The world is using data like never, and the terms Data Science and Data analytics are used almost interchangeably today. In fact, many people think that a Data Scientist is just a fancy name for a Data Analyst. However, while they do sound similar and both deal with big data sets, they are inherently different. Let us first define each one and then look a little deeper into the similarities and differences between Data Science and Data Analytics.
To put data generation in today’s world into perspective, let us look at the following graph, which depicts how much data was generated every minute in 2018.
Data Science and analytics is a multidisciplinary field which has a much broader scope when dealing with data science and business analytics, where several techniques and tools are used to extract insights from data. In most cases, data science and business analytics is used to scope out the right questions from the data set. It works at the raw level of data (structured, unstructured, or a combination of both) to build data models, to create more efficient machine learning algorithms, make predictions, and identify patterns and trends.
Some of the tools and techniques involved are clustering analysis, anomaly detection, association analysis, regression analysis, and classification analysis. data science and business analytics works in the realm of the unknown, trying to find new insights and relationships in big data.
Data Analysis is a subset of big data and data science . It can be defined as the process of applying statistical, logical, and analytical techniques to data sets to discover information that helps in making informed decisions. A data analyst can use several tools like visualizations, Business Intelligence (BI), data mining, and textual data analysis.
The information gleaned from data analysis is highly dependent on the quality of the data. Data analysis merely curates’ meaningful insights from past data but is generally not used for predictions. It is typically driven by business goals.
Both work with big data and data science to get better outcomes for business or society.
Both require a background Mathematics, statistical and programming skills (Hadoop, R, SAS, SQL, and Python). A Data Scientist should also be well versed with the Business.
While differences do exist between data science and data Analytics , together they form the future of our data driven world. Be it for business, personal, social, medical, or naturally occurring phenomena, embracing these technologies will make a significant difference in our lives. Their contributions have already started being felt in our daily lives and further advancement in areas like machine learning and artificial intelligence should truly prove to be of great use.
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