Systems Analyze • Data Analytics • Data Science • Artificial Intelligence • Operations Research • Mathematical Optimization

• B.Sc.: Computer Science
• Postgraduate: Data Science
• MBA: Data Science, Artificial Intelligence, and Analytics.

Graduated in Computer Science and completed a postgraduate specialization in Data Science. Has experience in systems analysis, data analysis, data science, artificial intelligence, operations research, and mathematical optimization. Applies computational and quantitative methods to investigate complex problems and develop solutions that support analysis and decision-making. Academic interests include data science, artificial intelligence, operations research, mathematical optimization, and theory of computation.

Recorded period: January 2019 to July 2025
Institution providing the materials: Massachusetts Institute of Technology (MIT) — MIT OpenCourseWare
Learning format: Independent study using open educational materials
Fields: Computer Science, Data Science, Statistics, Machine Learning, Applied Mathematics, Operations Research, Quantitative Finance, and Atmospheric Sciences

This document brings together 13 independent studies undertaken using materials published by MIT OpenCourseWare (OCW). The entries are arranged chronologically according to the dates recorded in the personal inventory/LinkedIn profile. These dates do not necessarily correspond to the academic terms in which MIT originally taught the courses.

The descriptions summarize the subjects covered by the official course pages. The Knowledge and Skills Developed sections describe learning outcomes that can result from studying the materials in full and completing the associated exercises. They do not imply an individual assessment, grade, or certification issued by MIT.

Nature of these studies: MIT OpenCourseWare is an open learning resource. OCW does not provide enrollment, academic credit, grades, certificates, or formal recognition of course completion. These entries document independent learning, not MIT-certified courses. See MIT's official explanation.

Recorded date: January 2019
Reference course: 6.088 — January IAP 2010
Field: Computer Science — Programming and Software Engineering

Description: An introduction to programming in C and C++, emphasizing explicit memory management, pointers, structures, dynamic allocation, and object-oriented principles. Topics include classes, constructors, destructors, inheritance, polymorphism, templates, and components of the C++ Standard Library.

Knowledge and Skills Developed: Thorough study and hands-on exercises can develop an understanding of the differences between memory management in C and object-oriented abstractions in C++; the ability to implement data structures and programs with greater control over computational resources; and awareness of common issues involving pointers, memory, and code organization.

Reference: MIT OpenCourseWare — 6.088

Recorded date: July 2019
Reference course: 12.811 — Spring 2011
Field: Atmospheric Sciences and Physical Modeling

Description: The dynamics and thermodynamics of the tropical atmosphere, including radiative-convective equilibrium, large-scale atmospheric circulation, intraseasonal variability, the El Niño–Southern Oscillation (ENSO), and tropical cyclones.

Knowledge and Skills Developed: Thorough study can support interpretation of the physical mechanisms behind tropical climate, connections across scales of atmospheric circulation, analysis of climate variability, and an understanding of conceptual and quantitative models used in meteorology.

Reference: MIT OpenCourseWare — 12.811

Recorded date: January 2020
Reference course: 6.251J — Fall 2009
Field: Operations Research and Mathematical Optimization

Description: Foundations of linear optimization and its extensions, including mathematical problem formulation, geometric interpretations, the simplex method, duality, sensitivity analysis, robust optimization, network flows, interior-point methods, and selected topics in discrete and semidefinite optimization.

Knowledge and Skills Developed: Studying the materials and solving the problems can develop the ability to formulate objective functions and constraints, analyze properties of optimal solutions, interpret duality and sensitivity, and identify appropriate algorithms for different classes of optimization problems.

Reference: MIT OpenCourseWare — 6.251J

Recorded date: July 2020
Reference course: 15.083J — Fall 2009
Field: Operations Research and Combinatorial Optimization

Description: Integer, mixed-integer, and combinatorial optimization, covering mathematical formulations, relaxations, computational complexity, duality, the geometry and algebra of integer optimization, cutting-plane methods, enumeration techniques, heuristics, and approximation algorithms.

Knowledge and Skills Developed: Comprehensive study can build the ability to represent discrete decisions using integer and binary variables, assess the strength of relaxations, understand major solution methods, and analyze allocation, selection, and planning problems involving combinatorial constraints.

Reference: MIT OpenCourseWare — 15.083J

Recorded date: January 2021
Reference materials: RES.LL-005 — January IAP 2020
Field: Data Science, Big Data, and Machine Learning

Description: Mathematical and computational approaches to large-scale data analysis, focusing on the Dynamic Distributed Dimensional Data Model (D4M), sparse matrix structures, associative arrays, distributed databases, and connections among linear algebra, graphs, and data processing.

Knowledge and Skills Developed: The materials provide a foundation for reasoning about mathematical representations of large-scale datasets, sparse data processing, links between computation and storage, and the application of algebraic operations to data analysis problems.

Reference: MIT OpenCourseWare — RES.LL-005

Recorded date: July 2021
Reference course: 12.864 — Spring 2005
Field: Statistical Inference, Modeling, and Inverse Methods

Description: Methods for extracting information from observations in conjunction with physical and mathematical models. Topics include inverse problems, least squares, singular value decomposition, covariance, recursive estimation, Kalman filtering, data assimilation, and spectral analysis techniques.

Knowledge and Skills Developed: Comprehensive study can develop the ability to connect observational data to models, identify estimation problems, interpret uncertainty and the limitations of solutions, and apply concepts from linear algebra and statistics to the analysis of dynamic systems.

Reference: MIT OpenCourseWare — 12.864

Recorded date: January 2022
Reference course: 18.S096 — Fall 2015
Field: Mathematics of Data Science

Description: Theoretical topics at the intersection of mathematics, statistics, algorithms, and data science, including high-dimensional principal component analysis, manifold learning, diffusion maps, spectral clustering, concentration inequalities, sparse recovery, and random graph models.

Knowledge and Skills Developed: Studying the materials can deepen understanding of the mathematical foundations of dimensionality reduction, clustering, and information recovery methods, while supporting the interpretation of assumptions, limitations, and theoretical guarantees underlying data analysis algorithms.

Reference: MIT OpenCourseWare — 18.S096

Recorded date: July 2022
Reference course: 15.062 — Spring 2003
Field: Data Mining and Predictive Analytics

Description: An introduction to data mining methods used to recognize patterns, identify relationships, and make predictions, with applications in business problems, fraud detection, customer management, credit, and investments. The materials bring together statistical and artificial intelligence perspectives.

Knowledge and Skills Developed: Completing the materials can support an understanding of knowledge discovery in databases, the selection of analytical techniques, and the strengths and limitations of data mining methods in real-world settings.

Reference: MIT OpenCourseWare — 15.062

Recorded date: January 2023
Reference courses: 6.0001 — Fall 2016; 6.100L — Fall 2022
Field: Computer Science and Python Programming

Description: An introduction to computational problem-solving, including Python programming, control structures, functions, decomposition, abstraction, algorithms, and program development. The two references represent related versions of introductory material with different organization and pacing.

Knowledge and Skills Developed: Completing the activities can develop the ability to design algorithmic solutions, write and test Python programs, decompose problems into functions, and strengthen computational thinking. The two versions can be used as complementary references without implying two certificates or two formally completed MIT courses.

References:

1. MIT OpenCourseWare — 6.0001: Introduction to Computer Science and Programming in Python
2. MIT OpenCourseWare — 6.100L: Introduction to CS and Programming using Python

Recorded date: July 2023
Reference course: 18.435J — Fall 2003
Field: Quantum Computing and Theory of Computation

Description: Theoretical and mathematical foundations of quantum computing, including quantum states and operations, Simon's, Shor's, and Grover's algorithms, quantum computational models, error correction, cryptography, and fault tolerance.

Knowledge and Skills Developed: Comprehensive study can help compare classical and quantum computational models, explain the significance of representative quantum algorithms, and understand challenges associated with error correction and the implementation of quantum systems.

Reference: MIT OpenCourseWare — 18.435J

Recorded date: January 2024
Reference course: 15.097 — Spring 2012
Field: Machine Learning, Statistics, and Predictive Modeling

Description: An integrated study of machine learning and statistical methods for prediction. Topics include classification, regression, clustering, validation, generalization, regularization, support vector machines (SVM), boosting, statistical learning theory, and Bayesian methods.

Knowledge and Skills Developed: Thorough engagement with the materials can support the comparison of predictive techniques, recognition of overfitting and underfitting, evaluation of generalization performance, and understanding of the probabilistic and statistical foundations of machine learning algorithms.

Reference: MIT OpenCourseWare — 15.097

Recorded date: January 2025
Reference courses: 12.010 — Fall 2011; 12.010 — Fall 2024
Field: Scientific Programming, Numerical Methods, and Applied Computing

Description: Programming methods and languages applied to scientific research. The 2011 version introduces FORTRAN, C, C++, MATLAB, and Mathematica, emphasizing algorithm design, development, and verification. The 2024 version updates the material with Python, Julia, C/C++, Jupyter, techniques for learning from data and models, and cloud and high-performance computing workflows.

Knowledge and Skills Developed: Thorough study and practical exercises can develop the ability to implement, compare, and verify scientific programs; make informed choices among programming languages and tools; and apply computational methods to the analysis of physical phenomena, data, and models.

References:

1. MIT OpenCourseWare — 12.010 (Fall 2011)
2. MIT OpenCourseWare — 12.010 (Fall 2024)

Recorded date: July 2025
Reference courses: 18.S096 — Fall 2013; 18.642 — Fall 2024
Field: Financial Mathematics and Quantitative Finance

Description: Mathematical applications in finance, bringing together linear algebra, probability, statistics, stochastic processes, regression, numerical methods, and topics in investment and pricing. The two references provide related versions of the course, including an updated 2024 edition.

Knowledge and Skills Developed: Comprehensive study can build the ability to interpret quantitative models used in finance, understand connections between mathematical assumptions and investment decisions, and develop a foundation for analyzing risk, returns, and financial modeling methods.

References:

1. MIT OpenCourseWare — 18.S096: Topics in Mathematics with Applications in Finance (Fall 2013)
2. MIT OpenCourseWare — 18.642: Topics in Mathematics with Applications in Finance (Fall 2024)

• Source of content: Official MIT OpenCourseWare pages used to describe the subjects covered by each study.
• Source of dates: Personal inventory/LinkedIn; they are not certification dates or the original MIT academic terms.
• Learning outcomes: The skills described represent potential educational outcomes from thorough study and completion of exercises, without claiming an individual assessment by MIT.
• Verification: MIT OCW does not issue certificates or academic credit. This document serves as an independent learning record and a curated reference for studies and projects.
• Links: All 16 official URLs in this document were supplied directly by the author of the inventory. Entries 9, 12, and 13 each include two references to different versions of the materials.

Institutional source: MIT OpenCourseWare · Official policy on certificates and credit