Tailor Maciel

About Tailor Maciel

Husband, Father, Son, Pet Lover, Entrepreneur and Aspiring Computer Scientist living in South America (order may change according to the circumstance :).
I enjoy engaging with people motivated by gratitude, collaboration and commitment to results.

Work experience

I have been working for over 20 years for the main raw material distribution companies in Brazil, with steel being the main product.
My main role has been commercially managing the supply negotiations.
During this period, I've discovered more than 1000 companies in the southern region of Brazil that consume the materials we sell.
I helped these companies form lasting relationships and develop mutual interests based on trust and understanding of key market dynamics.

Academic Education

University of London

April 2019 - Expected end March 2025

Bachelor - Computer Science

The education is based on programming activities based on P5.JS, C++, Python, SQL, Databases and WEB development.
Here I learned about the importance of OOP, software design development and theoretical mathematics for reasoning.
On line - Coursera

Uniftec Group - University Centre

Aug 2012 - Jun 2016

Degree - Management Processes

Education was mainly based on management tools for manufacturing and productive organizational processes, with a focus on project management tools associated with systemic thinking and marketing.
I learned the importance of human relationships and communication in business routines.
Caxias do Sul - RS Brazil

Courses and Trainings

Google IT Support

Operating Systems and You: Becoming a Power User.
The Bits and Bytes of Computer Networking.
IT Security: Defense against the digital dark arts.
System Administration and IT Infrastructure Services.
Technical Support Fundamentals

Data Science: Probability

Probability theory is the mathematical foundation of statistical inference which is indispensable for analyzing data affected by chance, and thus essential for data scientists.

Data Science: Inference and Modeling

How to define estimates and margins of errors of populations, parameters, and standard errors in order to make predictions about data.
How to use models to aggregate data from different sources. How to apply the very basics of Bayesian statistics and predictive modeling.

Data Science: Productive Tools

How to use Unix/Linux as a tool for managing files and directories and how to keep the file system organized. How to use the version control system git, a powerful tool for keeping track of changes in your scripts and reports.
How to write reports in R markdown which permits you to incorporate text and code into a document.

Data Science: Visualization

Data visualization principles to better communicate data-driven findings How to use ggplot2 to create custom plots. The weaknesses of several widely used plots and why you should avoid them

Data Science: R Basics

How to read, extract, and create datasets in R. How to perform a variety of operations and analyses on datasets using R. How to write your own functions/sub-routines in R.

Data Science: Essentials

How to build predictive analytics solutions with Azure Machine Learning.

Data Science: Principles of Machine Learning

Introduction to Classification, Loss Functions for Classification, Statistical Learning Theory for Supervised Learning, Logistic Regression, Maximum Likelihood Perspective, Evaluation Methods for Classifiers, ROC Curve Algorithm, Exploring Data for Classification, Multiple Linear Regression, Outliers, Improving Models, Regularization, Decision Trees, Boosting, Introduction to Neural Networks, Backpropagation, Support Vector Machines (SVMs), K-Means Clustering.

Data Science: Essential Statistics for Data Analysis

Descriptive Statistics, Basic Probability, Random Variables, Sampling and Confidence Intervals, Hypothesis Testing

Data Science: Analyzing and Visualizing Data with Power BI

Understanding key concepts in business intelligence, data analysis, and data visualization, Visualizing your data and authoring reports, Creating dashboards based on reports and natural language queries, Sharing dashboards effectively based on the organization’s needs, Connecting directly to SQL Azure, HD Spark, and SQL Server Analysis Services.

Data Science: Introduction to R for Data Science

Vectors, Matrices, Factors, Lists, Data Frames, Basic Graphics.

Data Science: Querying with Transact-SQL

Querying Tables with SELECT, Querying Multiple Tables with Joins, Using Set Operators, Using Functions and Aggregating Data, Using Subqueries and APPLY, Using Table Expressions, Grouping Sets and Pivoting Data, Modifying Data, Programming with Transact-SQL, Error Handling and Transactions,

Data Science Orientation

An overview of The Data Science Curriculum, Data Science Fundamentals and Getting Started with Data.

My Career Path

At fourteen I started working as a salesman. It helped me to develop speaking skills and to realize that listening to advice and listening to the most experienced ones goes a long way in avoiding unproductive paths.
I understood that the best results and what builds lasting relationships is a Win-Win attitude.
I decided to study Data Science after completing a degree in Management Processes in 2016, given the profound transformation that has been occurring in the job market and in business relations as of late.
Since then, I obtained certifications by GOOGLE, HARVARDX and MICROSOFT in the field of IT and Data Science and I realized that to apply the new knowledge effectively it would be necessary to immerse myself in Computer Science.
I want to make business processes simpler, more direct and less bureaucratic, to give others the opportunity to put their energies towards something more satisfying and productive.