Here are five courses every aspiring big data analytics student should take
As we move forward in the informational age, the amount of pervasive data and technology in our lives can be overwhelming. To add to the digital mayhem, data and technology continues to grow at an exponential rate.
Since technological advancement has far surpassed what most of us had imagined, we are still all wondering what to do with this vast pot of knowledge. Progression as we know it will rely on this big data dissemination. To become one of the pioneers of this great field, here are five courses every aspiring big data analytics student should take:
1. Data structures and algorithms
This course is the cornerstone of understanding big data. The course plan is structured to cover everything from data as a general concept to principles in analyzing statistical figures. The massive gamut of instruction given in this course includes serial and parallel data structures, data abstraction principles, linear programming, linked lists, trees and divide-and-conquer algorithms.
2. Legal, ethical, and management issues in technology
If there’s one class not to play hooky for, it’s this one. Although the web is seen as a free, independent entity, there are very strict rules and regulations when it comes to internet law. This course prepares students for the legal and ethical issues they will come across in internship programs and professional careers. Some major talking points of this course are service-based learning, professionalism, and the impact of technology on society.
3. Introduction to philosophy
Although big data analytics certainly doesn’t fall into the category of liberal arts, a general understanding of psychology and philosophy is vital in conducting business. This course provides knowledge and understanding of philosophical processes regarding problem solving, thinking, and the overall nature in its complex design. Combining the skill set of technological structure with the comprehension of human ambiguity creates a recipe for success.
4. Engineering and technology project management
After refining a skill set in data analytics, the next step is learning how to turn this applied skill set into thoughtful action. The tools of the trade to be learned here are risk analysis, project organization, leveling, and time and cost estimation, just to name a few.
5. Business intelligence (BI)
Business intelligence training is one of the final stages before students are ready to enter the workforce independently. In its preliminary stage, the course teaches the application and conceptual principles needed to implement a proper decision support system. After mastering its use, many students should follow up with advanced BI training to improve their proficiency in business intelligence software, as well as web services architectural methods.
A combination of these eclectic courses in programs for big data analytics degrees will prepare scholars not only to perform analytical research, but also provide them the insight to identify and understand the information they come across. More than just a focus on big data, this curriculum teaches skills in problem solving, innovation, patience, and dedication. Upon receiving a degree in big data analytics, the next steps are simply to apply this knowledge through exploring which organizations and job functions suit one’s personal interests, capitalizing on internship opportunities, and experimenting with the giant force of world wide web meta data.
The views expressed are of the author.
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