**Course Updated - September 2022**
>> COURSE REGULARLY UPDATED: NEW TIPS, MORE LESSONS <<
Data Science grew through our experiences with Business Intelligence BI, a field that became popular in the 1990s. However, the last 20 years have seen unprecedented improvement in our ability to take action using Artificial Intelligence. As we adopt the BI methodologies to AI deployments, how will these methodologies morph to add considerations needed for model deployment, and machine learning?
Today’s Data Science work deals with big data. It introduces three major challenges:
How to deal with large volumes of data. Data understanding and data preparation must deal with large-scale observations about the population. In the world of BI on small samples, the art of data science was to find averages and trends using a sample and then project it using universal population measures such as census to project to the overall population. Most of the big data provide significant samples where such a projection may not be needed. However, bias and outliers become the real issues
Data is now available at high velocity. Using scoring engines, we can embed insights into high velocity. Data Science techniques offer significant real-time analytics techniques to make it possible. As you interact with a website or a product, the marketer or services teams can provide help to you as a user. This is due to insight embedded in high velocity.
Most of the data is in speech, unstructured text, or videos. This is a high variety. How do we interpret an image of a driver's license and extract a driver's license? Understanding and interpreting such data is now a central part of data science.
As these deployed models ingest learning in real-time and adjust their models, it is important to monitor their performance for biases and inaccuracies. We need measurement and monitoring that is no longer project-based one-time activity. It is continuous, automated, and closely monitored. The methodology must be extended to include continuous measurement and monitoring.
What will you need to succeed in this Exam?
Excel, Statistics and Python knowledge is required though - you don't need to be an expert but the basics need to be set (though there are refresher sections in this course!)
NO Android, Java, Swift, or C knowledge is required!
What does this course offer you?
This course consists of 2 practice tests.
The practice test consists of 20 questions, timed at 30 minutes
The questions are multiple-choice.
Every question is associated with a knowledge area
The answers are randomized every time you take a test
Once the test was taken, you will get an instant result report with categories of strength to weakness.
You can retake the tests over and over again as and when it suits you.
I'd be very happy to welcome you to the course!
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