Data science is the area of study which is the convergence of strong business acumen, technology skills, and mathematical expertise. These elements build the foundation of data science and need a deep understanding of concepts under each domain. In this blog, you will walk through the skills you require if you are aiming to become a data scientist. Let’s dive into it:
If you are thinking that data analysis is all about statistics then there is a misconception. Undoubtedly, both classical and Bayesian statistics are very significant to data science, but other concepts are also important like quantitative techniques and particularly linear algebra that is the support system for many machine learning algorithms and inferential techniques.
Well-Built Business Acumen
Data scientists are the source of obtaining useful information that is significant to the business and is also accountable for sharing this knowledge with the concerned individuals and teams to be applied in business solutions. They are significantly positioned to participate in the business strategy as they have exposure to data like no one else. Therefore, data scientist should have the well-built business acumen that can fulfill their accountabilities.
Data scientists are supposed to work with compound algorithms and sophisticated tools. Also, they are required to prototype and code instant solutions using one or a combination of languages from Python, R, SAS, and SQL and sometimes Julia, Scala, Java, and so on. They should be able to navigate their way via technical challenges that might arise and prevent any bottlenecks or roadblocks that can occur because of a lack of technical soundness.
The Roles In The Field Of Data Science
Until now, we have discussed data science and what should be the critical skillset to become a data scientist. Now let’s discuss the job roles of data analysts in the market:
The role of data analyst serves as the bridge between the data scientist and business analyst. They operate on particular questions and find results by arranging and analyzing the given data. They transform technical analysis into action items. After having the results, communicating with the stakeholders is also part of the job responsibility. They do not only require the skills of mathematical and programming but also data visualization and data wrangling skills.
The prime job responsibilities of the data engineer are to manage large amounts of instantly changing data. They manage infrastructure and data pipelines to transform and transfer data to respective data scientists to work on. They frequently use Cassandra DB, Apache Hadoop, and Scala.
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