Data Science and Data Analytics are the two major buzzwords when referring to Big Data. Most of us tend to confuse these two or think they are similar—maybe because both of these deal with data. Despite the fact that both data science and data analytics are interconnected, there are various differences in their approach and the results produced when they optimize big data.
What is Data Science?
Data science is a versatile field of study and practice whose purpose is to discover actionable insights from large amounts of both raw and structured data. Advanced computer science and technologies such as artificial intelligence, machine learning, predictive analysis, statistics, data mining, and data inference are key tools for the data scientist. They are used to scrutinize, dissect, and parse huge sets of data assets to convert them into strategies and solutions to major problems and challenges for the business.
What is Data Analytics?
Data analysis is all about generating answers to questions to make better business decisions. Data analytics focuses on specific areas with specific goals as they capture, process, and organize historical and existing information to expose and make sense of all the pertinent data. Organizations that apply advanced analytics and algorithms can address key challenges and leverage the resulting insights to enhance products and processes.
Data Science versus Data Analytics
Many experts consider data science and data analytics two sides of a coin as they are both interconnected and contribute to understanding and managing Big Data. Data science identifies potential insights through the observations from data parsing. Data analytics takes this a step further and transforms the insights into actionable items for immediate use and better decision making. In short, data science generates the possible questions and problems that may arise in the future and data analytics is responsible to find answers and solutions to those questions.
Here is a glimpse of the differences between data science and data analytics:
|Data Science||Data Analytics|
|Responsibility||Ask the appropriate and potential questions||Determine the actionable data and derive answers|
|Major fields||Machine learning, artificial intelligence, search engine engineering, corporate analytics||Healthcare, travel, and any industry that has immediate data needs|
Now that we have discussed the nature of data science and data analytics, it is useful to understand the corresponding responsibilities of data scientists and data analysts.
Who is a Data Scientist?
The major role of any data scientist is to ask the right questions and identify potential areas of study. They predict potential trends, explore distinct and detached data assets, and prepare the right questions. Here is what is expected from a data scientist:
- Process, refine, and validate the integrity of data
- Exploratory analysis of data
- Collect business insights through algorithms and ML
- Identify the latest data trends and predictions
Who is a Data Analyst?
A data analyst is someone who is expected to have the knowledge and skillset to transform raw and historical data into usable insights using tools such as predictive analytics, prescriptive analytics, descriptive analytics, and diagnostic analytics. They extract required data from primary and secondary sources, remove corrupted data, and fix the code-related issues and errors.
- Collect and interpret data
- Identify appropriate patterns
- Data querying using SQL
- Present the extracted information
Data science and data analytics are different but correlative techniques used by organizations to uncover essential, meaningful insights from your data. When used together they can be powerful tools to improve your response to market changes and boost successful outcomes.