Yeriko Vargas
Computer Science Tutor — Python, Machine Learning, SQL, Real-World Projects & Assignment Help
Show all photos
Yeriko Vargas
Masters degree
/ 55 min
About your coding tutor - Yeriko
Hey — I’m Yeriko Vargas. If you’ve ever sat there like “why does none of this make sense?” — that’s exactly where I come in. I teach Python, stats, and machine learning in a way that actually clicks. No robotic lectures, no memorizing random formulas — we break things down, step by step, until you get it. I’ve built real models at Ford Motor Company and Chrysler, but more importantly, I know how to explain things in a way students understand. My whole focus is helping you go from lost → confident as fast as possible. We learn by building real stuff — not fake textbook problems. One of my recent projects is a music recommendation system that thinks like a DJ. It breaks songs into energy, texture, and dynamics using signal processing, then uses PCA + clustering to organize them into “states.” From there, it picks the next track based on similarity, BPM, key, and flow. So instead of guessing, you’re actually understanding how intelligent systems make decisions. I can help you with: * Python (Pandas, NumPy, Jupyter — all the real tools) SQL + working with real datasets * Statistics & probability (finally making sense of it) Machine learning (without the confusion) * Assignments, projects, exam prep Whether you’re stuck on homework, building a project, or just trying to understand what’s going on — I’ve got you. We’re not just learning… we’re making this stuff make sense. 🚀
Meet Yeriko
Yeriko graduated from Oakland University
Coding tutor specialities
Code Review
Debugging
Project help
Assignment help
Competitive Programming
Upskilling
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 coding classes
ADHD
Coding for adults
Coding for intermediate
Coding for beginners
Coding for advanced
Yeriko - Coding tutor also teaches
Coding for kids
Matlab
R Programming
Coding concepts taught by Yeriko
Student and Tutor debugged R code for an economics assignment, focusing on resolving a "path does not exist" error encountered when importing an Excel dataset into RStudio. They practiced correcting file paths and organizing files. The Student also discussed an upcoming midterm and a future assignment.
Resolving "Path Does Not Exist" Errors in R
R Package Management with `library()`
Efficient R Script Execution Strategies
Organizing Project Files for Reproducibility
The Student and Tutor worked through a statistics homework assignment using R programming, focusing on data manipulation, descriptive statistics, and visualization techniques. They covered topics including variable transformation, calculating central tendencies and dispersion, interpreting skewness and z-scores, and analyzing categorical data. The Tutor will provide a detailed R script with comments and a Word document of answers for the Student to review and complete.
R Studio Environment & Data Import
Data Preparation: Missing Values & Variable Transformation
Descriptive Statistics & Data Visualization
Z-scores & Data Normalization
Population vs. Sample Measures (Variance & Standard Deviation)
Categorical Data Analysis: Frequency & Group Means
The session was a demo lesson focused on R Studio programming for an undergraduate Economics course (Econ 221). The Student sought help with a homework assignment involving data analysis concepts such as identifying missing values, understanding variables, constructing new variables, analyzing relationships, and calculating probabilities. The Tutor explained these concepts in detail, relating them to economics and discussing a plan to work through the homework questions and prepare for an upcoming midterm.
Handling Missing Values (NA
NaN) in R
Understanding Data Variables and Structures
Feature Engineering: Creating New Variables
Analyzing Relationships Between Variables
Grouping Data for Comparative Analysis
Introduction to Probability Concepts in Data
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.
Python Environment Management (Conda)
Understanding Kernels in Computing
Machine Learning vs. Deep Learning
MIDI for Music Representation
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.
User Interface Elements: Pop-ups and Zoom Features
Website Content Renaming and Hyperlinking
Responsive Design and Alignment
Website Image Optimization
Affiliate Disclosure Banners
AI Chatbot Development Stages
Approach & tools used by coding tutor
NetBeans
Postman
Git & GitHub
Android Studio
Google Colab
Jupyter Notebook
Visual Studio Code
Hands-on coding classes
Note taking
Record lessons
Pets are welcomed
Open Q&A
Parent feedback
Programming tutors on Wiingy are vetted for quality
Every tutor is interviewed and selected for subject expertise and teaching skill.
