Yeriko Vargas
Data Science Help for College — Python, Stats, ML (Assignments + Projects) + Real Understanding
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Yeriko Vargas
Masters degree
/ 55 min
About your data science tutor
HeY! I’m Yeriko. I teach Python, statistics, and machine learning by building real, high-impact projects — not just theory. We work with sound, video, and real datasets so concepts actually stick and translate into real skills. I’ve built models at Ford Motor Company and Chrysler, and I bring that same production-level thinking into every session. This isn’t “tutorial-style” learning. This is how data science actually works in the real world. Here, you won’t just code, you’ll learn how to: Think like a data scientist Structure messy data into usable systems Build models that actually solve problems Communicate insights like a pro From predictive modeling to full ML pipelines, everything is broken down in a way that’s clear, practical, and immediately usable — no fluff, no confusion. One example: I’ve built a music recommendation system that treats audio as structured data. Instead of guessing songs, the system extracts features like energy, texture, and dynamics using signal processing, then uses PCA + clustering to organize tracks into “states.” From there, it selects the next track using similarity scoring + constraints like BPM, key, and energy flow — essentially modeling how a DJ thinks, but powered by machine learning. That’s the core idea: take something complex → break it into data → build an intelligent system around it. On top of that, I’m strong in SQL and tools like Tableau, so we go beyond modeling — we cover the full pipeline: data extraction → transformation → modeling → visualization → decision-making.
Meet Yeriko
Yeriko graduated from Oakland University
Data Science tutor skills
Business intelligence
Case Studies
Assignment help
Predictive modeling
Statistical analysis
Data engineering
CoTutorAI modules
Summary
Podcast
Quiz
Learnings
Flashcard
Spotlight
Zero Risk Guaranteed
15-days refund
Free tutor swap
No cancel fee
1-yr validity
24/7 support
Learner types for data science class
ADHD
Data Science for adults
Data Science for intermediate
Data Science for advanced
Data Science for beginners
Your data science tutor also teaches
Data Analysis
Data Science
Tableau
Data Science concepts taught by Yeriko
The student and tutor worked on establishing Python connectivity for trading applications, troubleshooting API errors, and understanding programming kernels. They then explored the differences between machine learning and deep learning, followed by a discussion on applying data science to music composition using MIDI notation. The session concluded with the creation of a dedicated Python environment for music-related packages.
MIDI for Music Representation
Machine Learning vs. Deep Learning
Understanding Kernels in Computing
Python Environment Management (Conda)
The student and tutor explored the distinctions between supervised and unsupervised machine learning, particularly in the context of computer vision and data representation. They discussed how images and music can be converted into numerical data for analysis and how this can be applied to various fields. Future sessions were planned to cover setting up development environments and further explore music generation projects.
Supervised vs. Unsupervised Learning
Computer Vision: Pixels to Vectors
Feature Extraction and Data Representation
Clustering and Pattern Recognition
The student and tutor worked on various website development tasks, including image optimization, affiliate link integration, and content/styling adjustments on a WordPress site. They planned follow-up sessions to continue these improvements and discuss the potential implementation of a chatbot feature.
Website Content Renaming and Hyperlinking
Responsive Design and Alignment
Website Image Optimization
Affiliate Disclosure Banners
User Interface Elements: Pop-ups and Zoom Features
AI Chatbot Development Stages
The Student presented their WordPress website, detailing specific requirements for mobile optimization of H1 headings, integration of Amazon affiliate banners, and removal of embedded theme text from image banners. The Tutor provided an initial assessment and outlined a plan to implement these web development and design changes, with a follow-up session scheduled to transfer the updated files.
Mobile Responsiveness: H1 Headings & Centering
WordPress Theme Customization & Content Management
Website Performance & SEO Foundations
User Experience (UX) & Design Refinements
Amazon Affiliate Banner Integration
The Student and Tutor engaged in a wide-ranging discussion covering principles of aerodynamics, the application of AI and statistics in fields like rocketry and medical diagnostics, and personal data tracking for self-improvement. The Student also demonstrated and explained techniques for playing complex drum rhythms. They made plans to cover Python and app development in their next session.
Personal Data Analytics for Self-Optimization
Outliers: Rethinking Deviations in Data & Society
Rocket Nozzle Fluid Dynamics & Optimization
Data-Driven AI & Predictive Modeling
Statistics as a Framework for Experimentation & Proof
Exponential Growth & Focused Skill Development
The Student and Tutor engaged in a comprehensive discussion on core Data Science concepts, including data normalization, different types of statistical distributions, and the classification of variables. They explored the stages of Exploratory Data Analysis (EDA), covering data imputation methods and the significance of residuals in statistical modeling. The session also introduced the foundational principles of Artificial Intelligence, explaining its operational mechanics through the analogy of neurons and discussing its real-world applications. The Student expressed interest in learning about optical recognition and applying these concepts to personal data analysis for self-improvement, which was noted for future lessons.
Data Normalization and Distributions
Exploratory Data Analysis (EDA) & Imputing Missing Data
Categorical vs. Numerical Variables & Predictive Models
Residuals and Data Variance
Introduction to Artificial Intelligence (AI) and Neural Networks
Teaching tools used by data science tutor
Google Colab
RStudio
Jupyter Notebook
Interactive data science classes
Pets are welcomed
Record lessons
Open Q&A
Parent feedback
Note taking
Data Science tutors on Wiingy are vetted for quality
Every tutor is interviewed and selected for subject expertise and teaching skill.