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Daniel
- Rate $17
- Response 14h
-
Students5
Number of students Daniel has accompanied since arriving at Superprof
Number of students Daniel has accompanied since arriving at Superprof

$17/hr
This teacher is currently unavailable
- Maths
- Algebra
- Statistics
- Linear Algebra
Data scientist from Javeriana University teaches college mathematics including statistics, algebra and calculus. Experienced in tutoring classes. Includes lessons in the R language
- Maths
- Algebra
- Statistics
- Linear Algebra
Lesson location
Recommended
Daniel is a respected tutor in our 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, as well as teaching. I have been a C++ tutor at the university and I have also given statistics classes to colleagues and classmates. I am a Data Science student at the Javeriana University in my final year already writing a thesis. I have 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
- High School
- NCEA Level 1
- +7
levels :
Primary
High School
NCEA Level 1
NCEA Level 3
Adult education
Undergraduate
Masters
Diploma
Doctorate
Other
- 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 mathematical intuition before introducing formalisms. Real-life examples will be used to connect concepts to practical applications.
3. Use of accessible reference books
Each topic will be supported by free books available online, allowing students to reinforce their learning independently. A list of references will be provided for each unit.
4. 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.
5. Interactive and active learning
Questions and challenges: Critical thinking is encouraged rather than memorization.
Use of technology: Software such as GeoGebra, R or Python to visualize concepts.
Real cases: Applications in science, economics and engineering to contextualize the theory.
6. 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.
7. 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.
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Rates
Rate
- $17
Pack prices
- 5h: $72
- 10h: $150
online
- $17/h
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