Teaching is one of my greatest passions. I have transitioned from teaching piano to children, to STEM-related courses at the college level, and later to programming and IT at my workplace. Additionally, I have volunteered to give talks on financial education.

Within the academic field, I have served as a Teaching Assistant 51 times across 15 courses, collaborating closely with 16 professors. Since May 2024, I have been appointed as a Lecturer at UTEC, where I am responsible for teaching postgraduate courses in the Master's program in Data Science and AI, as well as undergraduate courses in the Data Science, Computer Science, and Management & Digital Business programs.

Teaching History (Lecturer)

University of Engineering and Technology

2025-1

DS4341: Big Data (Data Science Major)

AD3005: Introduction to Data Analytics and Big Data (Management & Digital Business Major)

2024-3

CS8080: Data Discovery (Master's program in Data Science and AI)

2024-2

DS4341: Big Data (Data Science Major)

CS3700: Big Data (Computer Science Major)

AD3005: Introduction to Data Analytics and Big Data (Management & Digital Business Major)

2024-1

DS4341: Big Data (Data Science Major)

CS3700: Big Data (Computer Science Major)

Metropolitan University of Technology

2024-2

Visualization (Module in the Postgraduate Diploma in Data Modeling and Analysis)

Teaching History (Teaching Assistant)

Harvard University

Spring 2024

API222: Machine Learning and Big Data Analytics

Prof. Soroush Saghafian

STAT100: Introduction to Statistics and Data Science

Prof. Kelly McConville

Fall 2023

STAT100: Introduction to Statistics and Data Science

Prof. Kelly McConville

Pontifical Catholic University of Peru

2022-1

1IND51: Analytics 2

Prof. Christian Cornejo

1IND48: Analytics 1

Prof. Miguel Mejía

1EST21: Quantitative analysis for decision making

Prof. Arturo Calderón

EST218: Engineering statistics

Prof. Cristian Bayes

EST145: Statistics

Prof. Giancarlo Sal y Rosas

2022-0

1EST13: Applied statistics 2

Prof. Loretta Gasco

2021-2

1EST12: Applied statistics 1

Prof. Ana Valdivia

EST218: Engineering statistics

Prof. Cristian Bayes

EST145: Statistics

Prof. Giancarlo Sal y Rosas

EST103: Statistics

Prof. Evelyn Gutiérrez

2021-1

1EST12: Applied statistics 1

Prof. Ana Valdivia

EST218: Engineering statistics

Prof. Cristian Bayes

EST145: Statistics

Prof. Giancarlo Sal y Rosas

EST103: Statistics

Prof. Giancarlo Sal y Rosas

2021-0

1EST12: Applied statistics 1

Prof. Ana Valdivia

2020-2

IND273: Operations research 1

Prof. Eduardo Carbajal

1EST13: Applied statistics 2

Prof. Loretta Gasco

1EST12: Applied statistics 1

Prof. Ana Valdivia

EST103: Statistics

Prof. Giancarlo Sal y Rosas

2020-1

IND281: Operations research 2

Prof. Eduardo Carbajal

1EST13: Applied statistics 2

Prof. Loretta Gasco

1EST12: Applied statistics 1

Prof. Ana Valdivia

1MAT06: Differential calculus

Prof. Sergio Pavletich

2019-2

IND281: Operations research 2

Prof. Eduardo Carbajal

1EST13: Applied statistics 2

Prof. Ana Valdivia

1EST12: Applied statistics 1

Prof. Ana Valdivia

EST218: Engineering statistics

Prof. Ana Valdivia

EST145: Statistics

Prof. Giancarlo Sal y Rosas

EST103: Statistics

Prof. Giancarlo Sal y Rosas

2019-1

1EST13: Applied statistics 2

Prof. Loretta Gasco

EST218: Engineering statistics

Prof. Ana Valdivia

EST145: Statistics

Prof. Giancarlo Sal y Rosas

EST103: Statistics

Prof. Giancarlo Sal y Rosas

2018-2

1EST12: Applied statistics 1

Prof. Ana Valdivia

EST218: Engineering statistics

Prof. Ana Valdivia

EST103: Statistics

Prof. Giancarlo Sal y Rosas

2018-1

1EST13: Applied statistics 2

Prof. Loretta Gasco

1EST12: Applied statistics 1

Prof. Ana Valdivia

EST218: Engineering statistics

Prof. Ana Valdivia

2017-2

1EST13: Applied statistics 2

Prof. Carlos Véliz

1EST12: Applied statistics 1

Prof. Ana Valdivia

EST218: Engineering statistics

Prof. Maria Teresa Villalobos

EST103: Statistics

Prof. Giancarlo Sal y Rosas

1IND02: Work study laboratory

Prof. César Corrales

2016-2

EST145: Statistics

Prof. Giancarlo Sal y Rosas

EST103: Statistics

Prof. Giancarlo Sal y Rosas

IND251: Business administration and management

Prof. Jesús Luyo

2016-1

EST103: Statistics

Prof. Giancarlo Sal y Rosas

Syllabus

University of Technology and Engineering

CS8080: Data Discovery

2024-3

This theoretical-practical course guides students in the use of advanced techniques for data exploration and analysis. Throughout the data lifecycle, it will cover everything from data collection and preprocessing to exploratory analysis and feature engineering. The course incorporates visualization tools and the analysis of complex patterns that allow for the extraction of meaningful insights, optimizing decision-making across various industries. Emphasis will be placed on handling large volumes of data, time series analysis, and identifying patterns in unstructured data. Additionally, ethical and legal topics related to big data processing and the use of Big Data and Cloud Computing platforms will be included.

DS4341: Big Data

2025-0, 2024-2, 2024-1

This course, of a theoretical-practical nature, aims to address the challenges of Big Data management for the creation of data science applications and to provide skills for handling the large volumes of data that characterize the modern world. Additionally, the central theme of this course offers a global perspective on both traditional and cutting-edge data management.

CS3700: Big Data

2024-2, 2024-1

The course, of a theoretical-practical nature, is designed for Computer Science students to deepen their knowledge in storage, analysis, processing, and management of large volumes of information (terabytes, petabytes, and even exabytes). Every day, every hour, every minute, a vast amount of information is generated that needs to be processed, stored, and analyzed in real-time or near real-time. The main topics covered in this course are related to tools such as Hadoop, HDFS, MapReduce, YARN, and Spark, which enable students to work with large data sets.

AD3005: Introduction to Data Analytics and Big Data

2024-2

The course "Introduction to Data Analytics and Big Data" will provide students with the tools to understand the concepts related to managing large volumes of information and the different analytical approaches that can be applied to extract insights, with the goal of delivering maximum value to the company.

Metropolitan University of Technology

Visualization

2024-2

This module provides a comprehensive understanding of Data Visualization using R. By the end, participants will be able to grasp the importance of visualization in data analysis, create basic and advanced plots with ggplot2, develop animations with gganimate, and interactive plots with plotly. Additionally, they will design interactive dashboards using flexdashboard and visualize geospatial data by creating maps with ggmap and leaflet. Through a practical project, participants will consolidate their knowledge by applying various visualization techniques to effectively communicate the information contained in the data.

Harvard University

API222: Machine Learning and Big Data Analytics

Spring 2024

In the last couple of decades, the amount of data available to organizations has significantly increased. Individuals who can use this data together with appropriate analytical techniques can discover new facts and provide new solutions to various existing problems. This course provides an introduction to the theory and applications of some of the most popular machine learning techniques. It is designed for students interested in using machine learning and related analytical techniques to make better decisions in order to solve policy and societal level problems.

STAT100: Introduction to Statistics and Data Science

Spring 2024, Fall 2023

An application-oriented introduction to statistics and data science where students develop their data acumen and learn exploratory and inferential techniques for analyzing and modeling data. The course covers the entire data analysis process and includes topics such as data collection, data wrangling, exploratory data analysis, visualization, simple/multiple linear regression, sampling distributions, hypothesis testing, confidence intervals, data ethics, statistical communication, and reproducibility. Students develop a reproducible workflow for analyzing data in R and learn several tidyverse R packages.

Pontifical Catholic University of Peru

1IND51: Analytics 2

2022-1

It is a theoretical-practical course that aims to design models for estimation, segmentation, and pattern analysis in data analytics, and experiment with them to solve common problems in business analytics, data science, and big data projects. The course will cover applications of classification and scoring trees, clustering, association rules, logistic regression, neural networks, linear discriminant analysis, naive Bayes, support vector machines, and linear models, including assumptions review, error rate calculation, and model validation recommended in practical contexts. The course develops a business analytics project up to the phase of validation and recommendation of the analytics solution to be implemented. It contributes to the development of effective communication, teamwork performance, and autonomous learning.

1IND48: Analytics 1

2022-1

It is a theoretical-practical course that aims to design models for transforming input data and experiment with them to solve common problems in business analytics, data science, and big data projects. The course covers the main objectives of business analytics, common models, application cases, selecting target and input variables, and the difference between supervised and unsupervised learning. It introduces the use of programming with R and Python and covers the initial phases of an analytics project related to data preprocessing, exploration, visualization, and dimensionality reduction. The course develops a business analytics project up to the phase of transforming input data. It contributes to the development of effective communication, teamwork performance, and autonomous learning.

IND281: Operations Research 2

2019-2, 2020-1

The course is taught in a theoretical-practical manner and focuses on helping students effectively relate their knowledge of mathematical modeling. Students develop problem-solving skills applied to industrial engineering, covering the following topics: Network theory, Markov chains, and queuing theory.

IND273: Operations Research 1

2020-2

Introduction to Operations Research. Linear Programming. Graphical solution of linear programming problems. Formulation of linear programming problems. Multi-objective linear programming. Integer linear programming. Primal-simplex and dual-simplex methods. Sensitivity analysis.

1EST21: Quantitative Analysis for Decision Making

2022-1

This is a theoretical-practical course that aims to provide hands-on experience in problem-solving. It covers the analysis of categorical variables, simple and multiple linear regression analysis, and the examination of residual assumptions. It also includes a review of key techniques such as simple random sampling, stratified random sampling, and cluster sampling. Elements of decision theory and time series are explored as well. The course consists of theoretical classes and the development of practical case studies that apply the aforementioned topics in industrial engineering.

1EST13: Applied Statistics 2

2022-0, 2020-2, 2020-1, 2019-2, 2019-1, 2018-1, 2017-2

Introduction to parameter estimation: Point estimation and confidence intervals. Sample size.

Hypothesis testing: General principles. Hypothesis tests for mean, proportion, and variance.

Introduction to categorical data analysis: Proportion tests, goodness of fit tests, homogeneity and independence.

Introduction to experimental designs: Introduction. One and two-factor models. Parameter estimation. Applications.

Simple and multiple linear regression analysis: Introduction. Model. Parameter estimation. Applications.

1EST12: Applied Statistics 1

2021-2, 2021-1, 2021-0, 2020-2, 2020-1, 2019-2, 2018-2, 2018-1, 2017-2

The course offers topics in descriptive statistics, which involves the development of statistical techniques to organize, present, and summarize data. It prepares the student in the process of converting isolated numerical data into information that is useful for decision-making. It introduces concepts of probabilities, which includes the study of basic principles of probabilities, random variables, and the most common probabilistic models. Both parts are developed with the support of statistical software.

EST218: Engineering Statistics

2022-1, 2021-2, 2021-1, 2019-2, 2019-1, 2018-2, 2018-1, 2017-2

The course covers descriptive statistics: Introduction, organization, and treatment of data, numerical summaries of data. Probability: Introduction, basic concepts of probability, conditional probability, Bayes' theorem, discrete and continuous random variables, central limit theorem. Inferential statistics: Estimation (point and interval), hypothesis testing for mean and proportions.

EST145: Statistics

2022-1, 2021-2, 2021-1, 2019-2, 2019-1, 2016-2

The course is designed to provide techniques in descriptive and inferential statistics and prepare students for their application in converting isolated numerical data into information useful for decision-making. It also provides an introduction to the discipline of probability, including notions of probability, random variables, and probabilistic models.

EST103: Statistics

2021-2, 2021-1, 2020-2, 2019-2, 2019-1, 2018-2, 2017-2, 2016-2, 2016-1

It introduces the student to elementary statistical techniques, training them in their use so that they can apply them to the process of converting isolated numerical data into information for decision-making. Common descriptive and inferential statistical methods are reviewed, which are relevant to various disciplines within the General Studies in Arts program, laying the foundation for more advanced courses that follow.

1MAT06: Differential Calculus

2020-1

It is a theoretical-practical course that contributes to the achievement of the general competence of learning to learn, as it develops the ability to apply mathematical knowledge to problem-solving. The course focuses on the study of differential calculus of real functions of a real variable and their applications to the sciences. In this course, the fundamental concepts and properties of limits, continuity, derivatives, indefinite integrals, and first-order ordinary differential equations are developed through the solution of various intramathematical and extramathematical exercises and problems.

IND251: Business Administration and Management

2016-2

Fundamentals of Administration. Evolution of the Administrative Approach. Systems Approach. Planning. Organization. Direction. Control.

1IND02: Work Study Laboratory

2017-2

It is a practical course that covers topics related to process description and analysis, method improvement, understanding human factors, and work measurement.

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