CV

Formal, Natural/Pure and Applied sciences

• My projects portfolio

• Developing a Web Dashboard for analyzing Amazon's Laptop sales data

• Try the App at http://dashboard.sinfronteras.ws

• Github repository: https://github.com/adeloaleman/AmazonLaptopsDashboard

• eComerce Web Application for an optical glasses retailer

• Visit the Web App at http://www.vglens.sinfronteras.ws

• This Application was developed using Python-Django Web framework

• Web App - Clone of Twitter

• Visit the Web App at http://62.171.143.243

• Github repository: https://github.com/adeloaleman/WebApp-CloneOfTwitter

• This Application was developed using:

• Back-end: Node.js (Express) (TypeScript)

• Front-end: React (TypeScript)

• Supervised Machine Learning for Fake News Detection

• Visit the Web App at http://fakenewsdetector.sinfronteras.ws

• Github repository https://github.com/adeloaleman/RFakeNewsDetector

• Java Desktop App - Zoo Management System

• In this project, we have created a GUI Java (Swing) Application for a Zoo Management System.

• You can try the application by downloading the Java Jar file from this link: http://wiki.sinfronteras.ws/images/2/27/ZooManagementSystem.jar

• Github repository: https://github.com/adeloaleman/JavaDesktopApp-ZooManagementSystem

• Java Desktop App - Stock Market Simulator

• In this project, we have created a GUI Java (Swing) Application that simulates a trading day of a simplified model of a stock market.

• You can try the application by downloading the Java Jar file from this link: http://wiki.sinfronteras.ws/images/0/00/SMSimulator.jar

• Github repository: https://github.com/adeloaleman/JavaDesktopApp-StockMarketSimulator

• Seismic Wave Propagation Modelling

• Computer Science and IT

• Programming and Software Development

• Some of the languages I have worked with:

• Python

• Python for Data Science

• Django

• FastAPI

• Dash - Plotly

• Manim

• JavaScript

• React

• Node.js - Express.js

• HTML & CSS

• Java

• Java GUI Programming

• SQL

• Shell Scripting

• R

• PHP

• C

• Multi-Paradigm Programming and Scripting

• Object-Oriented Concepts and Constructs

• SOLID Principles

• Class vs Object vs Instance

• Methods

• Variables

• Modifiers

• Four fundamental OOP concepts

• Encapsulation

• Inheritance

• Polymorphism

• Abstraction

• Nested classes

• Upcasting - Downcasting

• Errors and Exceptions

• Passing Object References

• Connecting to a MySQL database using Java

• Threads

• Serialization

• Object Orientation with Design Patterns

• Creational Patterns

• Singleton Pattern

• Factory Pattern

• Abstract Factory Pattern

• Builder Pattern

• Prototype Pattern

• Structural Patterns

• Adapter Pattern

• Facade Pattern

• Proxy pattern

• Decorator Pattern

• Behavioural Patterns

• Chain of Responsibility Pattern

• Command Pattern

• Iterator Pattern

• Observer Pattern

• Mediator Pattern

• Data Access Patterns

• Data Access Object Pattern

• Model-View-Controller MVC

• Data Structures & Algorithms

• Analysis of algorithms

• The Stack Data Structure

• The Queue Data Structure

• The Array Data Structure

• The List Data Structure

• The Linked List Data Structure

• The Tree Data Structure

• Recursion vs Iteration

• Bubble and Insertion Sort

• Web Development

• HTML

• CSS

• Django

• FastAPI

• JavaScript

• React

• React_2

• Node.js - Express.js

• Dash - Plotly

• PHP

• Ruby on Rails

• Web server

• Web mapping

• HTML presentation with Reveal.js

• Excelentes diseños de WebSites

• Mobile Development

• Native vs Hybrid Frameworks

• Cordova - PhoneGap

• Ionic and Capacitor

• Systems Analysis & Design

• Universal design

• GitHub

• Data Science

• OpenAI - ChatGPT

• Descriptive Data Analysis

• Correlation & Simple and Multiple Regression

• Naive Bayes

• Decision Trees

• K-Nearest Neighbour

• Boosting

• Perceptrons - Neural Networks and Support Vector Machines

• Association Rules - Market Basket Analysis

• Clustering

• Time Series Analysis

• Model Evaluation

• Text Analytics

• Information Retrieval

• Natural Language Processing

• Python for Data Science

• NumPy and Pandas

• Data Visualization with Python

• Pandas Built-in Data Visualization

• Matplotlib

• Seaborn

• Plotly and Cufflinks

• Word cloud

• Text Analytics in Python

• Dash - Plotly

• RapidMiner

• Power BI

• Jupyter

• Big Data Integration

• ML Songwriting

• Other qualifications in IT

• Databases

• Database design

• Conceptual design

• Entity relationship modelling

• The Enhanced Entity-Relation Model

• Logical design

• Relational Model

• Normalisation

• RDBMS

• MySQL

• Backup and Restore MySQL/MariaDB Databases

• Import a CSV file into a MySQL database

• Reset the MySQL/MariaDB Password

• Authentication and Authorization

• SQL Practice Labs

• Stored Procedures

• Stored Functions

• Triggers

• Events

• Cursors

• Transactions

• Exception Handling

• PostgreSQL

• GUI DB administration tools

• MySQL Workbench

• SQLectron

• Cloud Computing

• AWS

• Google Cloud

• Operating Systems

• Linux

• Shell Scripting

• Networking

• Network Simulation using PacketTracer

• Docker

• Integration and automation of business apps

• Other IT pages

• Google

• Mi Computadora - Phone

• Physics and Mathematics

• Physics

• https://www.youtube.com/watch?v=M22MEShcyx8

• https://www.youtube.com/watch?v=tfSMcDqnh00&t=2061s

• https://www.youtube.com/watch?v=NrjFE_Rd2OQ&t=81s

• https://www.youtube.com/watch?v=e5_V78SWGF0

• Mathematics

• Geophysics

• Modelado sísmico

• Trading

Social sciences and Humanities

• Filosofía

• Historia

• La Révolution Cubana

• La Revolución Rusa

• Independencia de Venezuela

• La Révolution Française

• Cristianismo

• Judaísmo

• The big bang theory

• Origin and evolution of Homo sapiens

• Literatura

• Geopolitics

• Lideres

• Marx

• Che

• Fidel

• Chávez

• Ali

• Silvio

• Facundo

• Rubén

• Mohamed Ali

• Computational Modeling of Social and Political Systems

• Archivo - Discursos - Debates - Frases

Música

• Composición

• Guitarra - Bajo

• Salsa

• Video management

• Canciones - Films - Books

Base

• Mis páginas

• Página base

• Sidebar

• Desktop

• Timeline

• CV

Important resources

• https://github.com/adeloaleman

• https://app.grammarly.com

• https://www.office.com

• My Wikipedia Sandbox https://en.wikipedia.org/wiki/User:Adeloaleman/sandbox

• Jupyter Notebook https://colab.research.google.com

• Online IDE https://repl.it

• Online Web IDE https://jsfiddle.net

• Download video from youtube https://en1.savefrom.net/2ol/

• Mapas

Carrera

• CV

• CV - Skills and Qualifications

• CV - Skills and Qualifications_1

• CV - Work experience

• CV - Education

• CV - Courses

• CV - Publications

• CV - Languages

• CV - Interests

• Job search

• Entrevista

• Mis proyectos

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MediaWiki help

• Special:SpecialPages

• Special:Upload

• CSS & JS - MediaWiki:Common.*

• MediaWiki:Common.css

• MediaWiki:Common.js

• MW Manual:CSS

• Sandbox

• Página de pruebas

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• Some HTML/Wiki tags

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• Numbered figures

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Working on - Mis_páginas	Another	Another
Data science All the Python for data science .ipynb Descriptive Data Analysis Second example of Kurtosis Data Science Programming StockMarketSimulator-Java1 code StockMarketSimulator-Python code Web development example1 code ---- ZooManagementSystem Class diagram ZooManagementSystem-Java1 code ZooManagementSystem-Python1 code Object-Oriented_Concepts_and_Constructs JavaScript & React Database BookDB implementation example (.ipynb) ---- LaboratoryDB design and implementation example Databases	Level2 Level3 Level4 Level5 Level5	Level2 Level3 Level4 Level5 Level5

Adelo Vieira,  Developer / Data Scientist 47-A Phibsborough Rd, Dublin   +353 852 40 72 08   adeloaleman@gmail.com  Github            Linkedin          Portfolio          My Wiki         Download a pdf version of my CV

BSc. (Hons) in Information Technology, Geophysical Engineer, and MSc in Petroleum Geosciencewith strong mathematical, problem-solving, and analytical skills. I'm currently particularly interested in Data Analytics and Software Development. Proficient in multiple programming languages, including Python, Java, JavaScript, SQL, and R. I have a huge interest in Machine Learning and Natural Language Processing. I've been recently working in areas such as Text classification and Sentiment Analysis. I have solid knowledge in several ML algorithms (Naive Bayes, Decision Trees, K-Nearest Neighbour) and in Time Series Analysis. I have experience working with Python (Pandas, NLPTK, Scikit-learn, SciPy, Plotly, TextBlob, Vader Sentiment), R, and RapidMiner. Solid experience in Object-oriented programming and Web Development. I have developed several projects using Java, Python, React, Node.js (Express.js), and Dash. I also have advanced experience with Linux (including Shell Scripting) and I have worked with Relational databases (SQL, MySQL, PostgreSQL) and Cloud computing (AWS and Google Cloud).

Work experience

Present ↑ 2022	IDG Direct, Ireland Real Time Data Analyst I'm responsible for analyzing and monitoring call center data. This includes call volumes, performance indicators, queue time, agents availability, inactivity levels, average handle times, etc. Python programming for data analysis and data visualization: Pandas, Scikit-learn, Plotly, Dash. Data preparation, Data visualization, Dashboards/reports creation with SiSense. Finding patterns and trends in the data to help increase productivity and forecast requirements. Produce daily, weekly, and monthly internal reports to assist with the creation of metrics and targets for services. Data Management in Excel. Generate ideas for process and service improvement. Work closely with the operations team to analyze and help improve their delivery processes.
Present ↑ 2020	Python Developer - Freelance Development of crypto trading applications/bots using Python: Historical and real-time analysis of cryptocurrency data to forecast price movements. The data analyzed include prices, volumes, cryptocurrency news (tweets / Announcements of new cryptocurrency listing in Exchanges), etc. Real-time response optimization of crypto news (tweets/web content) through a multi-location server architecture. Centralized management of multiple social media accounts to monitor crypto news/market sentiment. REST API requests (Requests, asyncio/aiohttp). WebSockets client and server. Web Scraping, BeautifulSoup, Selenium, Mysql.connector. Concurrency: Multithreading (threading, ThreadPoolExecutor), Multiprocessing, Event loop (asyncio). AWS (EC2, VPC, Amazon S3, Amazon Glacier) / GoogleCloud Data analysis and visualization: Pandas, Scikit-learn, Plotly, Dash. Development of a eComerce Web Application for an optical glasses retailer using Python-Django: Visit the Web App at http://www.vglens.sinfronteras.ws
2022 ↑ 2017	IDG Direct, Ireland Senior Business Development Executive I am responsible for creating new sales prospects and carrying out b2b outgoing calls to prospective clients. Additionally, I prioritize staying well-informed about each customer's current and future business practices and processes, allowing me to build and maintain strong relationships with them. Building and maintaining a professional relationship with IT Managers is a key part of my role. This enables me to identify their needs and next investments. The gathered information is required by our clients (Largest Tech Companies) and used in the next step of the sales process. I work in different markets and contact clients in French, English, and Spanish: France, Belgium, Luxembourg, Spain, Middle East, and Africa. Gathering client details and Maintaining/Updating IDG database with accurate client details. Train new team members and provide ongoing training: Motivating, developing, and training staff at a BANT level. Script delivery and how to effectively communicate. How to construct specific campaign questions that encourage the prospect to expand on information. Monitoring team members on a daily basis, providing support and encouragement where necessary to ensure all service levels and KPIs are reached. Ensure Key Performance Indicators (KPIs) are met and constantly improved. Collaborating with the Team Manager in the recruitment process of new agents. I have experience leading campaigns. Liaise and work closely with the Client Manager, Team Leaders, and all key stakeholders involved in the team set-up. Identifying any business need or potential investment a prospect may have that can be potentially capitalized on by our clients. Communication and Sale Skills My current job at IDG is about communication. First, because I'm working in a team, and we always have to reach targets as a team, and communication within the team is always the key to reach the targets. Secondly, because one of my main responsibilities is to call contacts (to call IT Managers) on behalf of our clients, and of course this is about effective and clear communication. I have to explain to the contact the reason for the call, the topic of the campaign, and most importantly, I have to communicate in a way that... well I have to create an atmosphere in the call where the contact is going to feel comfortable and is going to accept answering my questions. In this position, I have improved my communication skills in French and English. I have learned how to build and maintain a professional relationship and improved my Active Listening Skills. I also think that I have developed communication skills not only at work but also in other aspects of my life; you know I have always done team sports in a high competitive-level: Volleyball when I was a chield; I was member of the Volleyball team of my state and attended 1 national games; and Waterpolo at university, where I attended 5 National University Games; and those are activities where you develop, sometimes without being aware, you develop many communication skills. I have to call IT Managers and establish and maintain a professional conversation with them in order to identify their next investments. So from this conversation we gather information about their next investment and this information is required from our clients (IT Companies: IBM, DELL, NetApp, etc) and they use this information next step of the sales process. Let's say that IBM is looking to sell a particular product (A Cloud backup solution, for example). So, IBM requires IDG's services, asking for a number of contacts (IT Managers) that are planning to invest in backup solutions. Then, we establish a professional conversation with IT Managers from our database and identify those that are looking to invest in the product required for the client. During the phone conversations, I have to explain the topic of the product that our clients are looking to sell and be able to handle objections. That is why this experience has made me aware of the latest solutions and technologies in which the most important IT companies are working on. At IDG, I have also completed a Certified Sales training. During this course, I have learned and put into practice, the most important concepts of the sales process. Prospecting, Preparation, Approach, Presentation, Handling objections, Closing, Follow-up https://www.lucidchart.com/blog/what-is-the-7-step-sales-process Target and KPI I'm used to work in a Target Working Environment because I'm currently working in a TWE at IDG. At IDG we have to reach a daily target of about €650 per day. To reach this target performance we need to generate what we call a «lead». A lead is a conversation that matches the criteria asked for the client. For example, if the client (Let's see IBM) is asking for contacts that are looking to invest in Backup solutions, then every time that we have a conversation in which the contact confirms to be looking for backup solutions; this contact represents a «lead». So each lead that we generated has a price, and we need to generate as many leads as needed to reach the target of €650. So normally an easy lead worth about €65 and a complicated one about €180. So, every day we need to fight to reach the target performance. We usually have many challenges to reach the target performance: Data challenges: We make calls using particular data that has been prepared for a particular campaign. Many times you can make many calls but you don't reach the contacts that you are looking for. So you can spend your day making calls but not having conversations with the IT Manager. So if you are not reaching the contact, you can not make leads. Hard campaign challenges: That means that we have a campaign in which the client is asking for a difficult criterion. Let's say, for example, that the client is asking for contacts that are looking to invest in a particular solution (SAP applications for example). That represents a campaign challenge because we have to reach a contact that is looking to invest, specifically, in this solution. Solutions: There are a few techniques that we use to apply when we face the challenges. Change the data or the campaign you're working on is the first action we can take. But sometimes you can not change the campaign because we really need to deliver lead for those campaigns because we need to reach a certain number of leads the client is asking for. We usually make calls using a platform that makes the calls automatically taking the contact from the database related to the campaign you're working on. So usually we don't need to worry about the criteria (company size, job title, industry) of the contacts we are calling because the platform makes the calls. But when you have data problems, the solution is to research for contacts manually. So, that is a little tricky because you can try to call the best contact by doing manual research in the database, but you can spend a long time doing this research and that doesn't assure that you are going to reach the contact and get leads. So when you have good data you have to use the platform, otherwise, you should search for contacts manually. So in this manual research is where you have to propose ideas and develop a good methodology to be able to find good contacts and get leads. One of the techniques we apply when we have a hard campaign is, for example, if we get a lead from a particular company; we try to call other contacts from the same company because we know that this particular company is going to review in the product that the client is looking for. The other approach is to try to search new contacts on the internet (usually on Linkedin), but that is even more tricky because it is complicated to get reach a new contact and to get the lead. Here is where I wanted to say that I had an important contribution. So the problem with this external research is that most of the contact that you are going to find on Linkedin is already in our database. So it doesn't make sense. But I realized that when we are looking for business job titles (because sometimes we have campaigns in which the client is asking for business titles) it makes sense to do external research (on Linkedin) because our database is composed mostly for IT Professionals (we have some business contacts in our database, but not a lot) so the chance of finding a contact on Linkedin that is not in our database increase a lot. Therefore, it makes sense to do external research when looking for business contacts. By doing that, I was able to get a good number of leads for hard campaigns; and that is a concrete contribution that I made to my team.
2014	WikiVox, France Web Programmer I was responsible for the installation and administration of a Wiki Web Application based on the MediaWiki engine. Extensive experience with the MediaWiki Engine: Configuration of a Multilingual Wiki. User access levels configuration. Implementation of different CAPTCHA methods. Implementation of a payment gateway. Page categorization. Take a look at my personal Wiki: http://wiki.sinfronteras.ws Administration of a Linux Server: Installation and configuration of a LAMP stack: Apache, MySQL, PHP. Database management: MySQL, PhpMyAdmin. WikiVox is a nonprofit organization whose goal is to create a website (a wiki) for debates of political, economic and environmental topics. They want to create a discussion method capable to generate, at some point in the debate, an article with precise suggestions, in order to contribute to the solution to the problem. When I was working at WikiVox, the project was just starting. The philosophy of the project was already mature, but the implementation of the Wiki was just in its first phase. It was a very nice experience. I liked very much especially the philosophy of the project. And... I think that working in a small organization was positive at this point in my career Because I had responsibilities that I am sure I would not have had in a big company; that's why I think that I learned a lot from them. I had responsibilities related to (1) the administration of a Linux Web Server and (2) to the design of the website. About Linux administration, my responsabilities were regarding the installation and administration of a LAMP stack (Apache, MySQL, PHP) on a Linux Server. About the design of the website, we used free software (Wikipedia Software). I was responsible for the installation and administration of a Wiki Web Application based on the MediaWiki engine. Some of the functionalities that we We had to install a LanguageSelector and translate the content into 5 languages: French, English, Spanish, German and Arabic. We had to install an extension to make donations (I mean to pay online). The payment gateway for implementing a donation service. An extension to categorize pages. I also had to program in PHP. Wiki - Organize information into a cohesive, searchable and maintainable system. One of the most important skills I have, which I usually find complicated to make understand its importance, is my Wiki management skills. A Wiki is a website on which users can collaborate by creating and modifying content from the web browser. So, the best example is Wikipedia. In Wikipedia someone can create a article and then it can be modify online for other users. A Wiki is an outstanding tool to organize information into a cohesive, searchable and maintainable system that can be accessed and modified online. The benefits of a wiki to organize information are remarkable. I have a personal Wiki (based on the MediaWiki engine) where I document everything I'm learning and working on. So, I use a Wiki as a Personal knowledge management that allows me to organize information into a cohesive, searchable and maintainable system. The benefits that I've had using a Wiki are amazing. It has allowed me to learn in a more effective way; and most importantly, to constantly review and improve in important topics by providing a very convenient online access (so from anywhere) to an organized and structured information. Take a look at some of my Wiki pages: http://perso.sinfronteras.ws/index.php/Computer_Science_and_IT
2012 ↑ 2011	Simón Bolívar University - Funindes USB, Venezuela Research geophysicist Click here to see some examples of my work in Seismic modelling. As a Research Geophysicist, I was responsible for performing a set of Signal analysis/Data processing tasks, and ensuring the correct integration and implementation of geophysical applications into a computer cluster platform. My responsibilities included: Machine Learning algorithms (Regression, classification) for Seismic/Borehole data Analysis. Python / MATLAB / Shell script programming for Seismic data analysis/Signal analysis (Seismic data processing and modeling). Simulations of seismic waves propagation: Wavefront and ray tracing. Generation of pre-stacked synthetic seismic data using wave propagation theories (raytracing and finite difference methods). 2D/3D Seismic data processing: Deconvolution Auto-correlation, Cross-correlation Analysis of signal noise reduction: time/frequency domain transforms Task automation using Shell scripting. Task automation using Shell scripting: Here I could mention the generation of images to create seismic waves propagation videos or the automatic generation of pdf reports using latex that contained details about the executed process: time vs. the features of the data generated (the amount of data generated). I have skills in Matlab, Scilab, Python and Shell scripting that I got during my participation in an R&D Unit at Simón Bolívar University (The Parallel and Distributed Systems Group - GryDs). MATLAB (matrix laboratory) is a language and numerical computing environment. MATLAB allows data analysis and data visualization, matrix manipulations, and performing numerical computations. Matlab contains a huge library of functions that facilitate the resolution of many mathematical and engineering problems. For example, I used it for Signal Analysis, specifically for Seismic data analysis. it for Ex. 1 and Ex. 2: Signal Processing in Geophysics Ex.1: That allows defining the coordinates of the layers of a geological model by opening an image file of the geological model and selecting, by clicking with the mouse, a set of points (or coordinates) that define each of the layers of the geological model. These coordinates will be saved in a very particular format that will be used as input of another program that is in charge of building the Geological model entity used by another program to perform a Seismic Wave Propagation Modelling.
2011 ↑ 2010	CGGVeritas, Venezuela Seismic data processing analyst I was responsible for performing Seismic Data processing/Analysis and Borehole Data Analysis for oil and gas exploration. Seismic/Borehole data Analysis: Machine Learning algorithms (Regression, classification) for estimating reservoir properties / reservoirs classification. 2D/3D Seismic data processing: Geometrical spreading correction. Set-up of field geometry. Application of field statics corrections, Deconvolution, trace balancing. CMP sorting, Velocity analysis, Residual statics corrections. NMO Correction, Muting, Stacking. Filtering: Time-variant, band-pass. Post-stack/Pre-stack time and depth migration. Numerical modeling of seismic wave propagation.
2010 ↑ 2008	Simón Bolívar University, Venezuela Academic Assistant - Geophysics Department As an Academic Assistant, I was in charge of collaborating with the lecture by teaching some modules of the Geophysical Engineering program at Simón Bolívar University. I was usually in charge of a group of between 20 and 30 students during theoretical and practical activities. Courses taught: Seismic data processing: Concepts of discrete signal analysis (time series analysis), sampling, aliasing, and discrete Fourier transform. Conventional seismic data processing sequence. Seismic methods: The convolutional model of the seismic trace. Propagation and attenuation of seismic waves. Interpretation of seismic sections. Seismic reservoir characterization: Relations between the acoustic impedance and the petrophysical parameters. Well-Seismic Ties. Seismic data analysis (Inversion and AVO). This experience has contributed to my professional development in two major areas: By teaching modules, I have enhanced my technical geophysical knowledge. I have also developed communication and presentation skills, as well as the leadership strategies needed to manage a group of students and to transfer knowledge effectively. I have three years of experience as an academic assistant in the courses of Seismic Processing, Seismic Reservoir Characterization, and Seismic Methods. During my experience as an academic assistant, I have solidified my knowledge of the theoretical basis of seismic processing. In particular, all the technical concepts that are required for this position, such as Seismic velocity analysis, Multiples, Surface statistics correction, Noise attenuation, and Imaging. During my experience as a teacher assistant, I was assigned three times to teach the Seismic data processing course. My work was to give theoretical and practical lessons. The theoretical part was focused on signal theory: Concepts of discrete signal analysis, sampling, aliasing, and discrete Fourier transform, and all the theoretical aspects of each stage of a conventional seismic processing sequence. And in the practical part, the students had to process a 2D seismic data set. We used the Seismic Unix software. It's a free software developed for the Colorado School of Mines. I was the assistant of the teacher in charge. But I was responsible for a large part of the course since I have participated three times in this course.

Education

2020	College of Computing Technology (CCT), Ireland Diploma in Predictive Data Analytics Grade of Distinction Project: Evaluating the Performance of Lexicon-based and Machine Learning Sentiment Analysis for Amazon reviews classification.
2020	College of Computing Technology (CCT), Ireland Bachelor of Science (BSc) (Honours) in Information Technology Final project: Developing a Web Dashboard for analyzing Amazon's Laptop sales data. To know more about this project, visit Developing a Web Dashboard for analyzing Amazon's Laptop sales data In my final Bachelor (Honours) in IT I worked in Sentiment Analysis using Python. I specifically developed a Web Dashboard for analyzing Amazon's Laptop sales data, mainly to perform a Sentiment Analysis on Amazon customer reviews. I have performed a Sentiment Analysis of Amazon customer reviews by using both, Lexicon-based and Machine Learning methods. Lexicon-based Sentiment Analysis: One of the purposes of this study is to evaluate different Sentiment Analysis approaches. That is why I performed a Lexicon-based Sentiment Analysis using two popular Python libraries: Textblob and Vader Sentiment. Machine Learning Sentiment Analysis: I have built a ML classifier for Sentiment Analysis using the Naive Bayes algorithm and an Amazon review dataset from Wang et al. (2010).It is important to notice that this is an extra result with respect to the initial objectives. I haven’t planned to carry out this studio. However, I realized that it was very beneficial to include another Sentiment Analysis approach. This has allowed me to evaluate and compare both approaches in terms of their performance. In addition, a Word Emotion Association Analysis has been also performed. This analysis complements the polarity analysis by adding more details about the kind of emotions or sentiments (joy, anger, disgust, etc.) in customer reviews. This analysis was performed by using the NRC Word-Emotion Association Lexicon.
2014	Claude Bernard Lyon 1 University, France Master – Complementary computer studies Specialty: Distributed information systems and networks. Final project: Design and Administration of a Wiki Web Application. I have always had a strong interest in Computer Sciences. In fact, when I start my studies in the university, I had to choose between Computer Sciences and Geophysics. During my studies in geophysics, I took courses in Fortran and C programming, and in Linux System Administration... and I had to program many times in Matlab (for example)... and thus I acquired computer skills. The master's programme included courses of: Algorithms and Programming. I took courses in C programming, java and SQL We also received a training in database management Linux systems administration Networks administration. Those are the areas I really appreciated, and in these areas is that I have my best computer skills, but there were other courses of course, like: VRML: Virtual Reality Modeling Language is a standard file format for representing 3-dimensional (3D) interactive vector graphics, XML: Extensible Markup Language is a markup language that defines a set of rules for encoding documents in a format that is both human-readable and machine-readable. PHP programming Yes, they were basic courses because it was a one year master... and it was a master oriented to not computer professionals. But even if we were not computer professionals we had all computer skill. Because it was a requirement to enroll in that master. So the the courses had a good level I think so. No existe traducción en francés Algoritmo y programación (C, Java, VRML) Base de Datos: SQL Administración de sistemas operativos: Linux Redes VRML XML
2011	Simón Bolívar University, Venezuela MSc in Earth sciences Specialty: Applied Petroleum Geoscience. Master thesis project (Excellence Honor Mention): Study of Pull up/Push down effects through seismic modelling, Colombian plains. I got a master's degree in Petroleum geosciences. I dit this master because I wanted to focuses in oil exploration... During this master, I took modules related to the oil exploration process. I deepen my knowledge in areas such as: Seismic processing and modeling Petrophysics Seismic réservoir characterization And I received a training in: Petroleum System Analysis Petroleum geology These studies gave me the knowledge to understand how the oil industry works These studies gave me an overview of how the oil industry works. No existe traducción en francés Métodos avanzados de interpretación sísmica: Petrofísica: Tectónica de Placas: Descripción sísmica de yacimientos: Análisis de sistemas petroleros: Geointegración petrolera: My master thesis at CGGVeritas Creo que hoy en día la geofísica de exploración y producción debe enfocarse en pequeños detalles. Esto porque la exploración de hidrocarburos ya no requiere de un campo gigante para ser economicamente rentable, actualmente pequeñas acumulaciones pueden dar lugar a importantes beneficios para las compañías, pero para ello es indispensable ser detallista en los diversos pasos de exploración y producción. Por ejemplo, el campo de estudio de mi trabajo de maestría estaba compuesto por pequeñas acumulaciones; y en mi trabajo se trata un problema relacionado a la deformación de los reflectores a causa de variaciones laterales de velocidad, el trabajo concluye que estos problemas, podrían ser vitales para una apropiada cuantificación de las reservas. Les vitesses de propagation des ondes sismiques No existe traducción en Inglés Les vitesses de propagation des ondes sismiques.
2007	Simón Bolívar University, Venezuela Geophysical Engineer Bachelor thesis project: Basic modelling of pre-stacked seismic data and its corresponding processing sequence, using Seismic Unix package. Geophysics is a discipline that uses different areas of knowledge, such as physics, mathematics, and geology to study the internal constitution and history of the Earth. The Geophysical engineering career at Simón Bolívar University is based on: A training in seismic exploration methods (acquisition, processing, inversion, and interpretation) and its direct application to the oil industry. The program also includes a basic training in gravimetric, magnetic and electrical exploration methods Some courses in geology. But the career is oriented towards oil exploration because Venezuela is an oil-producing country. La Ingeniería Geofísica aplica distintas áreas del conocimiento tales como la Física, las Matemáticas y la Geología, al estudio de la constitución interna e historia de la Tierra. La formación del Ingeniero Geofísico en la Universidad Simón Bolívar se cimenta en: Una preparación en métodos de exploración sísmica (adquisición, procesamiento, inversión e interpretación) y sus aplicaciones directas a la industria petrolera. Una preparación básica en métodos de exploración gravimétrica, magnética, y eléctrica Una cadena de cursos en Geología que sirven al futuro ingeniero en sus labores de interprete y geointegrador. Ejemplos de aplicaciones de la Ingeniería Geofísica: En la exploración petrolera, los métodos de prospección sísmica permiten detectar las trampas que sirven de receptáculos a los hidrocarburos a profundidades que por otras vías sería imposible determinar. En la exploración minera los métodos gravimétricos y magnéticos permiten la evaluación del suelo y del subsuelo con el fin de ubicar y/o descubrir, ampliar y redefinir yacimientos minerales que no son visibles en superficie, además de que por estas mismas vías es posible establecer zonas de recursos minerales económicamente explotables. En la Ingeniería Civil la Geofísica permite estudiar las condiciones del subsuelo para el emplazamiento de obras de envergadura (presas, viaductos, edificios, carreteras etc.) y en la Agronomía permite definir posibles acuíferos utilizables ya sea para riego o para consumo humano. No existe traducción en francés Simón Bolívar University and background in Mathematics/Physics I'm an engineer from the most important scientific Venezuelan university, which is Simón Bolívar University; and really, I need to highlight the academic level and the quality of Simón Bolivar University. If you check now, Simón Bolívar University is still in a good place in the LatAm University Rankings; but the university has been widely affected by the difficult political situation in the country. I don't know if you have heard about the critical political and economical situation in Venezuela. But the fact is that in my time when I started my career, Simón Bolívar university was always in the top 10 of the best LatAm Universities with scientific and technological orientation. I have a very good background in formal and pure sciences, like mathematic and physic. I followed 7 pure maths and 5 pure physics courses; without counting all the applied geophysical courses that I followed with a high content of mathematics, physics, or chemistry. If you review the course content of an IT program you will find at most 2 mathematic courses. I really think that for an IT professional it is very important to have a good background in mathematic. For example, to be able to understand some computational concepts (functional programming for example) you need to have a good mathematical background.

Skills and Qualifications

Lista de skills

Strong mathematical, Problem-solving, and Analytical skills,

Data Analytics,

Object-oriented programming,

Python, Java, JavaScript, SQL, and R, Shell Scripting,

Web Development: HTML, CSS, React, Node.js - Express.js, Python-Django, Dash,

ML algorithms: Naive Bayes, Decision Trees, K-Nearest Neighbour, Clustering,

Python for Data analysis and Machile Learning: Numpy, Pandas, Scikit-learn, SciPy,

Text Analytics / Natural Language Processing (NLP) / Sentiment Analysis using Python and R: NLPTK, TextBlob,

Data Visualization with Python (Plotly, Dash, Matplotlib, Seaborn) and Power BI, SiSense

RapidMiner,

Relational databases: SQL, MySQL, PostgreSQL,

Linux,

Cloud computing: AWS and Google Cloud,

GitHub,

Jupyter

Programming and Software Development	Data Science	Other qualifications
I started programming around 15 years ago, when I was studying geophysics, so in this part of my career, as a geophysics, I started coding mathematical, engineering problems and Data analysis and Data processing topics (Signal analysis in particular: A signal is a function that conveys information about a phenomenon. For example, Sound, images and videos are considered to be signals) . One of my main projects in this area was developing programs to perform Seismic Wave Propagation Simulations (Seismic Modelling). During this experience, I got skills in Python, Matlab (which is a data analysis environement/or a numerical computing environement), Scilab and Shell scripting. Research geophysicist at GRyDs As a Research Geophysicist, I was responsible for performing a set of Signal analysis/Data processing tasks, and ensuring the correct integration and implementation of geophysical applications into a computer cluster platform. Task automation using Shell scripting: Here I could mention the generation of images to create seismic waves propagation videos or the automatic generation of pdf reports using latex that contained details about the executed process: time vs. the features of the data generated (the amount of data generated). I have skills in Matlab, Scilab, Python and Shell scripting that I got during my participation in an R&D Unit at Simón Bolívar University (The Parallel and Distributed Systems Group - GryDs). MATLAB (matrix laboratory) is a language and numerical computing environment. MATLAB allows data analysis and data visualization, matrix manipulations, and performing numerical computations. Matlab contains a huge library of functions that facilitate the resolution of many mathematical and engineering problems. For example, I used it for Signal Analysis, specifically for Seismic data analysis. it for Ex. 1 and Ex. 2: Signal Processing in Geophysics Ex.1: That allows defining the coordinates of the layers of a geological model by opening an image file of the geological model and selecting, by clicking with the mouse, a set of points (or coordinates) that define each of the layers of the geological model. These coordinates will be saved in a very particular format that will be used as input of another program that is in charge of building the Geological model entity used by another program to perform a Seismic Wave Propagation Modelling. In the latest years I decided to reorient my career toward IT, specifically toward Data Sciences and Software Development. During my Bsc. in Information Technology, I have developed an excellent academic level and a clear understanding of the most important Object-Oriented Principles and Concepts. I have developed several object-oriented Java applications. I have a special interest for Web Development. I have also developed several Web Applications using different technologies: HTML, CSS PHP JavaScript But my main experience is using JavaScript frameworks: React for the Frontend Express.js for the backend. This is a Node.js framework: HTTP REST APIs Dash: Python web application framework for building data analytic applications So, I'm a programmer. Even if I haven't worked in a programming position for a long time, during my academic and professional experience I have worked in programming in several ocations. As I said I've been programming for 15 years. And during this time I have used many programming languages. I like programming so much that even when I'm writing a report I use a programming-based tool (Latex), I don't use a word processor like Microsoft Word. So, the programming logic, principles, and concepts of object-oriented programming, etc, is something that I'm really proficient in. Of course, I don't have 10 years experience working in a Software Developer role, so of course, you can ask me something about programming that I don't know, but you can be sure that I know how to program and that I'm able to learn any new programming language or concept in a very short time. So that is somethign that I really wanted to make clear, that I'm really proficient in programming... that I'm very confident about my programming skills... Python experience: I started using Python during my experience in Seismic data analysis. With four years of professional experience in this field, I developed a solid understanding of the most widely used Python libraries for Data Analysis, Machine Learning, and Data visualization, including Numpy, Pandas, Scikit-learn, SciPy, Matplotlib, Plotly, etc. More recently, I have acquired experience in Python web development using Django and Dash. In this regard, I have gained hands-on experience building REST APIs with Django and I'm currently actively engaged in expanding my skills by learning FastAPI. These are a couple of Web Apps I have developed using Python: http://dashboard.sinfronteras.ws/sentiment http://www.vglens.sinfronteras.ws/ You can see a more extensive list of my projects at: http://wiki.sinfronteras.ws/view/CV#Portfolio Projects: Java Desktop App - Zoo Management System In this project, we have created a GUI Java (Swing) Application for a Zoo Management System. You can try the application by downloading the Java Jar file from this link: http://wiki.sinfronteras.ws/images/2/27/ZooManagementSystem.jar Github repository: https://github.com/adeloaleman/JavaDesktopApp-ZooManagementSystem Java Desktop App - Stock Market Simulator In this project, we have created a GUI Java (Swing) Application that simulates a trading day of a simplified model of a stock market. You can try the application by downloading the Java Jar file from this link: http://wiki.sinfronteras.ws/images/0/00/SMSimulator.jar Github repository: https://github.com/adeloaleman/JavaDesktopApp-StockMarketSimulator Seismic Wave Propagation Modelling Computer Science and IT Programming and Software Development Some of the languages I have worked with: Python Python for Data Science Django FastAPI Dash - Plotly Manim JavaScript React Node.js - Express.js HTML & CSS Java Java GUI Programming SQL Shell Scripting R PHP C Multi-Paradigm Programming and Scripting Object-Oriented Concepts and Constructs SOLID Principles Class vs Object vs Instance Methods Variables Modifiers Four fundamental OOP concepts Encapsulation Inheritance Polymorphism Abstraction Nested classes Upcasting - Downcasting Errors and Exceptions Passing Object References Connecting to a MySQL database using Java Threads Serialization Object Orientation with Design Patterns Creational Patterns Singleton Pattern Factory Pattern Abstract Factory Pattern Builder Pattern Prototype Pattern Structural Patterns Adapter Pattern Facade Pattern Proxy pattern Decorator Pattern Behavioural Patterns Chain of Responsibility Pattern Command Pattern Iterator Pattern Observer Pattern Mediator Pattern Data Access Patterns Data Access Object Pattern Model-View-Controller MVC Data Structures & Algorithms Analysis of algorithms The Stack Data Structure The Queue Data Structure The Array Data Structure The List Data Structure The Linked List Data Structure The Tree Data Structure Recursion vs Iteration Bubble and Insertion Sort Web Development HTML CSS Django FastAPI JavaScript React React_2 Node.js - Express.js Dash - Plotly PHP Ruby on Rails Web server Web mapping HTML presentation with Reveal.js Excelentes diseños de WebSites Mobile Development Native vs Hybrid Frameworks Cordova - PhoneGap Ionic and Capacitor Systems Analysis & Design Universal design GitHub Data Science OpenAI - ChatGPT Descriptive Data Analysis Correlation & Simple and Multiple Regression Naive Bayes Decision Trees K-Nearest Neighbour Boosting Perceptrons - Neural Networks and Support Vector Machines Association Rules - Market Basket Analysis Clustering Time Series Analysis Model Evaluation Text Analytics Information Retrieval Natural Language Processing Python for Data Science NumPy and Pandas Data Visualization with Python Pandas Built-in Data Visualization Matplotlib Seaborn Plotly and Cufflinks Word cloud Text Analytics in Python Dash - Plotly RapidMiner Power BI Jupyter Big Data Integration ML Songwriting Other qualifications in IT Databases Database design Conceptual design Entity relationship modelling The Enhanced Entity-Relation Model Logical design Relational Model Normalisation RDBMS MySQL Backup and Restore MySQL/MariaDB Databases Import a CSV file into a MySQL database Reset the MySQL/MariaDB Password Authentication and Authorization SQL Practice Labs Stored Procedures Stored Functions Triggers Events Cursors Transactions Exception Handling PostgreSQL GUI DB administration tools MySQL Workbench SQLectron Cloud Computing AWS Google Cloud Operating Systems Linux Shell Scripting Networking Network Simulation using PacketTracer Docker Integration and automation of business apps Other IT pages Google Mi Computadora - Phone Physics and Mathematics Physics https://www.youtube.com/watch?v=M22MEShcyx8 https://www.youtube.com/watch?v=tfSMcDqnh00&t=2061s https://www.youtube.com/watch?v=NrjFE_Rd2OQ&t=81s https://www.youtube.com/watch?v=e5_V78SWGF0 Mathematics Geophysics Modelado sísmico Trading Social sciences and Humanities Filosofía Historia La Révolution Cubana La Revolución Rusa Independencia de Venezuela La Révolution Française Cristianismo Judaísmo The big bang theory Origin and evolution of Homo sapiens Literatura Geopolitics Lideres Marx Che Fidel Chávez Ali Silvio Facundo Rubén Mohamed Ali Computational Modeling of Social and Political Systems Archivo - Discursos - Debates - Frases Música Composición Guitarra - Bajo Salsa Video management Canciones - Films - Books Otras Páginas Sitios web Meditation Arquitectura Running Traveling Flamenco Venezuela IDG ────────────────── My MediaWiki help Página de pruebas	Base Mis páginas Página base Sidebar Desktop Timeline CV Carrera CV CV - Skills and Qualifications CV - Skills and Qualifications_1 CV - Work experience CV - Education CV - Courses CV - Publications CV - Languages CV - Interests Job search Entrevista Mis proyectos Important resources https://github.com/adeloaleman https://app.grammarly.com https://www.office.com My Wikipedia Sandbox https://en.wikipedia.org/wiki/User:Adeloaleman/sandbox Jupyter Notebook https://colab.research.google.com Online IDE https://repl.it Online Web IDE https://jsfiddle.net Download video from youtube https://en1.savefrom.net/2ol/ Mapas

• Some of the languages I've worked with:

• Python

• Python for Data Science

• Django

• FastAPI

• Dash - Plotly

• Manim

• JavaScript

• React

• Node.js - Express.js

• HTML & CSS

• Java

• Java GUI Programming

• SQL

• Shell Scripting

• R

• PHP

• C

• MATLAB

• LaTeX

• Object-Oriented Concepts and Constructs

• SOLID Principles

• Class vs Object vs Instance

• Methods

• Variables

• Modifiers

• Four fundamental OOP concepts

• Encapsulation

• Inheritance

• Polymorphism

• Abstraction

• Nested classes

• Upcasting - Downcasting

• Errors and Exceptions

• Passing Object References

• Connecting to a MySQL database using Java

• Threads

• Serialization

• Object Orientation with Design Patterns

• Creational Patterns

• Singleton Pattern

• Factory Pattern

• Abstract Factory Pattern

• Builder Pattern

• Prototype Pattern

• Structural Patterns

• Adapter Pattern

• Facade Pattern

• Proxy pattern

• Decorator Pattern

• Behavioural Patterns

• Chain of Responsibility Pattern

• Command Pattern

• Iterator Pattern

• Observer Pattern

• Mediator Pattern

• Data Access Patterns

• Data Access Object Pattern

• Model-View-Controller MVC

• Data Structures & Algorithms

• Analysis of algorithms

• The Stack Data Structure

• The Queue Data Structure

• The Array Data Structure

• The List Data Structure

• The Linked List Data Structure

• The Tree Data Structure

• Recursion vs Iteration

• Bubble and Insertion Sort

• Web Development

• HTML

• CSS

• Django

• FastAPI

• JavaScript

• React

• React_2

• Node.js - Express.js

• Dash - Plotly

• PHP

• Ruby on Rails

• Web server

• Web mapping

• HTML presentation with Reveal.js

• Mobile Development

• Native vs Hybrid Frameworks

• Cordova - PhoneGap

• Ionic and Capacitor

• Systems Analysis & Design

• GitHub

Data Analysis (Data Interpretation, Data modelling, performing analysis based on data), it is NOT something new for me at all, it's something that I have been working on for several years as a Geophysicist.

I am currently working on Business analysis/Data visualization...

During my career as a Geophysicist, I worked on Data Analysis amd Machine Learning. I worked, for example:

• Signal Analysis (Seismic data analysis), which is a way of Time Series Analysis (and Time Series is an important topic in Data Analysis).
• Well-Log analysis is also a kind of Data Analysis; where we analyse physical properties of the geologic formation (of the rocks) under the subsurfce.

I also worked in the creation/training and testing of Machine Learning model (Regression, classification) for Seismic data analysis and Borehole (well log) data analysis (HampsonRussell software by CGG).

• Reservoirs classification / Estimation of reservoir properties:
  • To classify rock foramtions in the subsurface using measurements of physical properties of the rocks.
  • To predict some physical properties of the rocks (Porosity or Permiability) by using measurements of other properties. See this paper: Comparison of machine learning methods for estimating permeability and porosity of oil reservoirs via petro-physical logs - https://www.sciencedirect.com/science/article/pii/S2405656117301633
• Seismic data classification

ML models for the prediction of reservoir properties can be trained (Supervised learning, Unsupervised learning, or Reinforcement learning techniques) by using seismic data, well logs data, core samples, and production data *(production data includes: Production rates (amount of hydrocarbons that are produced from the reservoir over time), Pressure and Temperature data over time). These models can learn from historical data to make predictions about the properties of new reservoirs.

Reservoir properties are important characteristics of an oil or gas reservoir that determine the amount of hydrocarbon resources that can be extracted from it. Some common reservoir properties that can be predicted using ML models include:

• Porosity: Porosity is a measure of the amount of pore space in a reservoir rock. ML models can be used to predict porosity based on well logs and seismic data.
• Permeability: Permeability is a measure of the ability of fluids to flow through a reservoir rock. ML models can be used to predict permeability based on well logs and production data.
• Saturation: Saturation is a measure of the amount of hydrocarbons in a reservoir rock. ML models can be used to predict saturation based on well logs and production data.
• Lithology: Lithology refers to the type of rock that makes up a reservoir. ML models can be used to predict lithology based on well logs and seismic data.

So, in the same way that we use a supervised algorithm (for example a linear regression method) for predicting the price of a house based on housing datae (like number of rooom, age of the house, lot size, etc.). In geophysics (or in petrophysics), we can use physical properties of the rocks to estimate some property of interest, such as permeability and porosity.

An oil well is a (drilling | a hole drilled) boring in the Earth that is designed to bring petroleum to the surface (Oil well ~ borehole).

An well-log is a record of measurements of physical properties of the geologic formations (the rocks in the subsurface) penetrated by a borehole. In other words, a well-log is a record of measurements of physical properties of the rocks as a function of depth. Some of the physical properties that are measured are: Resistivity, Natural radioactivity of the rocks-formations (Gamma Ray Log). Because radioactive elements tend to be concentrated in shales, the Gamma-ray log normally reflects the shale content of the formation. Sound wave velocity: measurement of the time required for a sound wave to travel a constant distance. The principle is that velocity of the rock decrease when the porosity increase.

Signal analysis:
There are many mathematical concepts related to signal analysis and thus to time series analysis that I've been using for a long time as a geophysicist, such as:

• Time series and Discrete signals
• Correlation, Auto-correlation, Cross-correlation
• Regression methods (Linear regression)
• Convolution and Deconvolution
• and, of course, concepts related to signal analysis like, Fourier series, Fourier transform etc.
• and many other concepts related to data analysis...

Examples of projects:

• Media:Application_of_machine_learning_algorithms_using_seismic_data_and_well_logs_to_predict_reservoir_properties.pdf

https://www.earthdoc.org/content/papers/10.3997/2214-4609.201800920

• Media:Application_of_machine_learning_algorithms_in_hydrocarbon_exploration_and_reservoir_characterization.pdf
• Seismic facies analysis using machine learning: https://library.seg.org/doi/10.1190/geo2017-0595.1
• Statistical imaging of faults in 3D seismic volumes using a machine learning approach: https://library.seg.org/doi/abs/10.1190/segam2017-17589633.1
• Coherence Cube (crosscorrelation): http://www.chopraseismic.com/wp-content/uploads/2015/09/COH_First-Break_012002.pdf
• In this paper is explained how Autocorrelation, Cross-correlation, and other time series analysis method are applied to seismic data https://www.sciencedirect.com/topics/earth-and-planetary-sciences/autocorrelation
• AVO

I also got a Diploma in Predictive Data Analytics that I got at CCT College, where I got a distinction. I have also compleate online courses on Data Analysis (mostly related to Python for data analysis); I have worked on NLP in my last 2 final degree projects; in my opinion, there is not a better way of learning something than working on a long academic project.

• Text classification: Supervised Machine Learning for Fake News Detection:

• Sentiment Analysis: Developing a Web Dashboard for analyzing Amazon's Laptop sales data:

• In my final Bachelor (Honours) in IT I worked in Sentiment Analysis using Python. I specifically developed a Web Dashboard for analyzing Amazon's Laptop sales data, mainly to perform a Sentiment Analysis on Amazon customer reviews.
  • I have performed a Sentiment Analysis of Amazon customer reviews by using both, Lexicon-based and Machine Learning methods.

  • Lexicon-based Sentiment Analysis: One of the purposes of this study is to evaluate different Sentiment Analysis approaches. That is why I performed a Lexicon-based Sentiment Analysis using two popular Python libraries: Textblob and Vader Sentiment.

  • Machine Learning Sentiment Analysis: I have built a ML classifier for Sentiment Analysis using the Naive Bayes algorithm and an Amazon review dataset from Wang et al. (2010).It is important to notice that this is an extra result with respect to the initial objectives. I haven’t planned to carry out this studio. However, I realized that it was very beneficial to include another Sentiment Analysis approach. This has allowed me to evaluate and compare both approaches in terms of their performance.

  • In addition, a Word Emotion Association Analysis has been also performed. This analysis complements the polarity analysis by adding more details about the kind of emotions or sentiments (joy, anger, disgust, etc.) in customer reviews. This analysis was performed by using the NRC Word-Emotion Association Lexicon.

• Projects:

• Developing a Web Dashboard for analyzing Amazon's Laptop sales data

• Try the App at http://dashboard.sinfronteras.ws

• Github repository: https://github.com/adeloaleman/AmazonLaptopsDashboard

• eComerce Web Application for an optical glasses retailer

• Visit the Web App at http://www.vglens.sinfronteras.ws

• This Application was developed using Python-Django Web framework

• Web App - Clone of Twitter

• Visit the Web App at http://62.171.143.243

• Github repository: https://github.com/adeloaleman/WebApp-CloneOfTwitter

• This Application was developed using:

• Back-end: Node.js (Express) (TypeScript)

• Front-end: React (TypeScript)

• Supervised Machine Learning for Fake News Detection

• Visit the Web App at http://fakenewsdetector.sinfronteras.ws

• Github repository https://github.com/adeloaleman/RFakeNewsDetector

• OpenAI - ChatGPT

• Descriptive Data Analysis

• Correlation & Simple and Multiple Regression

• Naive Bayes

• Decision Trees

• K-Nearest Neighbour

• Boosting

• Perceptrons - Neural Networks and Support Vector Machines

• Association Rules - Market Basket Analysis

• Clustering

• Time Series Analysis

• Model Evaluation

• Text Analytics

• Information Retrieval

• Natural Language Processing

• Python for Data Science

• NumPy and Pandas

• Data Visualization with Python

• Pandas Built-in Data Visualization

• Matplotlib

• Seaborn

• Plotly and Cufflinks

• Word cloud

• Text Analytics in Python

• Dash - Plotly

• RapidMiner

• Power BI

• Jupyter

• Big Data Integration

• ML Songwriting

• SQL

https://content.dsp.co.uk/the-key-responsibilities-of-a-database-administrator

• Install and configure database system
• Install and test new versions of the database management system (DBMS)
• Database Backup and Recovery
• Develop, manage and test back-up and recovery plans
• Maintaining database backup and recovery infrastructure
• Troubleshooting
• Make data available to authorized persons
• Create user documentation and user guidelines (tambien participe en la documentacion porque ellos no sabian si iban a usar este mediawiki siempre o si iban a desarrollar su propia aplicación. Se estaba haciendo una documentacion de la estructura de la base de datos para posibles migraciones)

• Linux

• I've been using Linux for about 15 years as my main OS. I consider myself a Linux power user, capable to program Shell Scripts and perform administrative tasks. I'm mostly a Debian-based systems user, but I have experience with the most popular flavors of Linux: Ubuntu, Red Hat, CentOS, Mint, SuSE.

• Throughout my career, I have worked on several occasions in activities related to Linux administration:

• Research geophysicist at GRyDs:

I was, for example, responsible for developing shell scripts for task automation and signal analysis.

• WikiVox:

I had the opportunity to work in the installation and administration of a LAMP stack (LAMP is stand for Linux - Apache - MySQL - PHP). So all the softwares needed to host a web application on a Linux Server.

• I have also developed a personal project, in which I perform an automatic backup of my personal data that is stored in my computer (and my Wiki) into a hard drive and into the cloud (Linux server). To do so, I have developed a shell script using technologies such as: rsync, ssh, sshpass, tar, zip, MySQL database backup, sed, gpg.

• Wiki - Organize information into a cohesive, searchable and maintainable system.
  • One of the most important skills I have, which I usually find complicated to make understand its importance, is my Wiki management skills.
  • A Wiki is a website on which users can collaborate by creating and modifying content from the web browser. So, the best example is Wikipedia. In Wikipedia someone can create a article and then it can be modify online for other users. A Wiki is an outstanding tool to organize information into a cohesive, searchable and maintainable system that can be accessed and modified online. The benefits of a wiki to organize information are remarkable.

  I have a personal Wiki (based on the MediaWiki engine) where I document everything I'm learning and working on. So, I use a Wiki as a Personal knowledge management that allows me to organize information into a cohesive, searchable and maintainable system. The benefits that I've had using a Wiki are amazing. It has allowed me to learn in a more effective way; and most importantly, to constantly review and improve in important topics by providing a very convenient online access (so from anywhere) to an organized and structured information.

  Take a look at some of my Wiki pages: http://perso.sinfronteras.ws/index.php/Computer_Science_and_IT

• Academic assistant at USB: Communication, Presentation and Leadership Skills

As an Academic Assistant, I was in charge of collaborating with the lecture by teaching some modules of the Geophysical Engineering program at Simón Bolívar University. I was usually in charge of a group of between 20 and 30 students during theoretical and practical activities.

• Courses taught:

• Seismic data processing: Concepts of discrete signal analysis (time series analysis), sampling, aliasing, and discrete Fourier transform. Conventional seismic data processing sequence.
• Seismic methods: The convolutional model of the seismic trace. Propagation and attenuation of seismic waves. Interpretation of seismic sections.
• Seismic reservoir characterization: Relations between the acoustic impedance and the petrophysical parameters. Well-Seismic Ties. Seismic data analysis (Inversion and AVO).

• This experience has contributed to my professional development in two major areas:

• By teaching modules, I have enhanced my technical geophysical knowledge.
• I have also developed communication and presentation skills, as well as the leadership strategies needed to manage a group of students and to transfer knowledge effectively.

• IDG: Communication and Sale Skills
  • My current job at IDG is about communication. First, because I'm working in a team, and we always have to reach targets as a team, and communication within the team is always the key to reach the targets. Secondly, because one of my main responsibilities is to call contacts (to call IT Managers) on behalf of our clients, and of course this is about effective and clear communication. I have to explain to the contact the reason for the call, the topic of the campaign, and most importantly, I have to communicate in a way that... well I have to create an atmosphere in the call where the contact is going to feel comfortable and is going to accept answering my questions.
  • In this position, I have improved my communication skills in French and English. I have learned how to build and maintain a professional relationship and improved my Active Listening Skills.
  • I also think that I have developed communication skills not only at work but also in other aspects of my life; you know I have always done team sports in a high competitive-level: Volleyball when I was a chield; I was member of the Volleyball team of my state and attended 1 national games; and Waterpolo at university, where I attended 5 National University Games; and those are activities where you develop, sometimes without being aware, you develop many communication skills.
  • I have to call IT Managers and establish and maintain a professional conversation with them in order to identify their next investments. So from this conversation we gather information about their next investment and this information is required from our clients (IT Companies: IBM, DELL, NetApp, etc) and they use this information next step of the sales process.
  • Let's say that IBM is looking to sell a particular product (A Cloud backup solution, for example). So, IBM requires IDG's services, asking for a number of contacts (IT Managers) that are planning to invest in backup solutions. Then, we establish a professional conversation with IT Managers from our database and identify those that are looking to invest in the product required for the client.
  • During the phone conversations, I have to explain the topic of the product that our clients are looking to sell and be able to handle objections. That is why this experience has made me aware of the latest solutions and technologies in which the most important IT companies are working on.
  • At IDG, I have also completed a Certified Sales training. During this course, I have learned and put into practice, the most important concepts of the sales process.

  • Prospecting, Preparation, Approach, Presentation, Handling objections, Closing, Follow-up

  https://www.lucidchart.com/blog/what-is-the-7-step-sales-process

• Target and KPI
  • I'm used to work in a Target Working Environment because I'm currently working in a TWE at IDG.
  • At IDG we have to reach a daily target of about €650 per day.
  • To reach this target performance we need to generate what we call a «lead». A lead is a conversation that matches the criteria asked for the client. For example, if the client (Let's see IBM) is asking for contacts that are looking to invest in Backup solutions, then every time that we have a conversation in which the contact confirms to be looking for backup solutions; this contact represents a «lead».
  • So each lead that we generated has a price, and we need to generate as many leads as needed to reach the target of €650. So normally an easy lead worth about €65 and a complicated one about €180.
  • So, every day we need to fight to reach the target performance. We usually have many challenges to reach the target performance:

  • Data challenges: We make calls using particular data that has been prepared for a particular campaign. Many times you can make many calls but you don't reach the contacts that you are looking for. So you can spend your day making calls but not having conversations with the IT Manager. So if you are not reaching the contact, you can not make leads.
  • Hard campaign challenges: That means that we have a campaign in which the client is asking for a difficult criterion. Let's say, for example, that the client is asking for contacts that are looking to invest in a particular solution (SAP applications for example). That represents a campaign challenge because we have to reach a contact that is looking to invest, specifically, in this solution.

  • Solutions: There are a few techniques that we use to apply when we face the challenges. Change the data or the campaign you're working on is the first action we can take. But sometimes you can not change the campaign because we really need to deliver lead for those campaigns because we need to reach a certain number of leads the client is asking for. We usually make calls using a platform that makes the calls automatically taking the contact from the database related to the campaign you're working on. So usually we don't need to worry about the criteria (company size, job title, industry) of the contacts we are calling because the platform makes the calls. But when you have data problems, the solution is to research for contacts manually. So, that is a little tricky because you can try to call the best contact by doing manual research in the database, but you can spend a long time doing this research and that doesn't assure that you are going to reach the contact and get leads. So when you have good data you have to use the platform, otherwise, you should search for contacts manually. So in this manual research is where you have to propose ideas and develop a good methodology to be able to find good contacts and get leads. One of the techniques we apply when we have a hard campaign is, for example, if we get a lead from a particular company; we try to call other contacts from the same company because we know that this particular company is going to review in the product that the client is looking for.

  The other approach is to try to search new contacts on the internet (usually on Linkedin), but that is even more tricky because it is complicated to get reach a new contact and to get the lead. Here is where I wanted to say that I had an important contribution. So the problem with this external research is that most of the contact that you are going to find on Linkedin is already in our database. So it doesn't make sense. But I realized that when we are looking for business job titles (because sometimes we have campaigns in which the client is asking for business titles) it makes sense to do external research (on Linkedin) because our database is composed mostly for IT Professionals (we have some business contacts in our database, but not a lot) so the chance of finding a contact on Linkedin that is not in our database increase a lot. Therefore, it makes sense to do external research when looking for business contacts. By doing that, I was able to get a good number of leads for hard campaigns; and that is a concrete contribution that I made to my team.

• Simón Bolívar University and background in Mathematics/Physics

  I'm an engineer from the most important scientific Venezuelan university, which is Simón Bolívar University; and really, I need to highlight the academic level and the quality of Simón Bolivar University. If you check now, Simón Bolívar University is still in a good place in the LatAm University Rankings; but the university has been widely affected by the difficult political situation in the country. I don't know if you have heard about the critical political and economical situation in Venezuela. But the fact is that in my time when I started my career, Simón Bolívar university was always in the top 10 of the best LatAm Universities with scientific and technological orientation.

  I have a very good background in formal and pure sciences, like mathematic and physic. I followed 7 pure maths and 5 pure physics courses; without counting all the applied geophysical courses that I followed with a high content of mathematics, physics, or chemistry.

  If you review the course content of an IT program you will find at most 2 mathematic courses. I really think that for an IT professional it is very important to have a good background in mathematic. For example, to be able to understand some computational concepts (functional programming for example) you need to have a good mathematical background.

• Geophysisc:

Geophysics is an applied science, we said that is a multidisciplinary field, that uses physic, mathematic, and geology to study the internal constitution and history of the earth. The term geophysics sometimes refers only to solid earth applications: Earth's shape; its gravitational and magnetic fields; its internal structure and composition; its dynamics and their surface expression in plate tectonics, the generation of magmas, volcanism and rock formation. [Wikipedia]. One of the first pieces of geophysical evidence that was used to support the movement of lithospheric plates (Plate tectonics) came from paleomagnetism. This is based on the fact that rocks of different ages show a variable magnetic field direction.

One of the main applications of Geophysics is in oil exploration, that is the area where I have experience. I specifically worked

During my acadimic and professional experience as a Geophysicist, I was involved in several data analysis topics:

• Seismic exploration - Seismic processing

I specialized in Seismic exploration for oil and gas, specifically in Seismic analysis and Seismic data processing, which theory or mathematical foundation is related to Data Science. You actually can say that Seismic data processing is a way of Data Science.

Seismic analysis is a kind of Signal analysis; and Signal analysis is closely related to Time series analysis. Statistical signal processing uses the language and techniques of mathematical time-series analysis, but also use other concepts and techniques like signal to noise, time/frequency domain transforms and other concepts specifically related to the physical problem under study. Of course, there are also many other concepts use in time series analysis applied to business and economics, such as time-series forecasting, trend analysis, etc. that are not present in the material on statistical signal processing; but in general Signal Analysis (which is the area where I have experience) is closely related to Data Analytics and Time-Series analysis in particular https://stats.stackexchange.com/questions/52270/relations-and-differences-between-time-series-analysis-and-statistical-signal-pr#:~:text=2%20Answers&text=As%20a%20signal%20is%20by,significant%20overlap%20between%20the%20two

======

The signal that is analysed in Seismic analysis (the seismic signal) is a Seismic wave. A Seicmic waves is an acoustic wave that propagates through the earth. So, this wave can be recorded to obtain a mathematical (or functional) representation of the seismic wave. This function (or signal), which is called a Seismogram, represents ground motion measurements as a function of time; and of course, these ground motions are related to the wave propagating through the earth.

The data tha we analyse in Seicmic Analysis (Seismic Data) consists on a large set of time series. These time series are called Seismograms or Seismic traces; but mathematically are just time series.

In physical terms, we can say that a seismogram is basically a representation of a seismic wave propagating into the subsurface. Now, in mathematical terms, a seismogram (seismic trace) is a time series of ground motion values (the ground motions are related to the wave propagating in the subsurface). In other words, a seismogram describes ground motions as a function of time.

In short, the purpose of seismic exploration is to create an image of the subsurface and to estimate the distribution of a range of properties - in particular, the fluid or gas content. This way the geophysicist is able to have a better idea of where oil or gas deposits can be located in the subsurface.

So, after the Seismic acquisition phase (that is something that I'm not going to explain now because I want to focus on the seismic data processing, that was my sector, and I wanted to explain the relationship with Data Sciences) the Seismic Data consists on a large set of time series. These time series are called Seismograms or Seismic trace; but mathematically are just time series.

I have worked in this area in my two thesis projects (bachelor and master's degrees). I have experience as an academic assistant of the course of Seismic Data processing at Simón Bolívar University; I have worked at the CGGVeritas processing center in Caracas and in an R&D Unit at PDVSA and Simón Bolívar University. So I have considerable experience in Seismic data processing, but I'm sure that the most important of all it's that I have the motivation to further developed my skills in Seismic Data processing, I am now incredibly motivated to pursue my career in Seismic data processing.

======

So, there are many mathematical concepts related to signal analysis and thus to time series analysis that I've been using for a long time as a geophysicist, such as:

• Time series and Discrete signals
• Correlation, Auto-correlation, Cross-correlation
• Regression methods (Linear regression)
• Convolution and Deconvolution
• and, of course, concepts related to signal analysis like, Fourier series, Fourier transform etc.

What Seismic attributes are: https://wiki.aapg.org/Seismic_attributes

• Projects:

• Databases

• Database design

• Conceptual design

• Entity relationship modelling

• The Enhanced Entity-Relation Model

• Logical design

• Relational Model

• Normalisation

• RDBMS

• MySQL

• Backup and Restore MySQL/MariaDB Databases

• Import a CSV file into a MySQL database

• Reset the MySQL/MariaDB Password

• Authentication and Authorization

• SQL Practice Labs

• Stored Procedures

• Stored Functions

• Triggers

• Events

• Cursors

• Transactions

• Exception Handling

• PostgreSQL

• GUI DB administration tools

• MySQL Workbench

• SQLectron

• Cloud Computing

• AWS

• Google Cloud

• Linux

• Advanced experience with the most popular flavors of Linux: Debian, Ubuntu, Red Hat, CentOS
• LAMP Administration: Apache, MySQL, PHP
• Installation and Post-installation configurations
• Users and Groups Administration
• Modify File Permissions
• Managing Processes
• Backups
• Network File System (NFS)
• Remote Management with SSH

• Shell Scripting

• Networking

• Network Simulation using PacketTracer











Portfolio




https://github.com/adeloaleman Developing a Web Dashboard for analyzing Amazon's Laptop sales data Try the App at http://dashboard.sinfronteras.ws Github repository: https://github.com/adeloaleman/AmazonLaptopsDashboard eComerce Web Application for an optical glasses retailer Visit the Web App at http://www.vglens.sinfronteras.ws This Application was developed using Python-Django Web framework Web App - Clone of Twitter Visit the Web App at http://62.171.143.243 Github repository: https://github.com/adeloaleman/WebApp-CloneOfTwitter This Application was developed using: Back-end: Node.js (Express) (TypeScript) Front-end: React (TypeScript) Supervised Machine Learning for Fake News Detection Visit the Web App at http://fakenewsdetector.sinfronteras.ws Github repository https://github.com/adeloaleman/RFakeNewsDetector Java Desktop App - Zoo Management System In this project, we have created a GUI Java (Swing) Application for a Zoo Management System. You can try the application by downloading the Java Jar file from this link: http://wiki.sinfronteras.ws/images/2/27/ZooManagementSystem.jar Github repository: https://github.com/adeloaleman/JavaDesktopApp-ZooManagementSystem Java Desktop App - Stock Market Simulator In this project, we have created a GUI Java (Swing) Application that simulates a trading day of a simplified model of a stock market. You can try the application by downloading the Java Jar file from this link: http://wiki.sinfronteras.ws/images/0/00/SMSimulator.jar Github repository: https://github.com/adeloaleman/JavaDesktopApp-StockMarketSimulator Seismic Wave Propagation Modelling









Relevant courses




Build a Backend REST API with Python & Django - Advanced See course content at https://www.udemy.com/course/django-python-advanced/ FastAPI - The Complete Course 2023 (Beginner + Advanced) See course content at https://www.udemy.com/course/fastapi-the-complete-course/ Build a Modern API with FastAPI and Python See course content at https://www.udemy.com/course/build-a-modern-api-with-fastapi-and-python/ Modern React with Redux - 2023 Update See course content at https://www.udemy.com/course/react-redux/ TensorFlow Developer Certificate in 2023: Zero to Mastery See course content at https://www.udemy.com/course/tensorflow-developer-certificate-machine-learning-zero-to-mastery/ Python for Data Science and Machine Learning Bootcamp Content overview NumPy and Pandas Python for Data Visualization: Matplotlib, Seaborn, Pandas Built-in-Data Visualization Plotly and Cufflinks Geographical Plotting Linear Regression Cross Validation and Bias-Variance Trade-Off K Nearest Neighbors Decision Tress and Random Forests Support Vector Machines K Means Clustering Principal Component Analysis Recommender Systems Natural Language Processing . See the complete course content at https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/ Click here to see the Certificate Interactive Python Dashboards with Plotly and Dash See course content at https://www.udemy.com/course/interactive-python-dashboards-with-plotly-and-dash/ Object Oriented Programming in Python (150+ Exercises) See course content at https://www.udemy.com/course/exercises-object-oriented-programming-in-python-oop-course/ AWS Academy - Cloud Foundations Content overview Introduction to AWS Cloud Essential Characteristics of cloud computing, Service Model, Deployment Models AWS Global Infrastructure: Regions, Availability zones, Edge Locations . AWS foundation services: Compute: EC2, AWS Lambda, ECS, Auto Scaling Networking: VPC, Elastic Load Balancer, Route 53 Storage: Amazon EBS, Amazon S3, Amazon EFS, Amazon Relational Database Service (RDS) AWS Academy - Cloud Architecting Content overview Designing a cloud environment Designing for High Availability Configuring VPS, Availability zones, NAT Gateway, Route Table, Load Balancer, Auto Scaling Group. Automating your Infrastructure Infrastructure as code AWS CloudFormation Templates . Decoupling your Infrastructure Loose coupling Strategies Designing Web Scale Media Storing Web-Accessible Content with Amazon S3 Caching with Amazon CloudFront Storing relational data in Amazon RDS, Managing NoSQL databases Multi-region failover with Amazon Route 53 . . . . . Red Hat Certified Systems Administrator Prep (RHCSA) Content overview Essential tools: Input/output redirection. Grep and Regular Expressions to analyse text Access remote systems using SSH. Configure key-based authentication for SSH. Finding files with Locate and Find Archive and Compress files using tar and gzip. Create hard and soft links Managing Processes: Adjust process priority and kill processes Configure Local Storage: List, Create and Delete partitions on MBR and GPT Disks. Configure systems to mount file systems at Boot Add new partitions, logical volumes and Swap to a System Create and configure File Systems: Create and Mount VFAT, EXT4 and XFS file systems. Mount CIFS and NFS Network File systems Configure network connections statically or dynamically. Schedule tasks using «at» and «cron» Install and update software packages and managing repositories using «yum» Manage users and groups: Create, delete and modify user accounts and groups Configure Firewall settings. See the complete course content at https://www.udemy.com/course/linux-academy-red-hat-certified-systems-administrator-prep/

Papers and Publications

• Adelo Vieira and Crelia Padrón.

Analysis of pull up effects through computational seismic data modeling and depth migration, Colombian plains. ↓Download

Estimation and modelling of reservoirs properties through seismic attributes and geostatistics in gas fields, southern Spain. SOVG, XIV Venezuelan Geophysics Congress I was co-author of a paper published in the XIV Venezuelan Geophysics Congress, in 2008. In this paper is performed a study of Seismic reservoir characterization (it's when you use seismic data for the estimation of rock properties). We used geostatistics to integrate seismic data and well logs. Also, because in this area we found clear amplitude anomalies, we analyze the origin of the anomalies in one of the reservoirs in this area. There are many methods trying to find a relationship between seismic attributes and well logs. The most basic method would be to plot a seismic attribute against a rock property and make a linear regression to find a relationship between the seismic attribute and the rock property. The geostatistical methods are much more complex than linear regression but they are more reliable. The attributes we used were: the instantaneous amplitude, amplitude RMS, instantaneous frequency and phase.

Estimación y modelado de propiedades de yacimiento a través atributos sísmicos y geoestadística en campos de gas del sur de España. SOVG, XIV Congreso venezolano de Geofísica. También, debido a que en esta zona la data sísmica muestra claras anomalías de amplitud, se analiza el origen de la anomalía de amplitud generada por uno de los yacimientos tipo en el área de estudio. Métodos como la regresión lineal proveen información acerca de la relación de los atributos y los datos de pozo. Los métodos geoestadísticos son mucho más complejos que los métodos de regresión lineal, pero producen resultados más confiable. Atributos utilizados: amplitud RMS, amplitud instantánea; atributos de frecuencia, frecuencia instantánea y fase; así como superficies de impedancia acústica. Con datos provenientes de estos 5 pozos se realizaron entonces los gráficos cruzados correspondientes para tratar de obtener alguna relación lineal, y los mejores resultados obtenidos se muestran en la Tabla 1 Posteriormente, se realizó un análisis de una de las anomalías de amplitud observadas. Con dicho propósito generamos un gráfico de la magnitud de la reflexión Rpp vs. θ (ángulo de incidencia) para un yacimiento gasífero que genera fuertes anomalías de amplitud negativa Esto se hizo a través de las ecuaciones de Zoeppritz tomando los datos de ρ y Vp de los registros de pozo, y utilizando el modelo de Castagna et al. (1985) para estimar Vs Por otra parte, causa suspicacia el hecho de que el yacimiento considerado presenta un espesor bastante pequeño (7 m). Esto nos obliga a pensar en la posibilidad de que las amplitudes anómalas observadas respondan a un efecto de entonación. Estudio del efecto de entonación Para modelar este efecto se diseñó una cuña que simula el comportamiento de una capa que se afina progresivamente. Los valores de densidad y velocidad asignados a este modelo fueron los correspondientes al yacimiento de interés La ondícula empleada para simular la respuesta sísmica de esta cuña fue una Ricker fase cero con una frecuencia dominante de 60 Hz.; valor obtenido a partir del espectro de amplitudes de un grupo de trazas que muestran la anomalía producida por el reservorio considerado. A través de estos parámetros se obtuvo la respuesta sísmica que se muestra en la Figura 7 (a). El modelo mostrado en la Figura 7 (a) exhibe el efecto de máxima entonación a un espesor de aproximadamente 7 m; esto implica que nuestro yacimiento no es lo suficientemente espeso como para poder diferenciar la reflexión del tope y la base del mismo, produciéndose una superposición de ambas reflexiones. Este modelo comprueba la hipótesis de un incremento de las amplitudes sin relación alguna con la presencia del gas.

Il n'y a pas de traduction au français

• Alejandro Gutiérrez, Evert Durán, Adelo Vieira and Crelia Padrón.

Estimation and modelling of reservoirs properties through seismic attributes and geo-statistics in gas fields, southern Spain. SOVG, XIV Venezuelan Geophysics Congress, 2008. ↓Download

Analysis of pull up effects through computational seismic data modeling and depth migration, Colombian plains In geophysics is called \pu{} to an uplift recognized in the seismic data that is not caused by the shape of geological structures, but by a local high-velocity region or lateral velocity variations. Because there is a considerable lateral variation and a velocity inversion in the Colombian plain basin, this effect has been hypothetically associated with convex upward folded events observed in the seismic data acquired in the area. In this work, we perform seismic simulations from a flat layers geological model which bring together two representative features of the basin of Colombian eastern plains: normal faulting and velocity inversion; features that produce a singular lateral velocity variation. With this model, the paper discusses three simulation methods: normal ray tracing, prestack ray tracing, and finite differences. This allows visualizing the propagation of the rays through the geological model, generate post a prestack synthetic seismic data, and its corresponding processing sequence. The results show and explain the presence of \pu{} in areas near the fault on synthetic events corresponding to the footwall block. These elevations produced by \pu{} effects coincide relatively with the location of the uplifts observed in the real data; results suggest the existence of such anomaly in the seismic data acquired in the Colombian eastern plains. The data was provided by the Pacific Rubiales company.

Il n'y a pas de traduction au français

No existe traducción en español

Languages

		Listening	Reading	Spoken interaction	Spoken production	Writing
Spanish	Mother tongue
English	Fluent	C1	C2	C1	C1	C1
French	Fluent	C2	C2	C2	C2	C1
Portuguese	Intermediate	C2	C1	B2	B2	A2

A: Basic user.       B: Independent user.       C: Proficient user. Common European Framework of Reference (CEF) level

Honors and awards

• 2021 - Employee of the year (IDG Direct, Dublin)
• 2020 - Diploma in Predictive Data Analytics completed with an overall grade of Distinction (CCT College, Dublin)
• 2011 - Master thesis project graded as Excellence Honor Mention (Simón Bolívar University, Caracas)

Interests and other activities

• Member of the water polo team at Simón Bolívar University: Attendance at 5 National University Games.
• Swimming instructor at U.E.U.S.B school.
• Open-source software.
• Travel, Volleyball, Open-Water Swimming.