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
Predictive modeling
Machine learning
Data visualization
Assignment help
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
Data Science for advanced
Data Science for intermediate
Data Science for adults
ADHD
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 focused on debugging a Python script to download historical commodity data from IBKR, successfully implementing a function to resolve the issue. They also reviewed key machine learning concepts, including logistic, multinomial, and multivariate regression. They scheduled a follow-up session to delve into generating synthetic data and advanced machine learning techniques.
Multinomial & Multivariate Regression
Python API Request Encapsulation for Stability
Handling Asynchronous Event Loops in Jupyter/Python
Introduction to Synthetic Data Generation
Logistic vs. Linear Regression
Downloading Historical Commodity Data with IB Insync
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
Organizing Project Files for Reproducibility
R Package Management with `library()`
Efficient R Script Execution Strategies
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.
Grouping Data for Comparative Analysis
Introduction to Probability Concepts in Data
Analyzing Relationships Between Variables
Feature Engineering: Creating New Variables
Understanding Data Variables and Structures
Handling Missing Values (NA
NaN) in R
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
Teaching tools used by data science tutor
Streamlit
Google Colab
Jupyter Notebook
RStudio
Looker
Interactive data science classes
Note taking
Pets are welcomed
Parent feedback
Open Q&A
Record lessons
Data Science tutors on Wiingy are vetted for quality
Every tutor is interviewed and selected for subject expertise and teaching skill.
