Data science and engineering

The three profiles that make up Data Science

Data science is an area of knowledge and professional practice that encompasses three distinct profiles but with important areas of overlap:

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Data Engineering.

Machine learning algorithms engineering (Machine Learning Engineering).

And Data Analysis (Data Analyst and Business Data Analyst).

To better understand how these disciplines complement and differentiate each other, we will explore their definitions, roles, tools and their impact on the business world.

Definitions and Objectives

Data Engineering: focuses on the creation and maintenance of systems that enable the collection, storage, and processing of large volumes of data. Data engineers design and build the necessary infrastructure to ensure that data is accessible, reliable, secure, and scalable.

Machine Learning Engineer: This is the profession of machine learning algorithm builders (developers). They are people with a strong background in software engineering and extensive knowledge in mathematics. We can say that it is a software engineer on steroids.

Data Analyst: Data analytics is a discipline that combines statistics, programming, and domain-specific knowledge to extract useful information and insights from data. Its main objectives include creating predictive models, discovering patterns and trends, and generating actionable insights that can guide decision making.

Work Process.

Each of the profiles has different work processes.

Data Engineering:

Data Architecture Design: Data infrastructure planning and design.
Development of Data Pipelines: Creation of automated workflows for data collection, processing and storage.
Database Management: Maintenance and optimization of storage systems.
Data Quality Assurance: Implementation of processes to ensure data integrity and accuracy.
Scalability and Security: Ensure that data systems are scalable and protected against threats.

Data analysis:

Problem formulation. The starting point of any analysis is the formulation of a problem or hypothesis to be addressed through data analysis.
Creation of a data set: The second step, closely related to the previous one, is the collection of data for the problem or hypothesis we want to work on.
Data Preprocessing: Data cleaning and transformation to ensure data quality.
Exploratory Analysis: Exploration of data to identify patterns, trends and relationships.
Modeling: Development and validation of predictive models using machine learning techniques.
Interpretation and Communication: Translation of results into actionable insights and communication of these to stakeholders.

Data analytics and data engineering are interdependent and work closely together to achieve effective results. Without a robust infrastructure created by data engineers, data analysts would not have access to reliable and scalable data for their analysis. On the other hand, without the analysis and insights provided by data analysts, the data infrastructure would lack purpose and direction.

What to learn to become a data analyst

At Ubiqum we offer three programs focused on three different student profiles. In each of them the student gets a solid foundation in Python programming and in the use of the libraries mentioned above.

Data Analysis and Machine Learning Courses

Tools Used in Data Science

The three profiles that make up Data Science use a variety of tools and technologies, many of which overlap. We describe some of the most common tools in each discipline.

Python: Versatile programming language with numerous libraries for data analysis, machine learning and visualization.
R: Programming language and environment for statistical analysis and data visualization.
Jupyter Notebooks: Interactive environment for creating and sharing documents containing live code, equations, visualizations and explanatory text.
Power BI: Data visualization tools that allow the creation of interactive dashboards and the sharing of insights.
SQL: Structured query language used to manage and manipulate relational databases.
Apache Hadoop: Framework for storing and processing large volumes of distributed data.
Amazon web services, Google Cloud Storage, Microsoft Azure Blob Storage: Cloud storage services that offer scalable and secure solutions for data management.

The relationship between data science and data engineering is critical to success in the field of data analytics. By integrating both disciplines, organizations can not only gain valuable insights, but also build the infrastructure necessary to handle large volumes of data efficiently and securely. This not only improves decision making and operational efficiency, but also drives innovation and enhances the customer experience, providing a sustainable competitive advantage in today’s marketplace.