The relationship between data science and machine learning

As technology advances by leaps and bounds, especially in the current digital transformation, it has become vital for companies to use data science to analyze information and generate predictive models that help them face and overcome each new challenge in their management.


In order to provide such highly accurate predictions and information, this science makes use of various disciplines and tools, such as machine learning. In this way, it builds high-quality models for solving various tasks.

But in this interaction between data science and machine learning, it is common for confusions to arise and for their concepts to be mixed up. Therefore, it is important to understand their definitions and the close relationship they maintain.

 

 

What is data science?

 

Data science is the field that studies and scientifically analyzes data to detect certain patterns and obtain accurate information to help solve problems and guide decision-making in various sectors, such as business or science.

The study performed by data science comprises the phase of extracting these data, their transformation, analysis, and the subsequent prediction. Consequently, it uses various disciplines, such as computer science, statistics, and mathematics, as well as complex machine learning algorithms.

 

 

Data science vs big data: differences and similarities

 

Data science, in order to subtract knowledge from a set of information, can deal with small volumes or a large quantity of data. However, it is necessary not to confuse this science with big data.

Some of the main differences between data science and big data are:

  • How they are defined:
    -Big data manages large volumes of data.
    -Data science performs a scientific analysis of data.

  • What they do:
    -Big data stores large volumes of data to generate information.
    -Data science analyzes data, but using different operations, through different tools such as big data.

  • What technological tools they use:
    -Big data uses platforms such as Hadoop, Spark, and Flink.
    -Data science uses languages such as R, SAS, and Python.

  • What the professional is responsible for:
    -Big data specialists develop and manage clusters with a large volume of data.
    -Data scientists analyze, extract information, and generate a solid result from the data.

Despite these differences, there are also similarities between these two terms. As expressed above, data science involves a series of processes or operations, which also includes big data.

Big data can be thought of as one of the subsets that make up the whole of data science, therefore, their main similarity is that both areas process and manage data.

Therefore, a data scientist must possess the knowledge and skills to work with the technology associated with big data.

 

Application of data science

Data science is widely used and appreciated in the business sector to establish future scenarios and make sound management decisions, such as those made within the sales area, in the recognition of advertising images and in a risk analysis.

In the industrial or business sector, data science and its analytics represent a perfect ally that helps you optimize your business processes, understand your customer, and offer them a product that meets and exceeds their expectations, as it is based on the predictive analysis of concrete facts and data.

Data science is also used in the medical field, both for the identification of diseases and for predicting epidemics or other consequences.

Data science and machine learning, a meeting point

 

Within the entire analysis process performed by data science, machine learning provides the techniques and tools necessary for building algorithms or models that learn by extracting meaning from the data studied.
What is their meeting point? Data.

Data science involves the entire data processing methodology: it is a multidisciplinary field involving data analytics, predictive analytics, software engineering, data engineering, and machine learning, among other disciplines.

On the other hand, machine learning is an area of artificial intelligence using advanced algorithms that, based on statistical and predictive analytics, extract data, learn from these data, and generate forecasts about future trends, according to the subject matter they are dealing with.

 

Pangeanic, an expert in data science

At Pangeanic, we specialize in professional and machine translation services through the application of artificial intelligence technology and data science in specialized sectors such as finance, economics, science, and law.

We are experts in handling and classifying large amounts of data, using the various analytical disciplines of data science to store, manage, and clean the necessary information.

In this way, we are able to provide companies and other organizations with the necessary tools to increase their decision-making capacity, without language being a limiting factor.


At Pangeanic, as leaders in the development of artificial intelligence technology, we apply the best practices of data science, providing you with a cognitive, adaptive, and almost human-quality machine translation.

 

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