Yeriko Vargas

Learn coding & data science in a hands-on way

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Yeriko Vargas

Masters degree

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Each lesson is 55 min

50 lessons


20% off

/ lesson

30 lessons


15% off

/ lesson

20 lessons


10% off

/ lesson

10 lessons


5% off

/ lesson

5 lessons


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1 lessons


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/ lesson

Yeriko - Know your tutor

Hello! I'm Yeriko Vargas, your guide to the world of Python, statistics, and machine learning. With a master's degree in statistics and hands-on experience at Ford and Chrysler, I've got a knack for making numbers tell stories and solving real-world problems. My work has involved everything from predicting trends with linear regression to improving car safety using machine learning. I love turning complex ideas into something anyone can grasp, whether we're talking probability or programming. As a Teaching Assistant, I've helped students tackle tough topics and gain confidence. I'm here to help you get comfortable with data, dive into Python coding, and unlock the exciting possibilities of machine learning

Yeriko graduated from Oakland University

Yeriko graduated from Oakland University
Yeriko graduated from Oakland University

Programming tutor specialities

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Debugging

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Homework help

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Exam prep

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Job readiness

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Upskilling

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Assignment help

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Project help

Learner for programming class

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ADHD

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College

Programming class overview

I specialize in tutoring students for homework, exams, and especially big exams, building understanding from the ground up. My approach is comprehensive, covering both theory in statistics and mathematics and practical Python coding. This ensures you're not just exam-ready but also equipped with essential programming skills. I emphasize Python solutions alongside statistical concepts, offering a dual-track learning path. My aim is to prepare you not just for academic success but for a thriving career in data science. With me, you get the tools and knowledge to excel in both the classroom and the professional world.

Your programming tutor also teaches

App Development

App Development

Computer Science

Computer Science

Databases

Databases

Flexible Scheduling

Allows 1h early scheduling

Allows 1h early rescheduling

Can wait for 20 mins after joining

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10 day Refund

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Computer Science concepts taught by Yeriko

Student learned 2 days ago

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.

Principal Component Analysis (PCA)

Data Normalization and Scaling

Data Wrangling and Feature Engineering

Terminal vs. Notebooks

Batch Processing and Memory Management

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Student learned 4 days ago

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.

Principal Component Analysis (PCA)

Clustering Analysis

Exploratory Data Analysis (EDA)

Data Preprocessing: Scaling and Normalization

Supervised vs. Unsupervised Learning

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Student learned 6 days ago

The Tutor and Student explored the concept of APIs and their application in financial data analysis, specifically using Yahoo Finance. They discussed anomaly detection techniques in machine learning, including cluster analysis and random forests, and touched upon fundamental statistical concepts like normality and data labeling (supervised vs. unsupervised learning). Future sessions were planned to delve deeper into PCA and its combination with clustering.

APIs and Data Fetching

DataFrames and Data Manipulation

Anomaly Detection

Cluster Analysis

Supervised vs. Unsupervised Learning

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Teaching tools used by tutor

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Visual Studio Code

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Jupyter Notebook

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Google Colab

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Git & GitHub

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Xcode

Dynamic programming classes

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Parent feedback

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Record lessons

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Pets are welcomed

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Open Q&A

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Note taking

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