

The email address and phone number of Daniel have been verified.
Daniel
- Rate UGX 63,185
- Response 14h
-
Students5
Number of students Daniel has accompanied since arriving at Superprof
Number of students Daniel has accompanied since arriving at Superprof

UGX 63,185/hr
Unfortunately, this tutor is unavailable
- Computer programming
- Python
- Programming languages
- SQL
- Artificial Intelligence
Data scientist from the Universidad Javeriana teaches artificial intelligence, machine learning and programming. With experience in programming and statistics tutoring.
- Computer programming
- Python
- Programming languages
- SQL
- Artificial Intelligence
Lesson location
Recommended
Daniel is a respected member of our tutor community. He is highly recommended for his commitment and the quality of his lessons. An excellent choice to progress with confidence.
About Daniel
Hello, I'm Daniel and I love math, artificial intelligence, and teaching. I've been a C++ tutor at the university and I've also given statistics classes to colleagues and classmates. I'm a Data Science student at the Javeriana University in my final year, already writing a thesis. I've had outstanding grades in subjects such as statistical inference, multivariate analysis, integral calculus, among others. I also have a company where I work as a data scientist.
About the lesson
- Primary
- Secondary
- Tertiary
- +11
levels :
Primary
Secondary
Tertiary
University
Adult Education
Masters
Diploma
PhD (Doctor of Philosophy)
MBA
Early childhood education
Beginner
Intermediate
Advanced
Children
- English
All languages in which the lesson is available :
English
1. Initial diagnosis
In the first class, the student's foundations will be assessed through key questions and exercises. This will allow the teaching to be tailored to their level and needs, ensuring that everyone has a solid foundation before moving forward.
2. Approach based on understanding and intuition
Learning will focus on developing the intuition to program solutions and understand the concepts, before going directly to the code.
3. Class structure
Each session follows an organized outline:
Introduction and objective: Brief explanation of what will be learned and its usefulness.
Prior knowledge assessment (first class only): Key questions to detect strengths and gaps in knowledge.
Conceptual explanation: Use of graphics, simulations and applied examples.
Practical development: Guided resolution of real exercises and problems.
Active learning: Questions, debates and group problem solving.
Assessment and reinforcement: Small tests or challenges to reinforce knowledge.
4. Interactive and active learning
Questions and challenges: Critical thinking is encouraged rather than memorization.
Using technology: Software like R or Python
Datasets: handling all types of datasets from kaggle to data.gov, web scrapping and more
Real cases: Applications in science, economics and engineering to contextualize the theory.
5. Flexibility and customization
The difficulty is adapted according to the level of the students.
Optional exercises and supplementary material are encouraged.
Consultation spaces are opened to resolve doubts outside of class.
6. Continuous evaluation without pressure
Instead of traditional exams, mini-challenges are applied to reinforce key concepts.
Self-assessment is encouraged with applied exercises and projects.
Recommendations
Recommendations come from relatives, friends and acquaintances of the teacher
Me ayudo con mi clase de fundamentos del aprendizaje automatico, fue muy paciente, explico todo al detalle y me ayudo a solucionar problemas de la vida REAL con lo aprendido en clase
Recomiendo el profesor Daniel porque siempre busca métodos que se adapten a la forma de aprender del estudiante.
Conocí a Daniel en la universidad y desde el primer semestre supe que era un fuera de serie en todo lo relacionado con estadística, probabilidad e inferencia. Mientras muchos nos rompíamos la cabeza tratando de entender algunos conceptos, él no solo los dominaba, sino que tenía una forma increíble de explicarlos. Recuerdo más de una vez quedarme después de clase con él, preguntándole sobre temas de machine learning, y en lugar de darme respuestas rápidas, me guiaba paso a paso hasta que todo tenía sentido.
Más que un compañero, Daniel se convirtió en una referencia para muchos de nosotros. No importa qué tan complicado sea el tema, siempre encuentra la manera de hacerlo digerible, con ejemplos prácticos y analogías que realmente conectan con el contexto de cada persona. Además, tiene una paciencia increíble, lo que hace que aprender con él sea una experiencia motivadora en lugar de frustrante.
Si hay alguien que realmente domina y sabe enseñar estos temas, es Daniel. No solo por su conocimiento técnico, sino por su capacidad de hacer que otros también lo comprendan. Lo recomendaría sin dudarlo para cualquier rol donde su talento pueda ayudar a formar y guiar a más personas.Conocí a Daniel en la universidad, y sinceramente, no sé si habría llegado a séptimo semestre sin su ayuda. Es un verdadero crack en estadística, probabilidad, inferencia estadística y machine learning. Muchas veces me quedaba atascado en conceptos complejos, y él, con una paciencia infinita y una claridad impresionante, lograba explicármelos de una forma que realmente tenía sentido para mí.
Lo que más admiro de él es que no solo entiende estos temas a un nivel profundo, sino que también sabe enseñarlos. No se limita a repetir fórmulas o definiciones, sino que usa analogías y ejemplos prácticos que hacen que hasta lo más complicado se vuelva intuitivo. Además, su pasión por estos temas es contagiosa; es de esas personas con las que hablar de estadística o machine learning no solo es útil, sino también interesante.
Si alguien busca un profesor que de verdad domine estas áreas y, más importante aún, que sepa transmitir ese conocimiento de manera clara y efectiva, Daniel es la persona indicada. Lo recomiendo con los ojos cerrados.
View more recommendations
Rates
Rate
- UGX 63,185
Pack prices
- 5h: UGX 278,016
- 10h: UGX 568,668
online
- UGX 63,185/hr
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