Statistics, Econometrics and R python programming tutoring and Assignment Assistance

From 43 C$ /h

I'm willing to assist in different applied and pure course of Statistics, Econometrics, Business Statistics and Bio-statistics such as, Applied Statistics, Time series Analysis, Survey Sampling, Econometrics, Quantitative Data Analysis, R Programming, Statistical Packages, Probability and Statistics, Statistical Inference, Parametric and Non Parametric Statistics, Regression Analysis etc.
Currently I am willing to support students and professionals in their assignments/task and data analysis projects and research work.

Extra information

All discussion will be online through What'sApp or Microsoft Team/Zoom/Skype

Location

Online from Pakistan

About Me

I'm Statistics Instructor at University and have more than 10 year teaching and research experience at university level. I taught different applied and pure course of Statistics, Business Statistics and Bio-statistics such as, Applied Statistics, Time series Analysis, Survey Sampling, Econometrics, Quantitative Data Analysis, R Programming, Statistical Packages, Probability and Statistics, Statistical Inference, Parametric and Non Parametric Statistics, Regression Analysis etc. Currently I am willing to support students and professionals in their assignments/task, and data analysis projects and research work. I have an expert skill of statistical software(SPSS, STATA etc) and R Language. I'm author or coauthor of more than 40 researches and willing to support in writing research articles.

Education

I have PhD in Statistics (Specialization in Survey sampling) and MSc in Statistics (Specialization in Multivariate Data Analysis). I have advance level expertise of Statistical software and Programming.

Experience / Qualifications

I have more than 10 Years teaching and research experience at University level and teach under graduate, graduate and post graduate level courses. I supervised 25 Msc level and 2 PhD researches.

Age

Adults (18-64 years old)

Seniors (65+ years old)

Student level

Beginner

Intermediate

Advanced

Duration

60 minutes

The class is taught in

English

Hindi

Urdu

Skills

Statistics

Computer programming

Python

Availability of a typical week

(GMT -04:00)

New York

Online via webcam

Mon

Tue

Wed

Thu

Fri

Sat

Sun

00-04

04-08

08-12

12-16

16-20

20-24

Basic Statistics for the Beginners Step by step guide

27 C$ /h

Its a beginner course, In this class you will study all basic topics of statistics step by step such as , organization of data numerically and graphically, descriptive statistics, dispersion, probability, correlation, regression analysis, hypothesis testing, Parametric and non parametric test etc. or any other according to your requirement.

Data analysis, Dissertation and research paper assistance

130 C$ /h

If you are worry about your research project/paper, dissertation data analysis in any field of study then stop now. Your are now with data scientist and I have advance skill on basic and advance analysis. My High Skilled areas in statistics and Data analysis are:
Econometric models: Regression Analysis and Model Diagnostics (Heteroskedasticity, Autocorrelation, Multicollinearity etc), Dummy Variables, Nonlinear Regression models
Time Series Modeling such as AR, MA, ARIMA, Vector Autoregressions, Unit roots, and Cointegration, ARCH, GARCH, EGARCH, GARCH-type models, Switching Models, Panel Data Analysis (Fixed and Random effect), ADRL and NADRL Model, Simulation methods etc
Biostatistics: Measurement and types of studies, frequency distributions, summary statistics, Hypothesis testing & confidence interval, Parametric and non parametric tests, discrete and continuous distribution, Cross table analysis, categorical data analysis, regression & correlation, Logistic regression, GLMs, Mata analysis etc.
Statistical Inference and estimation pure and applied.
Probability and statistics
Statistical Quality Control
Survey Sampling Pure and Applied

Robert

4.74

(27)

A highly experienced Franco-Belgian teacher (ook in het nederlands!) offers private lessons in mathematics (including finance), probability and statistics, as well as physics, chemistry, and biology for secondary and higher education levels. For physics, chemistry, and biology, the instruction is tailored to the secondary level, specifically up to the 5th year of secondary education in Belgium.

Whether you prefer lessons at your place, my place, or remotely, I am flexible to accommodate your needs. If necessary, I can travel to your home in Brussels, Walloon and Flemish Brabant, with a minimum duration of 2 hours per session. The lessons are designed to provide extensive practice with numerous exercises. Distance learning options are also available through platforms such as Skype, Facebook, etc. Please note that for students in France, only distance learning courses are provided.

In mathematics, I specialize in various topics and frequently provide lessons covering the entire secondary school curriculum, including math 6 and higher. These topics encompass factorization, equations of the 1st and 2nd degree (with in-depth study of parabolas), limits, derivatives, integrals, exponentials and logarithms, as well as trigonometry. Additionally, I am occasionally called upon to teach analytical geometry in space, including equations of lines and planes.

For statistics and probabilities, I provide instruction in descriptive and inferential statistics (univariate and bivariate), covering confidence intervals and hypothesis tests, applicable to secondary and higher education levels.

Feel free to reach out to me to discuss and arrange the lessons based on your specific needs and availability. My aim is to help you enhance your skills effectively and provide personalized instruction. By tailoring the lessons to your requirements, we can ensure rapid progress in your studies.

Matías

4.74

(27)

Python is one of the best, if not the best, language to start learning programming. It is also one of the most widely used languages today, especially in cutting-edge areas such as machine learning.

This popularity means that Python is constantly evolving. It offers a wide range of tools and libraries, which are free and very varied.

As an aeronautical engineer, I like to share my knowledge and derive satisfaction from it by teaching and motivating others.

I'm used to working with people of different ages. I believe in the importance of segmenting learning, visualizing progress, setting concrete goals and practicing regularly.

Beyond these general principles, there is no magic rule or method. Some approaches work with some students but not with others. Adaptation to individual needs is therefore the main objective of private lessons. So I will do my best to find what motivates and helps my student.

Lina

4.74

(27)

This lossons will help you understand and apply the fundamental concepts of probability and statistics.

We can cover topics such as:

- Counting techniques
- Key concepts and definitions in probability
- Key concepts and definitions in statistics
- Inferential statistics
- Confidence intervals and hypothesis testing
- Probability distributions

...and many more! This course will give you the tools to confidently analyze data and make informed decisions.

Amin

4.74

(27)

Python is the most in-demand programming language in the world right now — and one of the easiest to learn with the right guidance.
Whether you've never written a line of code or you're a student who needs to pass a programming course, this is a practical, no-fluff introduction that gets you writing real code from session one.
What we can cover depending on your goals:

Python fundamentals: variables, loops, functions, data structures
- Object-oriented programming (OOP)
- Data manipulation with pandas and NumPy
- Introduction to machine learning with scikit-learn
- Database management with SQL
- C and Java upon request
- MATLAB and R available for engineering/science students

Why learn with me?
I'm not a student teaching on the side — I'm a professional engineer who uses Python daily for data analysis, modeling, and automation. I know exactly which concepts matter in the real world and which ones you can skip for now.
Sessions are 100% personalized: I adapt the pace, the examples, and the exercises to your background and your goal — whether that's passing your university exam, building a project, or landing a job.

Enrique

4.74

(27)

Don't settle for anything less than excellence.
I am an Aerospace Engineer with a Master's degree in Quantum Physics and have completed Ph.D. work at the University of Cambridge in Computational Physics. Additionally, I have 4 years of experience developing MATLAB and possess deep programming skills in MATLAB/Simulink family, C/C++, Fortran, and Python.

With over 12 years of tutoring experience, I have successfully guided more than 50 students worldwide to achieve distinction in various fields. Consistent results are my priority, and I strive for excellence in all aspects of my teaching.

My lessons are customized to meet each student's unique needs and are designed to be engaging and insightful.

Whether you are at a school level or require advanced or professional-level instruction, I offer support in the following areas:
- Preparation for IB/IA, A-Levels, GCSE, University Entry, or equivalent.
- Experience in preparing students to access world-class schools and universities, including Cambridge University, Oxford, Ivy League and other top institutions in the UK and US.
- University levels (undergraduate and postgraduate).
- High school studies and diploma programs.
- Assistance with specific projects at a professional level, including job interview preparation.
- Extensive experience working with children.

Every lesson is meticulously planned in advance to ensure that it aligns with your goals and targets areas for improvement.
I prioritize a dynamic and interactive learning experience, with one-on-one sessions tailored to your individual requirements. Lessons will be conducted via webcam, enabling you to connect from anywhere.

I have a highly flexible schedule and can adapt to accommodate your needs.
If you have any questions about my teaching method, availability, or pricing, please don't hesitate to reach out. I am here to assist you and provide the support you need.

Giuliano

4.74

(27)

Python is a powerful and versatile programming language with countless possibilities. You can use it for data analysis, image processing, automation, software development, hardware control, and much more.

Do you want to create your own software?
Work with data or images?
Automate repetitive tasks?
Control or manage your own hardware?

Whether you are just starting to learn Python or already have a specific project and need some guidance, I would be happy to help you.

My goal is to explain things clearly, adapt to your level, and help you understand not only how to make something work, but also why it works.

Let's turn your ideas into working Python projects!

Salah

4.74

(27)

Master Python with Personalized Courses

Discover the art of programming with Python courses tailor-made to meet your specific needs. Whether you are a beginner, intermediate or professional, my lessons are suitable for all levels.

Why Choose My Courses?

Personalized Teaching Approach: Each course is tailored to your skill level and individual goals.

Practical Experience: Learn by doing with real-world projects that build your understanding and skills.

Ongoing Support: Get unlimited email support for any questions you have between sessions.

As a Python expert, I have a passion for teaching and sharing my knowledge. My goal is to guide you effectively in your learning journey.

Book Your First Lesson:

Start your journey to Python mastery now by booking your first lesson. Whether you aspire to enter the development field or hone your existing skills, these courses are designed for you.

Kevin

4.74

(27)

► EXPERT STATISTICS, DATA ANALYTICS, MACHINE LEARNING & AI TUTOR FROM SWITZERLAND
► UNIVERSITY, FOUNDATION, IB, A-LEVEL & INTERNATIONAL SCHOOL SUPPORT

I completed my Master’s degree in Business Information Systems at a Swiss University of Applied Sciences, where my academic background strongly combined mathematics, statistics, data analysis, analytical thinking and problem-solving. This technical and data-oriented foundation shaped the way I teach today: clearly, logically and with a strong focus on real understanding.

For many years, I have successfully supported students in Statistics, Data Analytics, Machine Learning and AI. My main focus is especially on Statistics — from basic descriptive statistics to advanced statistical methods, hypothesis testing, regression, probability distributions and interpretation of results.

I mainly use R for statistical analysis, data handling, visualisation and practical exercises. My goal is not only to help students calculate results, but to make sure they understand what the results mean and how to explain them correctly.

► STATISTICS, DATA ANALYTICS & AI SUPPORT

► STATISTICS & PROBABILITY
I help students understand descriptive statistics, probability, random variables, distributions, sampling, confidence intervals, hypothesis testing, p-values, correlation, regression and statistical interpretation. My lessons focus on explaining the logic behind each method, not just applying formulas.

► APPLIED STATISTICS WITH R
I support students in using R for statistical analysis, data cleaning, visualisation, hypothesis testing, regression models and interpretation of outputs. Students learn how to connect theory, calculation, code and real meaning step by step.

► QUANTITATIVE METHODS & RESEARCH STATISTICS
I help students with statistical methods used in business, economics, psychology, social sciences, science and university research. This includes choosing the correct test, understanding assumptions, interpreting results and presenting findings clearly.

► DATA ANALYTICS & DATA SCIENCE
I support students with data preparation, exploratory data analysis, visualisation, dashboards, summary statistics and practical interpretation. The focus is always on understanding the data and drawing meaningful conclusions.

► MACHINE LEARNING & AI FOUNDATIONS
For students working with modern data topics, I also provide support in the foundations of Machine Learning and AI, including regression, classification, clustering, model evaluation and practical applications. These topics are explained from a statistical point of view, so students understand the logic behind the models.

► UNIVERSITY, FOUNDATION & INTERNATIONAL COURSES
I support students in Statistics, Data Analytics, Business Analytics, Quantitative Methods, Econometrics, Research Methods and technical modules. I help with exam preparation, assignments, projects and practical data analysis tasks.

► HOW I TEACH

► I FOCUS ON REAL STATISTICAL UNDERSTANDING.
Statistics becomes much easier when students understand why a method is used, what the result means and how to interpret it correctly.

► I EXPLAIN FORMULAS STEP BY STEP.
Difficult formulas, tests and models are broken down into simple, logical parts so students can follow the reasoning clearly.

► I CONNECT THEORY WITH R PRACTICE.
Students learn not only the statistical theory, but also how to apply it in R, read the output and explain the result in proper academic language.

► I HELP STUDENTS CHOOSE THE RIGHT METHOD.
Many students struggle with deciding whether to use a t-test, chi-square test, ANOVA, regression or another method. I teach students how to recognise the correct approach from the question or dataset.

► I TRAIN INTERPRETATION AND EXAM TECHNIQUE.
Students learn how to structure statistical answers, write clear conclusions, explain p-values, interpret confidence intervals and present results professionally.

► I ADAPT EVERY LESSON TO THE STUDENT.
Some students need help with theory, others with R coding, assignments, research projects or exam preparation. I adjust every lesson to the student’s exact course, level and goals.

► YEARS OF EXPERIENCE WITH STATISTICS, DATA & UNIVERSITY STUDENTS

Over the years, I have successfully supported students from demanding academic programmes, helping them strengthen their statistical understanding, improve their analytical thinking and achieve excellent progress in Statistics, Data Analytics, Machine Learning and AI.

► ONLINE LESSONS

► Interactive whiteboard
► Clear digital notes
► Step-by-step statistical explanations
► R support for data analysis
► Exam preparation
► Assignment and project guidance
► Practical examples with real datasets
► Focused one-to-one support from Switzerland

► MY GOAL

My goal is not only to help students pass exams or complete assignments, but to help them truly understand Statistics. With the right guidance, statistical methods become logical, practical and much easier to apply.

► SUBJECTS: Statistics, Probability, Data Analytics, Data Science, Machine Learning, AI, Quantitative Methods, Research Methods, Econometrics
► MAIN TOOL: R
► LEVELS: International School, IB, A-Level, Foundation Courses, University Modules, Professional Training
► FORMAT: Online tutoring from Switzerland
► FOCUS: Statistical understanding, R practice, interpretation, exam preparation, assignments, projects and long-term analytical confidence.

Brent

4.74

(27)

Are you drowning in countless formulas? Is your head starting to explode with equations and graphs? Don't panic! Math doesn't have to be a stumbling block. With the right explanation and a calm approach, it often becomes much clearer. Together, we'll tackle it step by step, at your pace.

For whom?
- Students from primary and secondary education
- Children and young people preparing for exams or tests
- Anyone who wants to give mathematics a second chance, including adults

What can you expect?
- I explain the often complicated mathematical language in clear, human language
- Focus on insight, not just learning tricks and formulas by heart
- Exercises that we tackle together
- Space for questions, repetition and building self-confidence
I can also prepare exercises and even complete practice exams myself.

About me
I'm currently pursuing my Master's degree in Data Science/Analytics at the University of Antwerp. In high school, I had seven hours of math a week and always passed my exams with high marks. I've been happily tutoring students of various levels for several years now. I'm analytical, but also calm, patient, and good at sensing exactly where things are going wrong.

Practical:
- 1-on-1 lessons, online, at my place or at yours (if you don't live too far away)
- We will go through your material together or I will provide my own material
- Your own pace and approach, completely tailored to you

I have already successfully guided students with:
- Mathematics in secondary education: from the 1st to the 6th year, for various fields of study and schools, including Latin at Sint-Michielscollege Brasschaat, Economics-Mathematics at KA Schoten, and Humanities at Annuntia.
- Arithmetic in primary education: pupils in the 4th, 5th, and 6th grades, including mental arithmetic, written calculation, and other arithmetic skills.
- Mathematics in the Electromechanics program at AP University of Applied Sciences.

Feel free to send me a message with your questions or concerns, and we'll discuss how I can best support you.

Raouf

4.74

(27)

The final year of secondary school is approaching, and with it, the famous high school diploma! The mathematics program in the final year (whether in Classical or General education) is demanding and requires a solid foundation.

This support from the beginning of the year is specifically designed for students in the Luxembourg system who are transitioning from 2nd to 1st year of secondary school. The goal is to start now to consolidate existing knowledge, fill any gaps in understanding, and get a head start on the curriculum for the start of the academic year, allowing them to approach their final year of secondary school with confidence and composure.

🎯 Immediate objectives:

Review of 2nd year basics: Identify and correct the blocking points from the previous year.

Preparing for the 1st year program: A gentle introduction to the first major chapters to avoid the shock of starting school.

Baccalaureate methodology: Learning to write a clear essay, justify one's reasoning and manage one's time when faced with a complex problem.

📚 Sample program (Adaptable according to the student's section - B, C, D, G, etc.):

Module 1: Consolidation of Foundations (Basic Algebra and Analysis)

Perfect mastery of algebraic calculation, fractions and powers.

Solving complex equations and inequalities.

In-depth review of derivatives and the study of functions (variation tables, asymptotes).

Module 2: Introduction to the key concepts of the 1st

Introduction to logarithmic and exponential functions (ln and exp).

Introduction to integral calculus (calculating areas).

Depending on the section: Complex numbers, analytic geometry in space, or probability/statistics.

⚙️ Methodology and Format:

Initial assessment: The first lesson begins with a short placement test to adapt the program to the student's actual needs.

Flexibility: Pack of 10h, 15h or 20h spread over the summer (for example, 2 to 3 times a week in July and August to prepare for the start of the school year).

Ongoing support: Summary sheets provided at the end of each session and availability via message for small questions between classes.

Farouk

4.74

(27)

These courses are part of a structured and progressive training in Object-Oriented Programming (OOP) with JavaScript, designed for beginner or intermediate developers who want to understand in depth how the language works, write clearer, more maintainable code and prepare themselves calmly for modern frameworks like React ⚛️.

Object-Oriented Programming is often perceived as complex or abstract.

My goal is simple: to make it logical, concrete, and immediately applicable.

🎯 Training Objectives

Upon completion of this training, you will be able to:

Understanding what Object-Oriented Programming really is (and when to use it)
Create and manipulate objects in JavaScript in a clean and efficient way
Use ES6 classes, constructors, and methods with confidence
Mastering this, the prototype, and the instantiation logic
Apply encapsulation, inheritance, and polymorphism without confusion
Avoiding common mistakes made by OOP beginners
Structure your JavaScript code like a professional developer

📖 Training Plan – Object-Oriented Programming in JavaScript
1. Introduction to Object-Oriented Programming 🧠
Understanding the concept, objectives and benefits of OOP.
2. Procedural Programming vs. OOP
Why unstructured code quickly becomes unmanageable.
3. Objects in JavaScript
Properties, methods and representation of the real world.
4. The keyword this
Understanding the execution context (often poorly understood).
5. Limitations of simple objects
Why duplicating code is a bad idea.
6. Constructive functions
Create multiple objects from the same model.
7. The keyword new
What it's actually doing under the hood.
8. The prototype
Sharing methods and memory optimization.
9. ES6 Classes
Modern syntax and best practices.
10. The builder
Proper initialization of objects.
11. Data Encapsulation
Protect the internal state of objects.
12. Inheritance between classes
Reusing code intelligently.
13. The keyword super
Communication between parent and child in the classroom.
14. Polymorphism
The same behavior, several forms.
15. Composition vs. Inheritance
Choosing the right architecture.
16. Best practices in OOP
Write readable, scalable, and maintainable code.
17. Common mistakes made by beginners
Pitfalls to absolutely avoid.
18. Guided practical exercise
Creation of a concrete class (product, user, etc.).
19. Assessment Quiz (Multiple Choice Questions)
To validate the actual understanding of the concepts.

🛠️ Teaching method: Understand before writing

This training program is based on a progressive and pragmatic approach:
Clear and illustrated explanations
Concrete examples from real projects
Simple but effective exercises
Constant questioning to avoid rote learning
Adaptation to the learner's level and pace
Here, we don't "recite OOP" — we understand it.

🚀 Learner's result

At the end of the training, you will not only know how to write a JavaScript class.
You will know:

1- Why does it exist?
2- When to use it
3- and when not to use it

You will leave with:
a solid understanding of OOP
a cleaner and more professional code
an ideal foundation for learning React, Node.js or any other modern framework

Fisnik

4.74

(27)

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.

Ammar

4.74

(27)

A- TOPICS YOU CAN EXPLORE AND MASTER:
1- PYTHON FOUNDATIONS
• Variables, data types, operators, conditional structures, loops, functions, modules, files, exceptions, and object-oriented programming
• Lists, tuples, dictionaries, sets, comprehensions, debugging, and writing clear, reusable, well-structured code
• Jupyter Notebook, Anaconda, Visual Studio Code, virtual environments, and package management

2 — PROGRAMMING, ALGORITHMS, AND COMPUTER SCIENCE FOUNDATIONS
• Algorithmic thinking, problem decomposition, pseudocode, flowcharts, procedural programming, object-oriented programming, recursion, and modular program design
• Fundamental data structures including arrays, lists, stacks, queues, dictionaries/hash tables, sets, trees, graphs, and their appropriate use
• Searching, sorting, traversal, algorithm efficiency, computational complexity, Big-O notation, debugging, testing, code organization, and problem-solving strategies
• Practical programming exercises ranging from beginner problems to university-level algorithmic and computational challenges

3- DATA PREPARATION AND EXPLORATION
• NumPy and pandas for importing, cleaning, transforming, filtering, grouping, reshaping, and merging data
• Missing values, duplicates, outliers, inconsistent formats, data leakage, and data-quality validation
• Exploratory data analysis using descriptive statistics, Matplotlib, Seaborn, and graphical interpretation

4- MATHEMATICAL FOUNDATIONS
• Linear algebra, vectors, matrices, derivatives, optimization, probability, and statistics
• Loss functions, gradients, distance measures, regularization, likelihood, and model complexity
• Mathematical concepts are explained according to the learner’s level and the requirements of the selected algorithms

5- SUPERVISED MACHINE LEARNING
• Linear and polynomial regression, logistic regression, and regularized models
• k-nearest neighbours, decision trees, random forests, gradient boosting, support vector machines, and Naive Bayes classifiers
• Classification, regression, model assumptions, decision boundaries, feature importance, and interpretation of results

6- UNSUPERVISED LEARNING
• Clustering using k-means, hierarchical clustering, and density-based methods
• Principal component analysis, dimensionality reduction, anomaly detection, and pattern or structure discovery
• Method selection, evaluation of data structure, and interpretation of results without predefined labels

7- MODEL EVALUATION AND IMPROVEMENT
• Training, validation, and test sets; cross-validation; hyperparameter optimization
• Accuracy, precision, recall, specificity, F1 score, ROC–AUC, confusion matrices, MAE, MSE, RMSE, and R2
• Underfitting, overfitting, bias–variance trade-off, class imbalance, feature engineering, feature selection, scaling, and regularization

8- DEEP LEARNING
• Neural-network foundations, activation functions, forward propagation, backpropagation, and gradient descent
• Multilayer perceptrons, convolutional neural networks, recurrent neural networks, and Transformer foundations
• TensorFlow, Keras, or PyTorch depending on the learner’s project and working environment

9- ARTIFICIAL INTELLIGENCE APPLICATIONS
• Natural language processing, text classification, embeddings, sentiment analysis, and foundations of language models
• Computer vision, image classification, fundamental principles of object detection, and image preprocessing
• Recommendation systems, forecasting, anomaly detection, intelligent automation, and decision-support applications

10- GENERATIVE AI AND LARGE LANGUAGE MODELS
• Transformer architecture, tokens, embeddings, attention mechanisms, prompt engineering, Retrieval-Augmented Generation (RAG), and model evaluation
• Use of artificial-intelligence APIs, vector databases, document-retrieval systems, and structured AI-enabled workflows when relevant
• Reliability, hallucinations, bias, privacy, responsible use, and appropriate human validation

11- TOOLS AND LIBRARIES
• Python, NumPy, pandas, Matplotlib, Seaborn, scikit-learn, SciPy, Statsmodels, TensorFlow, Keras, and PyTorch
• Jupyter Notebook, Anaconda, Visual Studio Code, Git, GitHub, SQL, Excel, and Power BI when useful to the project
• Additional libraries may be introduced depending on the selected specialization and dataset

12- PROJECTS, RESEARCH, AND INTERVIEW PREPARATION
• Complete projects covering data preparation, model development, evaluation, interpretation, and presentation of results
• University assignments, dissertations, theses, research projects, portfolio projects, technical interviews, and professional applications
• Code review, debugging, documentation, reproducibility, model comparison, and communication of results

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B- PERSONALIZED TUTORING: LEARNING HOW TO REASON
Machine learning and artificial intelligence become much more accessible when mathematics, algorithms, Python code, data, and real-world applications are clearly connected.

My lessons help you move beyond simply copying code or using models as “black boxes.” You will learn how to define the problem correctly, prepare the data, select an appropriate algorithm, understand how it works, train and evaluate the model, diagnose errors, improve performance, and interpret results rigorously and responsibly.

Each lesson is personalized according to your current level, mathematical background, programming experience, dataset, university work, research project, interview preparation, or professional objective. We begin by identifying your existing knowledge, software environment, expected outcomes, and main conceptual or technical difficulties. We then establish a structured learning plan.

The first lesson combines a discussion of your background, objectives, and tutoring needs; an initial assessment of your current knowledge; personalized planning and organization of future sessions; and a short trial lesson to determine the most effective learning approach.

A typical session may include conceptual explanation, development of mathematical intuition, live coding, guided implementation, model evaluation, technical problem solving, and a concise summary of the next steps.

You may work with your own dataset, university assignment, research project, or professional problem, provided that confidential information is handled appropriately. I can also provide structured examples and datasets suited to your level.

My goal is not simply to help you run an algorithm. It is to help you understand why it is appropriate, how it learns from data, how to evaluate it correctly, why it may fail, and how to build a reliable, interpretable, and scientifically rigorous solution.

Baia

4.74

(27)

I'm a working MEP engineer, currently building Python automation for Revit workflows daily - plan checks, model coordination, and repetitive drafting tasks. I teach other engineers, architects, and BIM professionals how to do the same, using pyRevit and real project workflows, not toy examples.

Topics include:
pyRevit fundamentals and setup
Automating repetitive Revit tasks (model checks, plan generation, data extraction)
Writing custom scripts for your firm's specific workflows
Applying Python automation to real MEP/BIM projects

Neha

4.74

(27)

Learn how to build modern websites and mobile applications from scratch and develop the practical skills needed for real-world projects.

In this Frontend & Mobile Development course, I’ll guide you step by step through the technologies and tools commonly used by developers, including HTML, CSS, JavaScript, Angular, Git, GitHub, VS Code, and mobile application development.

You’ll learn how to create responsive and interactive websites, build structured applications with Angular, manage your code using Git and GitHub, and use VS Code efficiently for development. We’ll also explore how frontend technologies can be used to create mobile applications.

The course is suitable for beginners and intermediate learners. Lessons are practical and project-based, so you’ll learn by actually building websites and applications rather than only studying theory.

My goal is to help you understand how and why the code works, improve your problem-solving skills, learn professional development practices, and gain the confidence to create your own projects independently.

Lessons can be adapted to your experience level, learning pace, and personal or career goals.