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
Deep Learning
Machine learning
Business intelligence
Case Studies
Data visualization
Statistical analysis
Learner types for data science class
Data Science for advanced
Data Science for adults
Data Science for intermediate
ADHD
Data Science for beginners
Your data science tutor also teaches
Data Analysis
Data Science
Tableau
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15 days Refund
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Free Tutor Swap
Data Science concepts taught by Yeriko
The Tutor and Student explored predictive modeling concepts, starting with Y and Y-hat, and applying them to flight simulation scenarios. They discussed data generation, model fitting, and error evaluation, and concluded by demonstrating how to place a trade order using Python and the Interactive Brokers API.
Predictive Modeling with Y-hat
Linear Regression: The Foundation
Data Simulation and Generation
Model Evaluation: Finding the Best Fit
The student and tutor explored financial data analysis with Python, focusing on API integration and data processing. They worked on connecting to the IBKR API, troubleshooting connection errors, and implementing data fetching and analysis techniques, including statistical modeling for financial predictions. The session also involved debugging Python environments and package installations.
API Keys and Authentication
Connecting to Brokerage APIs (IBKR Example)
DataFrames and Data Manipulation in Pandas
Statistical Concepts: Z-scores and Confidence Intervals
Predictive Modeling: ARMA and Linear Regression
The tutor reviewed fundamental concepts of trigonometry, including the relationship between circles, right triangles, and trigonometric functions (sine, cosine, tangent, and their reciprocals). The student practiced identifying angles on the Cartesian plane based on coordinates and understood how these relate to trigonometric values, with plans to reinforce these concepts through practice problems in future sessions.
Relationship between Coordinates
Angles
and Trigonometric Values
Functions and the Vertical Line Test
Unit Circle and Coordinates
Trigonometric Ratios: SOH CAH TOA
The student and tutor worked on Python programming, focusing on data storage and retrieval using custom functions within Jupyter Notebooks. They practiced creating functions to save and import data frames, organizing code into modular Python files, and establishing templates for efficient project setup. The next steps involve the student practicing these concepts independently.
Python Functions and Modules
Data Storage and Retrieval (PKL Files)
Organizing Code with Templates and Folders
APIs and Inter-System Communication
The Tutor and Student explored data science techniques for financial analysis, focusing on Principal Component Analysis (PCA) and data processing for machine learning models. They practiced fetching financial data, normalizing it, and transitioning code from notebooks to terminal scripts for efficiency. The next session will involve reviewing the Student's setup and data acquisition process.
Batch Processing and Memory Management
Terminal vs. Notebooks
Data Wrangling and Feature Engineering
Data Normalization and Scaling
Principal Component Analysis (PCA)
The Student and Tutor explored Principal Component Analysis (PCA) and clustering techniques, applying them to a music dataset to understand song energy based on texture and dynamics. They discussed data preprocessing, including normalization and scaling, and explored methods for determining the optimal number of clusters. The session concluded with a plan to apply similar techniques to a finance project in future sessions.
Clustering Analysis
Exploratory Data Analysis (EDA)
Data Preprocessing: Scaling and Normalization
Supervised vs. Unsupervised Learning
Principal Component Analysis (PCA)
Teaching tools used by data science tutor
Google Colab
RStudio
Jupyter Notebook
Interactive data science classes
Record lessons
Note taking
Parent feedback
Open Q&A
Pets are welcomed
Data Science tutors on Wiingy are vetted for quality
Every tutor is interviewed and selected for subject expertise and teaching skill.