How Are Data analysis and Data science Different From Each Other

With possibly everything that one can think of which revolves around data, the need for people who can transform data into a manner that helps in making the best of the available data is at its peak. This brings our attention to two major aspects of data – data science and data analysis. Many tend to get confused between the two and often misuse one in place of the other. In reality, they are different from each other in a couple of aspects. Read on to find how data analysis and data science are different from each other.

Before jumping straight into the differences between the two, it is critical to understand the commonalities between data analysis and data science. First things first – both these areas revolve primarily around data. Next, the prime objective of both of them remains the same – to meet the business objective and aid in the decision-making ability. Also, both these fields demand the person be well acquainted with the business problems, market size, opportunities, risks and a rough idea of what could be the possible solutions.

Now, addressing the main topic of interest – how are data analysis and data science different from each other.

As far as data science is concerned, it is nothing but drawing actionable insights from raw data. Data science has most of the work done in these three areas –

• Building/collecting data
• Cleaning/filtering data
• Organizing data

Post this; the data is now transformed in a manner that the data scientists could draw meaningful insights from it. When data scientists are entrusted with the above tasks, they accomplish all of this by creating and leveraging algorithms, statistical models, and coming up with their own custom analysis. Well, the work of a data scientist is not just limited to this. There are other areas as well that a data scientist is expected to address. Some of them are –

• Making sure that the decisions taken are backed by solid research and numbers.
• Dealing with the mistakes or issues that might pop up in the middle of the analysis.
• Run experiments and gather sufficient information to be able to address the business problem.

In a nutshell, data science delves deeper into an unknown world to be able to have sound knowledge of new patterns and trends that might emerge in the long run. This is why data scientists spend most of their time designing tools, automation systems and data frameworks.

Talking about data analysis, it majorly revolves around answering specific questions about the organization's business. The work role of a data analyst is to process and perform statistical analysis of existing datasets. The reason why existing data is targeted here is for the sole reason that the objective here is to address the current business problems. Data analysis has a lot to with historical data as well. This historical data is taken into account to work on the existing business problems. With data analysis, the organization is in a position to work on its goals, strengths, and strategies in a much better manner.

While data science concentrates on which questions should be asked, data analysis focuses on discovering answers to questions being asked.

With data being the foundation of both these fields, they are equally important for the organization to grow. To conclude, the main objective of data science is to ask the right questions whereas, in the case of data analysis, the aim is to find actionable data.