Data Science vs Data Analytics: What Are The Differences?Technology
Data Science and Data AnalyticsAs we are moving into the digital world, we begin to work more closely with data.
Data has a vital role in decision-making and future prediction. So, data science and data analytics become two of the most important pieces of knowledge to learn.
It’s not always easy to know the differences that separate data science from data analytics. What are they?
Data analysts and data scientists represent two of the most in-demand, high-paying jobs. These roles are number one for increasing demand in many industries and companies. Followed by AI and machine learning specialists and big data specialists.
This subject relies on statistical techniques and machine learning tools to turn large sets of raw data into valuable insights.
A data scientist professional would need more knowledge about technology and machine learning algorithms. This information will help them to understand the data and make decisions out of it.
While data analyst roles require at least a bachelor’s degree in a field like mathematics and statistics, data scientists typically need to have at least a master’s or doctoral degree in data science, information technology, mathematics, or statistics.
Data analytics is a field that uses statistical techniques to uncover patterns or trends in data. Just like its name, a data analyst also conducts some basic analysis.
Understanding how to navigate programs such as Python or SQL is important to get the meaning of the data that they need to extract. But, coding isn’t generally required to do data analytics.
What They’re Doing
Using data techniques such as machine learning algorithms or design predictive modeling to handle both structured and unstructured data, data scientists have their role in predicting the future trend and make decisions based on it.
Generally considered a more advanced version of a data analyst, data scientist has a role in gathering, extracting, and processing raw data as well as designing predictive models and machine learning algorithms to mine big data sets.
Data analysts also build data visualization tools and design reports to help in the process of decision making and predicting future trends by analyzing the current data pattern.
Competency in technology can help data scientists to write programs that enable them to automate data collection and processing.
As mentioned before, data analysts typically work with structured data to analyze gathered data and solve business problems by using tools like SQL, R, or Python programming languages, data visualization software, and statistical analysis.
While a data scientist works by using advanced technology to figure out how current data affects the decision-making in the future, data analysts focus on collaborating with stakeholders to identify informational needs and how they can learn from the data.
Data analysts acquire data from primary and secondary sources. These data are important to spot patterns and trends to be translated into actionable insights.
Not only working in analyzing data, a data analyst must also be able to present their findings in an understandable way.
Along with leaders and stakeholders, they can make data-driven decisions that will benefit many.
In the end, both data science and data analytics are important knowledge to learn, especially during this digital age. The best decisions are the ones that are data-driven, so, being able to extract conclusions from data is necessary.