Last updated on March 4th, 2023 at 02:44 am
When it comes to data, there are two main areas of focus: data analysis and data science. Data analysis is the process of examining data in order to draw conclusions about it. Data science, on the other hand, is the process of extracting knowledge from data in order to solve problems. So which one should you be focusing on? In this blog post, we will discuss the difference between data analysis and data science, and provide a practical guide for deciding which one is right for you!
Data Analysis vs Data Science: The Differences
What is Data Analysis?
Data analysis is the process of examining data in order to draw conclusions about it. This can be done through a variety of methods, including statistical analysis, data mining, and machine learning. Data analysts use data to answer questions, solve problems, and make decisions.
There are four types of data analytics:
Descriptive analytics is the process of sorting and categorizing data in order to gain a better understanding of a particular event. This can be done through various business intelligence tools, which help to summarize the data.
Diagnostic analytics helps you understand why a current event has taken place by considering past performances. By the end of this process, you will have an analytical dashboard that tracks your progress.
Predictive analytics is about making predictions of possible outcomes.
Prescriptive analytics is a branch of data science that uses predictive models to identify the best course of action.
What is Data Science?
Data science is the process of extracting knowledge from data in order to solve problems. Data scientists use data to find patterns, trends, and insights that can be used to improve decision making. Data science combines statistics, computer science, and domain expertise to extract actionable insights from data.
There are three types of data science:
Statistical data science is the process of using statistics to find patterns in data. This can be done through various data mining techniques, such as regression analysis and cluster analysis.
Computer science data science is the process of using computer science algorithms to find patterns in data. This can be done through machine learning, which is a type of artificial intelligence that helps computers learn from data.
Domain-specific data science is the process of using domain expertise to find patterns in data. This can be done by analyzing data from a particular industry or field, such as healthcare or finance.
The key functions of a data scientist include the following:
Data wrangling is the process of making data more usable by cleaning and organizing it.
Statistical modeling entails feeding data through various models—e.g., regression, classification, and clustering models—to discern connections among variables and glean learning from the figures.
Programming is the process of creating code in various languages like R, Python, and SQL to streamline analyzing big data sets.
Unless you are a data scientist that is specifically hired to do these things, it is unlikely that you will need to perform any of these duties in your job. However, data science still holds value for business professionals. If you familiarize yourself with the concepts, terminology, and techniques used by data scientists on your team, you will be able to better communicate with them. Additionally, this gives you a firmer understanding of what insights are possible or not possible to glean from the data.
So which one should you be focusing on? What Is the Difference?
Although people often use the terms interchangeably, data science and big data analytics are two distinctive fields. The primary distinction between the two is scope. Data science encompasses a group of disciplines that scholars use to study vast datasets. Conversely, data analytics software provides a more concentrated analysis of this same information and can even be considered part of the larger process. Analytics teams focus on developing actionable insights that decision-makers can implement immediately based off pre-existing queries.
Data science differs from data analysis in that data science doesn’t stick to answering specific queries. Data scientists will parse through big, unstructured datasets to try and expose hidden insights. On the other hand, data analysis works better when it is focused on having questions in mind that need answers based on existing data. Consequently, data science produces broader insights while big data analytics focuses more on discovering answers to specific questions.
The focus of data science is on establishing trends and discovering better ways to model data, rather than finding specific answers.
It’s crucial to stop thinking of data science and data analytics as two separate entities. Instead, we should view them as integral parts of a whole system that is essential to comprehending not only the information we have but also how to scrutinize and assess it more effectively.
Dani Lehmer is the Founder and Head Honcho of Dani Digs In.
She is a Quality Assurance Analyst and blogger whose natural curiosity allows her
to dig in (pun intended) to help people build their businesses and satiate curiosity
in regard to data science, analysis, and crypto.