Demand for Data Analysts has shot through the roof over the last 5 years.
Data analysts are in increasing demand.
And, thanks largely to data coming in from the Internet of Things (IoT) and advances in cloud computing, global data storage is set to grow from 45 zettabytes to 175 zettabytes by 2025.
So the need for experts to parse and analyze all of this data is set to rise.
Why are so many data analysts required?
After all, there are plenty of data analytics programs out there that can sort through it all.
And “digital transformation” has supposedly replaced many human-led business tasks.
Sure, machines can help analyze data.
But big data is often extremely messy and lacking in proper structure.
Which is why humans are needed to manually tidy training data before it’s ingested by machine learning algorithms.
It’s also increasingly common for data people to be involved on the output end too.
AI-produced results are not always reliable or accurate, so machine learning companies often use humans to clean up the final data.
And write up an analysis of what they find in a way that non-tech stakeholders can understand it.
Amazon’s Mechanical Turk is the biggest platform where “Turkers” complete data labeling and cleaning jobs.
The Data Science and Machine Learning methods of the 2020s will be less artificial and automated than initially expected.
Augmented intelligence and human-in-the-loop artificial intelligence will likely become a big trend in Data Science.
Do you see yourself filling the gap in the Data Analytics space?
Do you have the requisite skills to play in the Data Analytics space?
If not, would you want to acquire the relevant skills required to become a Data Analyst?
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