As we all know that the demand for data science in the IT sector is at the peak. And that is the reason behind this blog to educate everyone about these demanding job roles. So let’s start with a brief introduction to data science.
The topics which we will discuss in this post include:
- What is data science?
- Importance of data science,
- Demanding job roles in data science.
What Is Data Science?
Data science refers to the process of uncovering patterns and insights hidden in huge volumes of messy data. This is done by using techniques such as:
- Data mining,
- Machine learning,
- Deep learning,
- Predictive analytics, and
- Cognitive computing, among others.
Unlike traditional business intelligence and related approaches, data science isn’t restricted to structured data. It also doesn’t require data to be organized into accurate rows and tables and isn’t limited to small data sets. Rather, data science techniques can be tested at scale to massive volumes of semi-structured and unstructured data such as text-based data, machine data, and social media data.
Importance of Data Science
- Open the value of data: Modern approaches to deal with data management, for example, Hadoop and cloud-based storage, make it more affordable than ever to store vast amounts of data. But storing data doesn’t offer value in and of itself. Applying data science opens the value of data by revealing actionable insights.
- Continuous learning: Data science is definitely not an irregular event. As data science-driven insights are put into response, the conclusion of those actions is fed back into the system of predictive models and sets of calculations. The outcome is a self-learning system that is continuously improving.
- Be predictive and proactive: By predicting the likelihood of events happening before they happen, data science allows organizations to be proactive and take actions to optimize results rather than being reactive to events after the fact.
Data science applies to all organizations: Data science has applications across virtually all industries. Farmers use data science to determine the best circumstances to plant crops. Retailers use it to customize offers to clients. Industrial companies adopt data science to prevent equipment malfunctions. From financial services and insurance to healthcare and energy, each industry is being developed and changed by data science.
Demanding Job Roles in Data Science
However, there is a wide range of different jobs and roles in the data science field to choose from. Here is a comprehensive list:
Data engineers build and test scalable Big Data biological systems for business organizations. So, the data scientists can run their calculations on the data systems that are stable and highly optimized. Data engineers also update the existing systems with newer or upgraded versions of present technologies to improve the efficiency of the databases.
The job profile of a database administrator is quite clear. They are responsible for the proper functioning of all the databases of an enterprise. Their role also includes granting or revoking its services to the employees of the company depending on their requirements. Furthermore, they are also responsible for database backups and recoveries.
Data analysts are answerable for a variety of tasks including visualization, munging, and processing of massive amounts of data. They also have to perform queries on the databases every once in a while and one of the most important skills of a data analyst is optimization. This is because they have to create and modify calculations that can be used to extract information from some of the biggest databases without corrupting data.
Data scientists have to understand the challenges of the business and offer the best solutions using data analysis and data processing. For example, they are expected to perform predictive analysis and run a fine-toothed comb through “unstructured/disorganized” data to offer actionable insights. They can also do this by identifying trends and patterns that can help the company in better decisions making.
Machine Learning Engineer
Machine learning engineers are so high in demand today. However, the job profile comes with its difficulties. Apart from having in-depth knowledge of some of the most powerful technologies such as SQL, REST APIs, and so on. Machine-learning engineers are also expected to know A/B testing, build data pipelines, and implement common machine learning calculations such as classification, clustering, etc.
A statistician, as the name suggests, has a sound understanding of statistical theories and data organization. Not only do they extract and offer valuable insights from the data clusters, but they also help create new strategies for the engineers to apply.
The designation of business analysts is slightly different than other data science jobs. While they do have a good understanding of how data-oriented technologies work and how to control large volumes of data, they also separate the high-value data from the low-value data. They are the ones who decide how Big data actions link to business insights and help in business growth.
A data architect creates the blueprints for data management so that the databases can be effectively integrated, centralized, and protected with the best safety measures. They also ensure that the data engineers have the best tools and systems to work with.
Wrapping Things Up
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