Data Science and Analytics are two highly related, but still distinct, fields within the area of information management. Both essentially vary in their scope, operating with different objectives, uses, means, branches, volumes and types of data.
Their similarities, however, are responsible — in large part — for the confusion between terms and even for the “mixing” of concepts in the minds of those who are less familiar with the segment. However, as digital transformation has meant that these activities are no longer just a trend for the future and are now representing a current need in the corporate world, it is essential to understand them.
So, continue reading the post and find out what each one involves to know how to differentiate them! Let’s go?
What is Data Science?
Before directly addressing the differences between Data Science and Analytics, it is necessary to know how to define these two terms. Translated as “Data Science“, the first is linked to a more technical area, requiring in-depth knowledge of programming for its operation.
As it conducts more complex and broader processes, its objectives are strategic. In practice, this includes designing, developing, and implementing high-capacity models to:
- establish new forms of collection;
- organize the elements;
- find patterns;
- to perceive Tendencies;
- extract information;
- predict events;
- interpret your indications.
What is Analytics?
More contained — one might say —, Data Analytics seeks answers to questions with practical and limited impacts. In this case, known standards, techniques or tools are applied to formulate new correlations, operationalizing the implementation processes of existing models.
Regarding the interpretation of the information extracted, its focus is on what the data indicates about the scope of the analysis. The same applies to the foundations behind this, which are capable of generating some security regarding the conclusions obtained.
What are the differences between Data Science and Analytics?
Even in the conceptual field, Data Science and Analytics tend to mix, generating occasional confusion. After all, in practice, these fields are related, as stated. Therefore, we list the main differences between them below. Look!
Scope and indication of use
Data Science is more comprehensive, not limited to one scope when approaching the analyzed sources. In this way, it is possible to affirm that Data Science “listens” to what the data has to say, building means to better understand what they indicate and understand what there is to discover in these sets and/or from them.
In the same analogy, Data Analytics asks specific questions and applies the usual means indicated to obtain precise answers to these questions. In other words, it is more limited because its focus has been previously defined.
Thus, the first uses the past to predict the future. The second focuses on what is available in front of you, so that your conclusions are factual, but always expanding the scope and inferring new perspectives in relation to them.
Taking bases linked to time and climate as examples, the Data Science area can create a model for projection, using a greater number of variables. In this context, the purpose is to identify what the meteorological trends will be in the future.
In turn, Analytics is restricted to concluding what and when the highest incidences of certain types of weather events were. Furthermore, it is capable of estimating the chances of phenomena recurring.
Data types and volume
Using this comparison criterion, differentiating Data Science and Analytics becomes much easier. Data Science works on data that is unstructured and has not yet been processed, covering a large volume that reaches terabytes.
The other uses analytical tools on structured but raw data, the scope of which is adequately defined because it has been organized and cleaned. In other words, they were already limited to the area — marketing, commercial, etc. — in which the study will be conducted. In this case, the quantity is smaller, with its maximum being around 500 thousand lines.
Technology and methods used
Both activities are based on and use both Mathematics and Statistics. Furthermore, logic is essential knowledge for the operation of both, which also involve SQL databases — and it is common for them to use some languages, such as Python or R.
However, Data Analytics uses information collected in Big Data or Business Intelligence solutions to make inferences and allow the visualization of elements through programs. Excel, Power BI, Data Studio and Tableau are good examples.
Differently, Data Science involves more complex languages, such as Java, when programming algorithms. It also uses Machine Learning and Artificial Intelligence to create predictive models and data flows or to automate correlation processes.
Business areas in which they are applied
We live in the era of digital transformation and, in the corporate world, these two fields differ by the type of enterprise in which they are implemented. In practice, both present different functions as the limits of their scope and scope are applied.
Data Science is present in online retailers, streaming services and other content distributors, discovering insights linked to the public’s consumption behavior to make more specific recommendations. Financial institutions take advantage of their predictive capacity to foresee risks and fraud.
On the other hand, Analytics is seen in the areas of healthcare, hospitality, energy, accounting and HR (Human Resources). After all, it is aimed at finding ways to increase efficiency and requires less predictability by acting more on planning.
What are the similarities between these two fields?
Data Science and Analytics are essential fields in companies in which having a culture Data Driven really creates a competitive advantage. Therefore, the first aspect in common between them is the adoption of the bases of data management, including knowledge related to Mathematics, Statistics, Logic and Computing.
In relation to objectives, despite using different approaches and having other complementary applications, both serve to support corporate decision-making. Because each person — within their scope — extracts information from data to create a realistic image, varying the point of view from which observations are made.
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