What is The Difference Between Data Analysis and Data Science?

Following the current technological transformations within the economy, there has been an emergence of enormous career options, wherein, Data Science is the hottest. According to the Glassdoor, Data Science arose as the highest-paid area. On the other hand, there is a significant field that has been gazing attention for years, i.e., Data Analysis. Both the Data Science and Data Analysis is often confused by the individuals.

However, the terms are incredibly different in accordance with their job roles and the contribution they do to the businesses. But, are these the only factors that make these two distinct from each other? Well, to know more we need to take a look below:

Also Read: Top 5 Data Science Trends in 2018

Data Analysis Data Science:

Data Analysis is referred to as the process of accumulating the data and then analyzing it to persuade the decision making for the business. The analysis is undertaken with a business goal and impact the strategies. Whereas, Data Science is a much broader concept where a set of tools and techniques are implied to extract the insights from the data. It involves several aspects of mathematics, statistics, scientific methods, etc. to drive the essential analysis of data


The individuals misinterpret Data Analysis with Data Science, but the methodologies for both are diverse. The skillset for the two are distinct as well. The fundamental skills required for Data Analysis are Data Visualisation, HIVE, and PIG, Communication Skills, Mathematics, In-Depth understanding of R and Python and Statistics. On the other hand, the Data Science embed the skills like – Machine Learning, Analytical Skills, Database Coding, SAS/R, understanding of Bayesian Networks and Hive



Though the areas – Data Analysis and Data Science, are often confused about being similar, but the methodology is different for both. The methods used in the two are diverse. The essential techniques used in Data Analysis are – Data Mining, Regression, Network Analysis, Simulation, Time Series Analysis, Genetic Algorithms and so on. While, the Data Science involves – Split Testing, categorizing the issues, cluster analysis and so on


Just like the areas are different, so are their goals. The Data analysis is basically about answering the questions generated, for the betterment of the businesses. While Data Science is concerned with shaping the questions followed by answering The Data science, as illustrated above, is a more profound concept

The era of Artificial Intelligence and Machine Learning is shaping the economy in a much more comprehensive aspect. The organizations are moving towards a data-driven decision-making process. The data is becoming imperative in functioning and is not limited to the Information Technology organizations.

It is soon taking over the industries like – Sports, Medicine, Hospitality, etc. Such technological advancements have led to a rise in job opportunities in the area of Data Science and Analysis. The merely significant facet which needs to be taken into consideration is the understanding of the difference between the two. Big Data is the future which is expected to lay a considerable impact on the operations of both industries and routine life.

Related Article: What a Data Scientist Could Do?

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