Python is a high-level, versatile, interpreted and easy-to-learn programming language. It stands out for its clear and readable syntax, which makes it suitable for a wide range of applications in software development, data analysis, artificial intelligence and scripting, among other fields.
The R language is an open source statistical analysis and programming environment specifically designed for data manipulation, visualization and modeling. Noted for its wide range of packages and its emphasis on statistics and data science, R has become a fundamental tool in academic research, business data analysis and data science.
SQL (Structured Query Language) is a programming language designed to manage, manipulate and query relational databases. It is a widely used standard for data management in database management systems (DBMS), such as MySQL, PostgreSQL, Microsoft SQL Server, Oracle, among others.
Visit our page with the Data Analytics courses we offer for many different professional profiles.
You will learn Python, R and SQL as well as the main Machine Learning algorithms. You will learn how to create data models to solve complex business problems.
Data Mining refers to the process of discovering meaningful or potentially useful patterns and relationships from large data sets. This discipline is within the broader field of data analysis and has a strong overlap. It would be safe to say that Data Mining and Data Analytics are two ways of referring to the same processes.
Power BI is a powerful data analysis and visualization tool developed by Microsoft. It provides a set of software services, applications and connectors that work together to turn raw data into interactive visualizations, dashboards and interactive reports. This tool enables users to gain valuable insights from their data, facilitating analysis, information sharing and collaboration within an organization.
A Machine Learning algorithm is a set of mathematical instructions or rules that allow a computer system to discover patterns or make predictions from data, without being explicitly programmed to perform a specific task. These algorithms are the fundamental basis of machine learning, a field of artificial intelligence (AI).
Exploratory Data Analysis(EDA) is a meticulous process aimed at the comprehensive evaluation and understanding of data sets. Using statistical and graphical techniques, EDA seeks to summarize the essential characteristics of the data, identify underlying structures, patterns, distributions and significant relationships. Its main purpose lies in generating hypotheses, obtaining key insights and detecting possible anomalies or inconsistencies in the data. The successful application of EDA involves students’ understanding and use of concepts related to descriptive statistics.
Feature/variable engineering (feature engineering) represents a fundamental stage in the data pre-processing process in the field of machine learning and data mining. Its main objective lies in the creation of new features or in the transformation of existing ones, with the purpose of improving the performance of the models and enhancing the predictive capacity of the machine learning algorithms.
In simple terms, an algorithm is a set of step-by-step instructions designed to solve a problem or perform a specific task.
Any computer program is an algorithm. However, we call machine learning algorithms, with special emphasis, complex and sophisticated programs that perform very advanced mathematical logic operations.
In Ubiqum’s data analysis and machine learning courses, students learn how to use the main machine learning algorithms to create useful models for decision making.
Agile methodologies are a set of collaborative practices and approaches that seek to improve effectiveness and flexibility in software development and project management. These methodologies focus on adaptability, incremental delivery, collaboration and continuous feedback to achieve faster and more satisfying results.
dplyr is a software package in the R programming language, used to efficiently manipulate and transform data. It was developed by Hadley Wickham and is part of the R language package ecosystem, especially popular in the field of data analysis and data science.
ggplot2 is a data visualization package in the R programming language, created by Hadley Wickham. It is based on the “Grammar of Graphics” philosophy, which allows the creation of complex and customized graphics from data in an intuitive and flexible way.
Scikit-learn is an open source machine learning library for the Python programming language that provides simple and efficient tools for predictive data analysis. This library is designed to be accessible and easy to use, while offering a wide range of machine learning algorithms and tools for preprocessing, model evaluation and more.
caret is a library in R that provides a unified interface for training and evaluating machine learning models. Its name, “Classification And REgression Training”, highlights its initial focus on classification and regression, although it has evolved to include a wide range of supervised and unsupervised learning techniques and algorithms.
pandas is a powerful Python library designed specifically for structured data manipulation and analysis, providing flexible data structures and efficient tools for processing, cleaning, transforming and exploring datasets. This library is central to the Python ecosystem for data science and data analysis.
NumPy (Numerical Python) is a powerful Python library used primarily for performing numerical operations and working with multidimensional data structures, such as arrays and arrays. This library is fundamental in the field of scientific computing and data analysis, providing efficient structures for storing and manipulating numerical data.
Jupyter Notebook is an interactive computing environment that allows the creation and sharing of documents in which executable code, explanatory text, visualizations and other multimedia elements can be combined. The Jupyter Notebook platform is based on the Jupyter open source project, which derives its name from the main programming tools it supports: Julia, Python and R.
At Ubiqum Code Academy, our goal is to help students learn the digital skills they will need to advance professionally in the digital economy. We do this through our Accelerated learning programs.
Data science is an area of knowledge and professional practice that encompasses three distinct profiles but with important areas of overlap.
In the information age, data has become one of the most valuable assets for companies. However, simply collecting data is not enough; it is crucial to analyze it and transform it into useful information that can guide strategic decisions.
When we started Ubiqum in 2016, Data Science was a very new concept and today there is still some confusion. So let’s start by clarifying which professional roles have a role in the field of Data Science.
