Two most popular careers in recent time are in data science and software development niches. There are various advantages offered by both data science and software development. If you are someone who is confused with these terms or which career to opt for, this short article will offer you a quick insight to these two popular career niches, which will help you to know them. Let’s take a quick look at both data science and software development.
A look at data science and software development:
The extraction or conversion of data in different forms is data science if it were to be described in a simple way. These data used by businesses to bring any improvement in the business and using these data, the businesses know how to convince customers to purchase any specific products. On the other hand, software development offers an approach that is structured to develop a software program by designing it. It also consists of maintaining the software after it has been deployed. The needs and requirements are made clear through software development so that it is easy for the developers to proceed.
Data is important for any sector. The advancement in the information technology sector is creating changes through its impact in the sciences. There are data everywhere and these data are growing. Because of this reason, there needs to be appropriate expertise is required to manage and analyze the huge data and get insight from it. On the other hand, software development doesn’t provide a solution similar to data science. In data science, ETL is the method to collect the data, through extraction from various sources. The data is then converted into a format that is clear to understand. It is then put into the system to get it processed. For software development, the base is known as the Software Development Life Cycle that is followed by software developers when developing a software product. Data science is data-oriented, whereas, software development is framework oriented. Some tools that are used in data science are data visualization tool, analytical tools, and database tool. For software development, tools such as database tool, programming language tool, continuous integration tool, design, and analysis tool etc.
Different skills are required for data science and software development. In software development, a developer will need to know and understand and analyze the needs and the requirements of the users and be proficient in the main programming languages, know how to use various tools and testing methods, etc. On the other hand, a data scientist will need to have knowledge of how data product can be built and visualized to make it easy to understand the data. The data scientist will also need to have the knowledge of the domain, algorithms, machine learning, structured and unstructured data, big data processing, statistics, and probability etc.
In software development, the user needs and requirements are analyzed and build the software product accordingly. Data science has a certain similarity with data mining, where the data is extracted from different sources and this can be structured or unstructured.