Modern business thrives on fresh ideas from a skilled pool of data professionals, ranging from data scientists to business subject-matter experts. Data-driven organizations succeed when all personnel—both technical and business—have a common understanding of the core big-data best skills, tools and practices.
Organizations are actively cultivating these skills internally while scouting for the best big-data talent being groomed in institutions of higher learning. If you want to be a leading-edge business analytics professional, you will need a degree, or something substantially like it, to prove you’re committed to this challenging career. You may not want or need to transform yourself into a full-blown data scientist, but you’ll probably work directly with them at some point in your career and will need to master many of the same core skills.
Whatever specific big-data analytics role you aspire toward, you will need to leverage a structured curriculum in order to address your own personal “skills gap.” Credentials are essential for demonstrating to the next potential employer that you have the right skills at the right place and right time to be a valuable player from day one. Whether you secure these credentials during your “college years” or midway through your career is entirely up to you, but the fact that you’ve done so at all speaks volumes about your commitment to this discipline.
Keep in mind that big-data-relevant academic credentials vary widely in breadth, depth, rigor and utility. Unless you’re a self-taught genius, it matters whether you get that old data-science sheepskin from a traditional university vs. an online school vs. a vendor-sponsored learning program. It also matters whether you only logged a year in the classroom vs. sacrificed a considerable portion of your life reaching for the golden ring of a Ph.D. And it matters whether you simply skimmed the surface of old-school data science vs. pursued a deep specialization in a leading-edge advanced analytic discipline.
Whatever path you take to gain your specific big-data analytics education, make sure you emerge with credentials that equip you, at the very least, with the following:
- Fluency in the core concepts of strategic business analytics, including “360-degree view of the customer” and “customer experience optimization”
- Comprehensive understanding of the business-analytics lifecycle, including the typical roles and responsibilities of data scientists, statistical analysts and subject-matter domain experts every step of the way.
- Practical understanding of the primary approaches and tools—including basic statistics, linear and logistic regression, data mining, predictive modeling, decision trees, machine learning, etc.—that are central to mainstream business big-data initiatives.
Classroom instruction is important, but a curriculum that is 100 percent devoted to reading books, taking tests and sitting through lectures is insufficient. Make sure that you can also demonstrate to your future employers that you have actually developed statistical models that use real data and have some relevance to real-world problems that they might task you with.
Fortunately, a growing range of colleges and universities recognize that they need to align their big-data analytics curricula with the needs of their students’ future employers. In this regard, many institutions of higher education are deepening their engagement with leading big-data analytics solution providers, such as described in this fresh announcement from IBM.
For other resources on big-data education and skills enhancement, see:
- Infographic: What Big Data Skills are Most in Demand?
- The Best Data Scientists Cluster Around the Biggest Big-Data Challenges
- Data Scientist: Master the Basics, Avoid The Most Common Mistakes
- Data Scientist: Strike a Balance Between Quantitative & Qualitative Exploration
- Data Scientist: Bias, Backlash and Brutal Self-Criticism
- There’s no shortage of data science smarts
- The Rise of the Data Scientist in the Smarter Planet
- Data Journalism: Big Data, Data Science, & the Art of Non-Fictional Storytelling
- Data Scientist: Exploration in the Age of the Unstructured
- Data Scientist: Closing the Talent Gap
- Data Scientist: Consider the Curriculum
- Data Scientist: Mastering the Methodology, Learning the Lingo
- Data Scientists: Credentialed or Otherwise
- Data Scientists: Grow and Sustain a Center of Excellence