Technological resources arrive at an increasingly surprising speed and, sometimes, we cannot keep up with them. If you don't know what Data Science is, or even know the term, but still don't know how it works in practice, we are here to help you. Read on and check out the “Basic Guide” on Data Science in the article that we, from the CodeBlog team, have prepared for you!

Data Science: the definition

Translated as “Ciência de Dados”, the term Data Science is a set that applies statistics methods, elements of computer science and mathematics to find important information in data. This involves machine learning algorithms and structures, programming languages and visualization libraries.
In this way, the so-called “data scientists” combine the concepts of programming, mathematics and graphics to find answers to user questions and obtain valuable insights.

Data Science: the importance

In the corporate context, Data Science helps companies competitiveness and productivity.

This is because, data analysis obtained through this technique, helps organizations to identify trends and opportunities. This provides valuable insights that can positively impact business.

The great advantage of Data Science is that it is useful even in smaller datasets, unlike Artificial Intelligence, which needs large volumes of data to work.

An excellent example of this are retailers themselves, who used to base their store inventories on the number of sales of the units themselves. With the pandemic and all the consequent restrictive measures that were imposed, many businesses were temporarily closed. With this, these merchants had to look for other methods of forecasting, in accordance with changes in data availability.

This fact reinforces that Data Science is capable of using practices such as data enforcement or even generation of synthetic data and learning in tandem, to present insights in situations where only a small amount of information is made available.

Another advantage of Data Science is that it allows organizations to develop resilience. After all, in an absolutely changing world, where any process can transform instantly, it is crucial that companies know how to adapt and respond quickly to changes.

Data Science: life cycle:

Data science is a cyclical process, which follows a specific pattern or cycle. Its life cycle is divided into a few stages. They are:

Knowledge of the topic:

The first step is to understand the problem you want to solve with Data Science. To ask the right and relevant questions, it is important to have a specialized knowledge base that defines the purpose of the project.

Data acquisition:

For questions to be answered properly, data must be collected correctly. Usually, information is found in different locations and often difficult to access. At this stage, it is especially important for the data scientist to collect relevant and quality data, and prepare them for the next stages.

Data preparation:

Seen as the most time-consuming and consequently most important step of the cycle, data preparation requires that information be properly “cleansed” and previously combined.
During the process, it is common for scientists to feel the need to go back and collect more information, whether to include different information or perform treatment of missing values.

Data Exploration

Data exploration is the stage that identifies and analyzes patterns that have been integrated into the information set.
Once the data is cleaned and ready for use, it is time for scientists to understand the information and then create hypotheses to test them.
In addition, data exploration also covers reviewing different attributes of each set and analyzes that identify if other combinations or transformations of data could produce new, more meaningful resources.

Modeling and predictive evaluation:

The next step, after exploration, is the start of training predictive models, taking into account that, in many cases, these modelings can be combined with case exploration.
At this point, it is natural for the Data Scientist to realize new possibilities and resort to resource engineering. After building the models, they must be evaluated, tested and refined, until they reach their ideal standard.

Interpretation and deployment:

This stage is characterized by the interpretation of data and its results.
It is time for the Data Scientist to step in and put into practice all the models and analyzes tested during the Data Science life cycle to answer the question that aims the work.
If the result obtained is positive, the model is intended for deployment, or, in other words, used to help the team in decision-making through data.

Monitoring:

After the deployment of the model, it must be constantly checked and maintained, to ensure its correct functioning, even when it is to host new data or undergo alterations due to external factors, such as trends, behavior changes etc.

Repetition:

Because it is cyclical, this cycle constantly repeats, regardless of whether the main focus is immediate interpretation or long-term implementation. In the end, the goal of any project focused on Data Science is learning. After all, this resource is a great ally for those who wish to learn something new about a specific topic or about a problem that requires deeper answers.

Data Science: how to apply it in different sectors?

Many organizations use Data Science to optimize their products and internal processes. The good news is that every type of business, regardless of industry, can benefit from the strategy.

For example, an energy software company can use the models to recommend new or existing energy products to potential customers. Similarly, an educational institution can commit to implementing standardized tests capable of identifying which students are at risk of not graduating.

Now that you already know the benefits of Science, how about leveraging all the power of this tool to create competitive advantages for your business?

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A big embrace and see you in the next post!