{"id":2389,"date":"2023-11-15T21:13:00","date_gmt":"2023-11-16T02:13:00","guid":{"rendered":"https:\/\/blogarchive.utc.edu\/business\/?p=2389"},"modified":"2024-01-26T13:39:36","modified_gmt":"2024-01-26T18:39:36","slug":"data-analytics-skills-a-data-science-professional-needs","status":"publish","type":"post","link":"https:\/\/blogarchive.utc.edu\/business\/2023\/11\/15\/data-analytics-skills-a-data-science-professional-needs\/","title":{"rendered":"Technical Expertise: The Hard Skills Every\u00a0Data Science\u00a0Professional Needs"},"content":{"rendered":"
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A degree in data analytics can provide you with a number of in-demand technical skills.<\/figcaption><\/figure><\/div>\n\n\n

Data drives our modern world. It is responsible for all the digital conveniences we take for granted, not to mention significant advancements in healthcare, logistics, and even higher education.<\/p>\n\n\n\n

Technology’s powers are far-reaching these days, but even the most advanced digital offerings still call for human expertise. These analytical brains are passionate about all things data science. Their most impressive accomplishments can be credited, in part, to their mastery of technical abilities known as hard skills. These form the basis of data science \u2014 an important and rapidly growing industry with major implications for our digital future.<\/p>\n\n\n\n

Currently, the most influential data scientists boast a unique blend of hard and soft skills, including a range of in-demand technical abilities that are highly sought after by top employers. These skills take a lot of time and effort to develop, but the rewards are certainly compelling: better job opportunities, personal satisfaction, and the chance to make a discernible difference while helping find solutions to today’s most pressing problems.<\/p>\n\n\n\n

If these opportunities sound intriguing, you could have an exciting future ahead in data science. First, however, you will want to prepare for this journey by pursuing a data analytics master’s degree. This program could help you develop an impressive technical skill set<\/a>, as explained below. <\/p>\n\n\n\n

The Importance of Technical Skills in Data Science<\/h2>\n\n\n\n

Data science is, at its core, a technical field. Yes, curiosity and critical thinking are vital, but these soft skills must be underscored by a strong foundation that encompasses modeling techniques, statistical methods, and programming languages.<\/p>\n\n\n\n

Technical skills allow data scientists to understand and leverage the inner workings of the most impactful models and algorithms of today. Without these skills, little can be accomplished \u2014 communication and teamwork, for example, are less meaningful if there is nothing of substance to communicate and no tangible contributions to make to the data science team. <\/p>\n\n\n\n

Core Data Science Skills<\/h2>\n\n\n\n

Opportunities to build data science skills are constantly evolving, right alongside the quickly changing digital landscape. The skills that are most relevant today may no longer be as influential in a few short years. Still, it’s worthwhile to develop a comprehensive skill set that includes the most key and current competencies. Remember, a strong foundation will make it easier to efficiently adapt and acquire new skills in the future.<\/p>\n\n\n\n

Not sure where to start? The following are among the most essential skills required for a data scientist \u2014 ones that can be obtained while pursuing your Master of Science in Data Analytics (MSDA)<\/a>.<\/p>\n\n\n\n

Programming Languages<\/h3>\n\n\n\n

Programming languages form the basis of the modern digital world. These allow us to translate human language and directions into highly practical binary code. This, in turn, helps humans communicate with a variety of machines and devices while also making better sense of complex data.<\/p>\n\n\n\n

Although programming languages are a must, determining which languages to master can be a bit trickier. While pursuing your MSDA, you could expect a basic overview of multiple languages but within the context of data analytics. From this perspective, programming languages most worth studying include: <\/p>\n\n\n\n

Python<\/h4>\n\n\n\n

A high-level programming language, Python is a modern go-to for building websites and software. Part of its appeal lies in the English-like nature of its syntax, which makes it far easier to learn than many other programming languages.<\/p>\n\n\n\n

This language, above all others, is a must for data analytics, as it is widely used to clean and manipulate data. Open-source and well-supported, it boasts a sizable and highly engaged community of enthusiasts \u2014 and it’s uniquely well-suited to handling repetitive tasks. <\/p>\n\n\n\n

R<\/h4>\n\n\n\n

Emphasizing graphics and statistical computing, R is a lesser-known (but still important) programming language heavily relied upon for data analytics. Quickly expanding in reach, R is expected to become more dominant in the next few years due to its suitability for machine learning. Though it is similar in many respects to Python, R is worth learning in addition to (and not in lieu of) Python because it has a greater focus on statistical project components. <\/p>\n\n\n\n

SQL (Structured Query Language)<\/h4>\n\n\n\n

Featuring a user-friendly syntax and a powerful mathematical foundation, SQL makes it possible to efficiently process huge volumes of data. What’s more, it is highly scalable and offers excellent community support.<\/p>\n\n\n\n

In data analytics, SQL is typically used to retrieve or update select data from databases. It can also be used to filter data or complete calculations using diverse data sets. <\/p>\n\n\n\n

Other Relevant Languages<\/h4>\n\n\n\n

There are hundreds of programming languages worth learning, but aspiring data scientists only have so much time and attention to dedicate to these. Still, expanding this skill set is important, and a diverse command of programming languages provides a far better toolset for tackling complex problems in data analytics.<\/p>\n\n\n\n

The best languages to learn will largely depend on personal preferences and career plans, but options worth considering include:<\/p>\n\n\n\n