Team Oct 13, 2022 No Comments
Data science and data analytics are the trending words of this decade. For people who are looking for a long-term career option, data science and big data jobs have been a safe bet. This trend is likely to continue but before you think further about this career option, you should know about the difference between data analytics and data science. Let us begin with the comparison of data science vs analytics.
Presently, data is the primary object for the swift operation of any business to gather important insights and enhance business performance to evolve in the market. So without any further delay, let us begin with the difference between data science and data analytics with example.
Before we get into data science vs analytics, let us gather some ideas about these topics separately. Data analytics aims at performing and processing statistical analysis of prevailing datasets. Analytics emphasizes formulating methods to process, capture, and organize data to unravel actionable insights for present issues and formulating ideal ways to offer this data. More conveniently, the area of data and analytics is directed towards solving issues for questions we know we don’t know the answers to. More crucially, it is based on producing outcomes that can lead to instant enhancements.
Data analytics also revolves around a few varied branches of broader statistics and analysis which help to mix diverse sources of data and locate connections while easing the outcomes.
Data science is a multiskilled field aimed at finding actionable insights from big sets of structured and raw data. The field basically fixates on unraveling answers to the things we are unaware of. Data science professionals use many varied techniques to get answers, including computer science, predictive analytics, machine learning, and statistics to examine huge datasets in an attempt to establish solutions that haven’t been thought of yet.
The primary aim of a data scientist is to ask questions and find out potential routes of study, with less concern for prominent answers and more focus placed on finding the appropriate question to ask. Experts attain this by anticipating potential trends, exploring disconnected and disparate data sources, and finding better avenues to analyze data.
While there are many who use these terms interchangeably, big data and data science are unique fields, with the major variation being the scope. This is an umbrella term for a group of fields that are used for mining large datasets. Data analytics software is a more niched version of this and can even be considered part of the bigger process. Analytics is inclined to identify actionable insights that can be levied immediately based on prevailing queries.
Another prominent difference between the two areas is the question of exploration. Data science isn’t concerned with answering major queries, instead analyzing via huge datasets in sometimes unstructured ways to expose insights. Data analysis operates better when it is aimed, at having questions in the mind that require answers based on the prevailing data. Data science offers wider insight that focuses on which questions should be asked, while big data analytics focuses on discovering answers to questions being asked.
More crucially, data science is more worried about asking questions in comparison to finding specific answers. The niche is aimed at creating potential trends based on prevailing data along with realising better ways to evaluate and model data.
| Data Science | Data Analytics | |
|---|---|---|
| Scope | Macro | Micro |
| Goal | To ask the right questions | To find actionable data |
| Major Fields | Machine learning, AI, search engine engineering, corporate analytics | Healthcare, gaming, travel, industries with immediate data needs |
| Using Big Data | Yes | Yes |
The two fields can be considered varied sides of the same coin, and their operations are highly interconnected. Data science lays the foundations and analyses big datasets to formulate initial observations, potential insights, and future trends that can be crucial. This data by itself is useful for some niche, especially modelling, enhancing machine learning, and improving AI algorithms as it can enhance how data is evaluated and sorted. However, data science asks crucial questions that we didn’t know about before while offering little in the way of hard answers. By adding data analytics into the mix, we can turn those things we know we don’t know into actionable insights along with practical applications.
When considering two disciplines, it is crucial to forget about seeing them as data science vs analytics. Instead, we should see them as parts of a complete system that are crucial to evaluating not just the data we have, but how to better evaluate and analyse it.
As stated above, the career prospects for data science and data analytics are huge and if you want to land your career in the same, then it is important that you get a certification from a reputed institution. The best we can think of is Ivy Professional School. They offer specially curated courses taught by industry experts.