One of the lucrative and in-demand professions for qualified experts is data science. Although a job in data science is fulfilling and lucrative, getting started is not that easy. It is not necessary to have a master’s or bachelor’s degree to work in data science. One requires the proper skill set and expertise.
You need to possess some hard skill sets, such as analysis, machine learning, statistics, neural networks, etc., to launch a successful career in data science. To succeed in the field, you must also be a problem-solver, a critical thinker, and a skilled storyteller. Let’s discuss the 9 best Career Options in Data Science for 2023.
1. Data Analyst
Data analysts are in charge of many different activities, such as processing enormous volumes of data and visualising data. They occasionally need to run queries against the databases. Optimization is one of a data analyst’s most crucial talents. This is because they must develop and alter algorithms that can be utilised to extract data without tainting it from some of the largest datasets.
2. Data Engineers
To enable data scientists to execute their algorithms on secure, highly optimised data platforms, data engineers develop and test scalable Big Data ecosystems for enterprises. To increase the effectiveness of the databases, data engineers also upgrade or replace older versions of the existing systems.
3. Database Administrator
A database administrator’s job description is self-explanatory; they ensure that all databases in an organisation are operating properly and granting or denying access to employees based on their needs. They are also in charge of recovering and backing up databases.
4. Data Scientist
Data scientists need to be familiar with business difficulties in order to provide the best solutions through data processing and analysis. For instance, they must conduct predictive analysis and carefully dig through “unstructured/disorganized” data to provide useful information. In order to help businesses make better judgments, they can also do this by spotting trends and patterns.
5. Data Architect
The blueprints for data management are developed by a data architect, allowing for simple database integration, centralization, and best-practice security protection. Additionally, they guarantee that the data engineers have the greatest equipment and setups.
6. Machine Learning Engineer
Engineers skilled in machine learning are in high demand nowadays. The work profile does have some difficulties, though. Machine learning engineers are expected to do A/B testing, design data pipelines, and implement popular machine learning algorithms such as classification, clustering, etc., in addition to having an in-depth understanding of some of the most powerful technologies like SQL, REST APIs, etc.
As the name suggests, a statistician is knowledgeable about statistical theories and data organisation. In addition to extracting and providing priceless insights from the data clusters, they also aid in the development of fresh approaches that the engineers might use.
8. Data and Analytics Manager
Assigning tasks to their team in accordance with abilities and knowledge, a data and analytics manager supervises the data science operations. Their areas of expertise should be managing technologies like SAS, R, SQL, etc.
9. Business Analyst
Business analysts’ responsibilities differ slightly from those of other data scientists. They distinguish between high-value and low-value data while also having a solid grasp of how data-oriented technologies operate and how to handle enormous volumes of data. In other words, they show how Big Data can be connected to useful business insights for a company’s expansion.
We at Praxis are offering all the above-mentioned were not courses but career/designations for students, and with a star-studded faculty, you will learn the best industry practices for the applied field as well as the best of the theoretical knowledge. Join us soon to make your next career move even smoother.