Data & AI Training Guide 2021
Download the GoDataDriven brochure for a complete overview of available training sessions and data engineering, data science, data analyst and analytics translator learning journeys.
Whitepaper – The Analytics Translator
The Analytics Translator is the liaison between senior management, the business, and data experts. On some days, you are the gatekeeper of the project funnel, brainstorm ideas with executives, and work with the data experts to groom the backlog of viable ideas.
Are the advancements of cloud technology making data engineering and data science skills obsolete? Far from it. Without knowing what is going on under the hood, extracting maximum performance is impossible. Understanding, controlling, and utilizing the full power of cloud tools makes having up-to-date data and AI skills more relevant than ever.
There was a time, not so long ago, when the complex nature of installing and maintaining a big data infrastructure and code made our courses an essential step in becoming data-driven. We were training Hadoop professionals from Amsterdam to Dubai to Bangalore!
Then cloud providers took off in the data and AI space. Now everyone can create recommendations from customer data or recognize cats in your holiday photos—which begs the question: Is being proficient in data science and engineering still relevant when you can outsource the job to the cloud?
And everything is changing again. In fact, with all the advantages of offloading our algorithms to the cloud, it's more important than ever to stay in the driver's seat. Here are three reasons why:
- The lack of transparency or bias in algorithmic decision-making has fueled public outcry on numerous occasions—Apple's discrimination against women applying for the Apple Card being a recent example. You can only control the algorithm if you can tweak and modify it to address the ethical issues around it. Understanding what happens under the hood, even when using AI-as-a-Service, allows you to stay in control
- In 2020, the Internet of Things will generate 4TB of data per person every month. Storing it all would be far too expensive, but we can apply machine learning to the devices that generate it and only keep the insights. Currently, iOS has a framework that imports your data scientists' models and embeds them into your app.
- Transfer learning takes a highly sophisticated algorithm and adapts it to your use case and data. As it requires less training samples and computational power, it is affordable to do it in-house.
These trends make data science and engineering skills more relevant than ever.
Hence it is my pleasure to present GoDataDriven's all-new training guide. You can find it at godatadriven.com/topic/training-brochure/.
The guide reflects a curriculum developed and taught by the very best professionals in the field with pragmatism in mind — you can apply tomorrow what you learn today.