Programación, SQL y Python con Ingeniera de Datos en activo

From 29 € /h

Soy Ingeniera Informática por la Universidad Carlos III de Madrid y trabajo como Data Engineer, construyendo plataformas de datos en la nube con Snowflake y Azure. En mis clases aprenderás lo que de verdad se usa en el mundo profesional, explicado de forma clara y práctica.

Puedo ayudarte con:
- Programación desde cero: lógica, Python, Java o C
- Bases de datos y SQL, desde consultas básicas hasta nivel avanzado
- Análisis de datos, Excel y herramientas digitales
- Apoyo en asignaturas de Bachillerato, FP y universidad
- Orientación para quienes quieren reconvertirse al sector de datos

Cada clase se adapta a tu nivel y objetivos, con ejercicios y pequeños proyectos prácticos para que lo aprendido se quede. Doy clases en español e inglés (viví 4 años en EE. UU.).

Extra information

Necesitas un ordenador con conexión a internet. Antes de la primera clase te indicaré si hace falta instalar algo.

Location

Online from Spain

About Me

¡Hola! Soy Ana, Ingeniera Informática y Data Engineer. Trabajo cada día con datos, SQL y la nube, así que en mis clases no solo aprenderás la teoría, sino cómo se aplica de verdad en el mundo profesional.

Me encanta explicar lo complicado de forma sencilla. Mis clases son prácticas y cercanas: empiezo por entender tu nivel y tu objetivo (aprobar un examen, aprender a programar desde cero o dar el salto al sector tecnológico) y diseño las sesiones a tu medida, con ejercicios y pequeños proyectos.

Con 15 años me fui a estudiar a Estados Unidos, donde viví 4 años, así que sé lo que es enfrentarse a algo nuevo y lo importante que es un buen profesor. Puedo dar las clases en español o en inglés.

Si te apetece aprender, ¡escríbeme y hablamos!

Education

Grado en Ingeniería Informática – Universidad Carlos III de Madrid (2020–2025)
Año de intercambio en Computer Science and Engineering – University of Arkansas, EE. UU. (2023–2024)

Experience / Qualifications

- Data Engineer en Eurovision Services desde 2024: plataformas de datos en la nube con Snowflake y Azure, pipelines de datos e inteligencia artificial.
- Experiencia dando clases particulares.
- Dominio de SQL, Python, Java, C y JavaScript.
- Inglés nivel C1–C2.

Age

Preschool children (4-6 years old)

Children (7-12 years old)

Teenagers (13-17 years old)

Adults (18-64 years old)

Seniors (65+ years old)

Student level

Beginner

Intermediate

Advanced

Duration

45 minutes

60 minutes

90 minutes

120 minutes

The class is taught in

English

Spanish

Skills

Computer programming

Web development

Database

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

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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.

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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.

Salah

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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.

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Whether you're a beginner looking to build your first web project or an experienced developer aiming to refine your skills, our customized tutoring sessions are designed to help you achieve your goals.

I really happy to teach people who will really want to part of my lesson.

My teaching style is the step by step oriented. We will start from basic and move towards advanced topics.

A very warm welcome my students on apprentus.com

Wish you all the best.

Patrick

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I offer professional support in sampling, surveying, data collection, and quantitative data management. This service is designed for organizations, researchers, students, NGOs, and businesses seeking to design reliable surveys and effectively utilize their data. My services include defining appropriate methodologies, designing data collection tools, fieldwork supervision, quality control, and database organization and structuring. Support is also available for analyzing and interpreting results. My services are flexible and tailored to specific needs.

Mariem

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Mohamed

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Python is one of the most widely used languages today, thanks to its simplicity and power. You'll learn how to write your first programs, manipulate data, automate tasks, and understand the essential foundations of modern programming.
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Wissem

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Lessons focus on logic, basic programming, and practical exercises.
Classes are adapted to the student’s pace.
Students can choose between website or mobile app development.Hands on dev

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Module 1: Beginner Level – The Fundamentals
The goal is to understand the ecosystem and launch your first website.
Introduction to the Web: Understanding the Domain Name /
Accommodation.
Installation: Setting up WordPress (locally or with a hosting provider). Exploring the interface: Exploring the dashboard. Content management: Crucial difference between Posts and Pages.
Using the block editor (Gutenberg). Managing the media library (images, videos, PDFs). Appearance: Choosing, installing and configuring a free theme.
Extensions (Plugins): What are they and how to install them safely? Module 2: Intermediate Level – Customization and Optimization
The goal is to transform a basic website into a professional and efficient tool.
Advanced customization: Configuration of navigation menus and widgets.
Forms and Interaction: Create a professional contact form and integrate a Google Maps map.
Search Engine Optimization (SEO): Install and configure an SEO plugin (Yoast or RankMath) to be visible on Google.
Security and Maintenance: Implementation of automatic backups
Protection against spam and hacking attempts.
Performance: Optimize loading speed (image caching and compression). Pedagogical Methodology
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Experienced Programming Language Tutor with 10+ years of teaching experience in Python, HTML, CSS, and JavaScript. I provide step-by-step, practical-oriented learning designed for beginners, students, and aspiring developers. My teaching approach focuses on real-world projects, hands-on coding exercises, and easy-to-understand explanations to help students build strong programming skills confidently.

What You Will Learn:
Python Programming Fundamentals to Advanced Concepts
HTML for Website Structure
CSS for Modern Web Design & Styling
JavaScript for Interactive Web Development
Real Projects and Practical Assignments
Debugging and Problem-Solving Techniques
Frontend Development Basics
Teaching Style:
Step-by-step guidance
Beginner-friendly explanations
Practical and project-based learning
Live coding sessions
Personalized support and doubt clearing
Easy learning methods with real examples

Perfect for:

School & college students
Beginners in coding
Future web developers
Anyone wanting practical programming skills

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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.

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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

منال

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