z-scores and associated indicators

4) Statistical modeling

- Linear regressions (simple and multiple)

- Logistic regression

- Interpretation of coefficients, diagnostics and validation of models

5) ANOVA & ANCOVA

- One- or multi-factor ANOVA

- Repeated measures ANOVA

- Fixed and random effects

- Post-hoc tests and effect sizes

6) Factor analyses

- ACP / PCA (scree plot, factor scores, matrices)

- Exploratory factor analysis

- Factorial rotations

- Validation and interpretation of structures and clusters

✓ Reporting & communication

- Clear, structured and concise reporting of results

- Visualizations tailored to decision-makers

- Support for strategic and operational decision-making
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