Data science is an interdisciplinary discipline that integrates mathematics, statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning. Its goal is to uncover practical information hidden in an organization’s data.
Although the term “data science” is not new, its meaning has evolved. It emerged in the 1960s as an alternative name for statistics and was formalized by computer professionals in the late 1990s. It wasn’t until a decade later that the term was adopted outside of academia.
Data science is crucial in combining tools, methods, and technology to give meaning to data in modern organizations, which are inundated with the proliferation of online devices and systems. It offers the ability to capture large amounts of data of different types (text, audio, video, images), providing opportunities for analysis and application in fields such as e-commerce, medicine, finance, and other aspects of human life.
The data science lifecycle begins with the collection of data, both structured and unstructured, from a variety of sources and using a variety of methods. These can include manual entry, web scraping, and real-time data from systems and devices. Sources such as customer data, log files, video, audio, IoT and social networks are common.
Due to the diversity of data formats and structures, companies must consider different storage systems depending on the type of data. Data cleansing, transformation, and merging using ETL or other technologies is essential before loading into data warehouses or repositories.
Data scientists perform exploratory analysis to examine biases, patterns, and value distributions. This allows them to generate hypotheses for A/B testing and determine the relevance of data for modeling and predictive analytics.
Valuable information is presented through understandable reports and data visualizations, making it easy for analysts and decision-makers to understand and understand its impact on the business.
A data scientist must be business savvy, apply statistics and computer science to data analysis, use various tools and techniques to prepare and extract data, as well as extract valuable insights from big data through predictive analytics and AI.
Artificial intelligence and machine learning have streamlined data processing. The growing demand has generated a wide supply of training and jobs in this field, which promises sustained growth in the coming decades.
Data science is broader and encompasses the entire data process, while data analytics focuses primarily on statistical analysis.
Although they overlap, data science focuses on using technology to work with business data, while business analytics is broader and does not focus on technology.
Data engineers create and maintain data systems, while data scientists use the data processed by engineers to analyze and create models.
Data science is broader, using scientific methods, processes, and systems to extract knowledge from data in general, while statistics focuses on collecting and interpreting quantitative data.
Machine learning is a method used in data science projects to obtain automated information from data.
This type of analysis examines data to gain insight into what has occurred in the data environment, using visualizations and tables to reveal patterns.
Drills down into the data to understand why certain events occurred, using detailed analysis techniques, data discovery, and correlations.
Uses historical data to predict future patterns, employing machine learning, forecasting, and predictive modeling techniques.
Goes beyond prediction, suggesting optimal responses to certain outcomes. Uses techniques such as graph analysis, simulation, complex event processing, and machine learning.
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