Adalina - Computer programming tutor - San Diego

1^st lesson free

Adalina

Rate R413
Response 1h

R413/h

1^st lesson free

Computer Programming
Python

University of California, San Diego Data Science graduate teaches Python coding from middle school to high school in San Diego

Name: Adalina
Address: San Diego, ZA
Telephone: undefined
Price range: R413

Computer Programming
Python

Lesson location

- online

About Adalina

About Me as a Tutor I am an experienced Data Science and Machine Learning tutor with a strong academic background and hands-on project experience. As a college senior specializing in Data Science with a minor in Cognitive Science, I have worked extensively with machine learning models, neural networks, clustering techniques, and real-world applications of AI. My expertise spans Python, statistical modeling, and deep learning frameworks, and I have applied these skills in both academic research and practical projects. My Teaching Philosophy I believe that anyone can master data science and machine learning with the right guidance. My approach is: Concept-First Learning – I break down complex topics into intuitive, real-world explanations. Hands-On Problem Solving – Learning happens by doing, so I emphasize coding exercises and debugging strategies. Personalized Guidance – Every student has a unique learning style, and I tailor my lessons accordingly. Real-World Applications – I connect theoretical knowledge to industry use cases to make learning engaging and practical. What to Expect from My Lessons Clear Explanations – No jargon overload. I ensure you understand the “why” behind every concept. Interactive Coding Sessions – Whether it’s KNN, neural networks, or clustering, we’ll work through real datasets together. Debugging & Troubleshooting Skills – I teach not just how to write code, but how to fix it when things go wrong. Confidence Building – I guide students to think critically and solve problems independently.

About the lesson

My Approach as a Tutor Teaching Method & Techniques My tutoring style is hands-on, adaptive, and problem-solving-oriented. I focus on: Conceptual Understanding First – Before diving into coding or equations, I ensure students grasp the "why" behind concepts. Real-World Applications – I relate topics to practical examples, especially in data science, machine learning, and cognitive science. Guided Problem-Solving – Instead of giving direct answers, I ask leading questions to help students think critically and build confidence. Iterative Learning – I encourage testing ideas, debugging, and reflecting on mistakes as a way to deepen learning. Visual Aids & Intuition-Based Learning – I use visualizations, analogies, and code walkthroughs to make abstract topics clearer. A Typical Lesson Plan Example: Understanding K-Nearest Neighbors (KNN) with EEG Data Warm-Up & Review (5-10 min) Briefly discuss last session or foundational concepts (e.g., distance metrics in machine learning). Quick discussion on why KNN matters in classification problems. Concept Breakdown & Interactive Discussion (20 min) Explain KNN with an analogy (e.g., "finding similar friends based on interests"). Show a step-by-step breakdown of train/test split vs. LOOCV using real-world EEG data. Engage in a short discussion: When would LOOCV be better than a normal split? Hands-on Practice & Code Implementation (30 min) Walk through a Jupyter notebook together, visualizing KNN clusters. Debug errors together (teaching debugging strategies). Challenge the student with an open-ended question (e.g., How would KNN perform with high-dimensional data?). Wrap-Up & Reflection (5-10 min) Review key takeaways. Assign a mini-project (e.g., Apply KNN to another dataset and compare results). What Sets Me Apart as a Tutor Personalized Learning Paths – I adapt my teaching to the student's pace, strengths, and goals. Interdisciplinary Approach – My background in data science & cognitive science allows me to connect machine learning with human decision-making. Debugging & Problem-Solving Focus – Many students struggle with troubleshooting. I teach them how to break down problems logically. Collaboration-Oriented – I approach sessions like a peer discussion rather than a lecture, which makes students more comfortable exploring ideas. Who My Lessons Are For Level: Undergraduate & Graduate Students in Data Science, Cognitive Science, or CS Beginners to Intermediate learners in Machine Learning or Python Students working on projects involving AI, ML, clustering, or neural networks

Rates

Rate

R413

Package rates

5h: R2067
10h: R4135

online

R413/h

free lesson

The free first lesson with Adalina allows you to get to know the tutor and discuss your needs and expectations.

University of California, San Diego Data Science graduate teaches Python coding from middle school to high school in San Diego

Lesson location

About Adalina

About the lesson

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Package rates

online

free lesson

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University of California, San Diego Data Science graduate teaches Python coding from middle school to high school in San Diego

Lesson location

About Adalina

About the lesson

Rates

Rate

Package rates

online

free lesson

Similar Computer Programming tutor profiles

Oleksandr

Bryce

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Senzosenkosi

Armandt

Wendy

Charles

Newman Blessing

Satish

Chihebe

Tendani

Meezaan

Joseph

Haritha

Cole

Tino

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