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.
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 — an important and rapidly growing industry with major implications for our digital future.
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.
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, as explained below.
The Importance of Technical Skills in Data Science
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.
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 — 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.
Core Data Science Skills
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.
Not sure where to start? The following are among the most essential skills required for a data scientist — ones that can be obtained while pursuing your Master of Science in Data Analytics (MSDA).
Programming Languages
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.
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:
Python
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.
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 — and it’s uniquely well-suited to handling repetitive tasks.
R
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.
SQL (Structured Query Language)
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.
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.
Other Relevant Languages
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.
The best languages to learn will largely depend on personal preferences and career plans, but options worth considering include:
- Java
- C++
- Ruby
- PHP
Machine Learning
As a prominent subfield of artificial intelligence (AI), machine learning (ML) equips computers to develop algorithms and improve their capabilities based on previous experiences — and without explicitly being programmed. Supervised learning techniques rely on known input or output data to help predict or anticipate future outputs, while unsupervised solutions emphasize hidden patterns from input data.
With ML, the real challenge lies in knowing which algorithms to use (and when). This calls for extensive data literacy, as different cues will reveal when certain types of algorithms are more likely to play out as desired. Prior to mastering ML, you will need a basic understanding of Python, plus prior knowledge of statistics and linear algebra. Beyond this, hands-on practice is your best bet for mastering the intricacies of ML algorithms.
Data Visualization
Data is often most easily and effectively conveyed via graphics. These representations form the basis of data visualization, in which everything from charts to graphs to maps are used to translate highly complex concepts and make them easier for everyday individuals to understand.
As you study data visualization within an MSDA program, you will gain a thorough grasp of geospatial and time-oriented data, along with insight into multivariate data and human perception as it relates to visualization strategies.
Analytics
Analytics play heavily into the full range of skills referenced in this guide. Simply put, this practice involves collecting and organizing data in a way that allows you to extract insights, reveal trends, and make predictions accordingly.
Programming languages provide a solid start, but your study of analytics will also involve several algorithm-based techniques and operationalization strategies, used to turn abstract concepts into easily measurable observations. Analytics is incorporated in every core course within the MSDA program at University of Tennessee at Chattanooga, with real-life projects providing plenty of opportunities to develop and practice these skills.
Big Data Management
Big data plays an outsized role in our modern digital environment. These days, unprecedented volumes of data are simply too extensive to handle via traditional data processing solutions. Hence the central role of big data management in numerous data science career paths and within the MSDA curriculum.
One of the most crucial concepts to master? The common framework known as Hadoop, which can efficiently process sizable data sets. Many other techniques and platforms should be studied, too, including the programming model MapReduce and NoSQL database technology.
Databases and Data Warehouses
As central repositories responsible for storing and analyzing a wealth of information, data warehouses contribute to a range of business intelligence activities. The data warehousing process ensures not only that information is gathered from various quality resources but also that standardized information and consistent results can be expected.
Coursework centered around these data warehouses delves into logical and conceptual design, along with the extract, transform, and load (ETL) technique. This is where a thorough understanding of SQL will come into play, as this particular programming language will prove necessary for querying purposes.
Data Mining
As an analytics cornerstone, data mining emphasizes patterns and trends as they appear within vast quantities of data. It represents the intersection between statistics and machine learning.
Sometimes referred to as knowledge discovery in data (KDD), this practice delivers much-needed clarity within the chaos that so commonly pervades today’s enormous (and often poorly structured) data sets. Tools and techniques such as data warehousing and cleaning are critical to success both in data mining as well as within the broader realm of AI and its many applications.
The Evolving Nature of Data Science Skills
Data science is in the midst of significant changes. Machine learning in particular has seen rapid growth in recent years, with the implications already evident across numerous sectors. With each new development comes the need to master new, increasingly complex skills. Different algorithms and programming languages may become more or less prominent as this transformation occurs at lightning speed.
Advanced data analytics promises solutions to many of the greatest economic and societal challenges, but it also presents new problems that call for updated skills and knowledge. Cybersecurity, for example, must take a step up as threat actors become ever more sophisticated. Thankfully, integrating AI and big data makes it possible to develop more effective cybersecurity tools and strategies. As the use of AI and big data expand, ethical questions arise and must be addressed.
The Power of a Data Analytics Master’s (MSDA)
Data-driven leadership matters, but it takes research-backed training and extensive hands-on applications to master the most impactful tools and techniques of tomorrow. A data analytics master’s degree is a must for any aspiring data scientist, as it provides an overview of cutting-edge, data-focused concepts.
At the University of Tennessee at Chattanooga, we are pleased to prepare a new generation of data scientists for the field through a curriculum that emphasizes core data analytics skills. Multiple paths to success are available by incorporating elective courses that may appeal to aspiring data managers, healthcare managers, or other future leaders with a need for both management and data science skills.Ready to begin your data science career journey? Reach out today to learn more about UTC’s Master of Science in Data Analytics.
