Types of analysis (Oversimplified)

Hello data nerds! Data Analytics is everywhere these days. There are plenty of courses, certifications, data-fluencers, etc. teaching and guiding the masses. For someone just starting the journey, this can be quite daunting. Read along as I do a simple breakdown of some of the common types of analytics that can help you decipher what is required when. You may thank me later!

Let’s look at some realistic made-up use cases to drive home the points better. Also, to make things easier, I am focusing this article mostly on use cases of data analytics in the healthcare education sector.

The "What Happened?"

Descriptive analytics. It's the bedrock of data-driven decisions in healthcare education. Think of it as your university's medical history chart. It summarizes past performances, enrolments, and feedback. Patterns emerge. Benchmarks are born.?But what benefits can be got out of this?

Example: Nursing Program Performance Analysis

Professor Smith teaches pharmacology. She's puzzled. Her students keep bombing exams after mid-term break. Netflix binge, anyone? She digs into the last five years of data. Grades, completion rates, clinical feedback - she analyses it all. Time patterns catch her eye.

The revelation? Post-break catch-up is a struggle. It's a domino effect on later exams. Smith acts fast. Pre-break reviews. Post-break workshops. Spaced repetition for key concepts. Boom! Exam scores jump 15%. Dropouts plummet by 20%.

The main takeaway is that descriptive analytics turns hunches into strategies. It's not just number-crunching; it's academic alchemy.

Answering the "But, why?"

Descriptive analytics tells you what happened. But why? Enter diagnostic analytics. It's like being Dr. House, but for education. You're not diagnosing rare diseases; you're uncovering the root of educational woes.

Another Example: The Great Radiology Dropout Mystery

A university's radiology program is hemorrhaging students. 30% dropout rate - ouch! Time for some diagnostic detective work. They analyse everything. Student backgrounds. Admission criteria. Course performance. Feedback surveys. Even prerequisite grades.

The culprits? Weak math skills. Mismatched expectations. Lack of hands-on experience. Armed with these insights, the university springs into action. Math support. Virtual reality simulations. Revised admissions. Mentorship programs. Who knew math skills can be related to the field of radiology?

The result? Dropouts halve. Student satisfaction soars. It's not just about treating symptoms; it's about curing the disease at its source.

Like palmistry

Imagine having a weather forecast for student success. That's predictive analytics. It's like palmistry. Someone looking at your palm and forecasting what your future might hold, but instead of mystical mumbo-jumbo, it uses stats and machine learning.

Case Study: The Great Enrolment Surge

An admissions office decides to play fortune-teller. They analyse application trends, demographics, economic indicators. Even government policies. Their crystal ball... er, algorithm predicts a 25% surge in nursing applications. Why? A new government scholarship.

But wait, there's more! Non-traditional students are flocking to healthcare. Career changers are on the rise. The university doesn't panic. They prepare. More faculty. Extra clinical sites. Flexible learning options.

What is the outcome you ask? They ride the enrolment wave like pros. Non-traditional student numbers skyrocket. It's not about reacting; it's about surfing the trends before they hit.

What to do? Do this....

Prescriptive analytics is like having a personal advisor. It doesn't just predict problems; it maps out solutions. It's like GPS, but instead of avoiding traffic jams, it's optimizing your problem and providing the required solutions for it, like an all-knowing mighty AI. Now don’t get excited with this buzzword. I am not referring to generative AI here, but the actual AI which is (basically) complex neural networks doing lots of calculations to solve a real world problem......one algorithm at a time.

Another Case Study: The Great Clinical Placement Puzzle

A nursing program decides to play matchmaker. Not for romance, but for clinical placements. Their tool? A prescriptive analytics system that would make Cupid jealous.

It considers everything. Student preferences. Clinical site specialties. Preceptor teaching styles. Even geographic constraints. It's like a dating app, but for medical training.

The results? Student satisfaction skyrockets. Clinical sites are singing praises. Performance scores are through the roof. It's not just about making decisions; it's about making the best decisions.

Understanding the data

Cognitive analytics is the brainiac of the bunch. It doesn't just process data; it understands context. The easiest way to explain this would be to imagine an algorithm which can read between the lines. Freaky? Fancy? You tell me!

Scenario: The All-Seeing AI Student Support

A university unleashes an AI watchdog. It monitors everything. Grades. Attendance. Online engagement. It even reads between the lines of student communications.

Meet John, a struggling first-year nursing student. The AI spots trouble brewing. Falling quiz scores. Spotty attendance. Reduced online engagement. The AI doesn't just raise alarms; it prescribes solutions. Tailored study resources. Advisor check-ins. Peer tutoring.

Cognitive analytics usually Failing grades plummet. Student well-being soars. Early interventions work wonders. It's not just about collecting data; it's about understanding the student behind the numbers.

So Where Do We Go From Here?

As we stand on this data-driven precipice, questions swirl like a hurricane. Will AI teaching assistants become the norm? How do we balance data-driven decisions with ethical considerations? Can we quantify empathy and bedside manner?

The future of healthcare education analytics is as exciting as it is uncertain. It's a brave new world, and we're just scratching the surface.

Now, if you could create the ultimate AI for healthcare education, what would it do? How do you think data will reshape healthcare professions in the coming years?

Remember, when you're drowning in datasets, you're not just pushing pixels. You're potentially revolutionizing your respective industries, one report at a time. So, the next time you're wrestling with a time-series analysis, take a moment. Appreciate the life-changing potential hidden in those numbers.

Now, if you'll excuse me, I have an excel sheet converting decimals to date again..........Until next time!

Thank you for reading, hope it helped you understand some concepts overall :)