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You can become a data scientist : A successful career in the USA 2022

Data scientist : Each person has their own career path. It often begins with curiosity. In data science, this can only be resolved by applying analytics. Robert Blanchard was studying economics, and wanted to know more about consumer behavior in relation to buying patterns. His research was focused on online behavior and price discrimination models. His research required more than a background in economics. It required him to learn analytics to compliment the economics. Robert began studying analytics and eventually learned SAS, which led him to a career as a data scientist.

Alberto has been a good friend for many years . As a data scientist with SAS, his role has allowed him to be flexible and live wherever he likes, in this case, San Diego. This has caused a lot of jealousy in our relationship. But I didn’t want this to stop me from sharing his insights gained working with data scientists across industries.

Which is the most challenging problem you’ve been solving in your current job?

It’s difficult to choose one. Just recently, I completed a project for a customer where we used machine-learning to improve an engineering process. I loved that project because it used several areas of machine learning that interest me, including hyperparameter search and explainable ML/AI. My most fascinating projects are usually those that use computer vision technologies.

What did you learn as you moved from a graduate student to a career working in data science?

When I switched from economics to analysis, I had to shift my analytical perspective. While in economics we use the right methods of inference to describe economic relationships, analytics focuses on predicting or classifying the unknown. Although subtle, this adjustment is very important because it allows us to move away from identifying all patterns and instead focus on those that are consistent and persistent within the data. This is the key to prediction: identifying patterns and relationships that are consistent across time and data.

Which technologies and methods are you most familiar with?

I have spent many years using traditional machine learning models. I have used generalized linear models as well as tree-based models. In the past three to four years, my focus has shifted towards deep learning with a heavy focus on computer vision. My current work is focused on computer vision models and understanding their potential biases. My research also focuses on neural architecture search strategies for deep-learning models. These areas have many benefits and gains.

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Are you more likely to spend your time programming or using visual tools?

While I spend most of my time programming, visual tools can be very useful for certain projects. Both types of tools have value to me and I enjoy working with them both.

What was your biggest learning experience on the job?

Models and data will always have mistakes, but they will never be clean. This is a good thing, as it allows models to learn from mistakes and improve. The learning process that models engage in would be impossible without error.

Concerning the lesson that data is always dirty, I believe in the motto “trust but verify”. Don’t just trust what someone tells you about the data. Do your own research. Cross-tabulations can be used to examine bivariate relationships. Examine the margin distributions and, if time permits, the conditional distributions.

What are the emerging trends in data analytics? What are the roles changing and what skills should analysts be focusing on?

In the future, I see synthetic data generation becoming more important. The models that create synthetic data are improving every day. Think about removing data (or lack thereof) from your list. You can also improve your model’s generalization performance by creating “edge cases”, which are unlikely but possible instances that may not exist in your historical data. Imagine a plane flying through lightning storms. Although this is rare, you might not have enough data to represent these scenarios. However, they are possible. Imagine that all the data you generate is “clean”, which can save data scientists many hours.

Are you a data analyst for good projects?

A few years back, I was part of a data hackathon where we were forecasting wildfires. My analytic skills were donated to the Full Belly Project, based in Wilmington, NC, early in my career. Unfortunately, I haven’t had the opportunity to work with data on good projects. Although I had considered donating my data science skills for the Bill & Melinda Gates Foundation I have not pursued this idea.

This question really highlights one of the greatest aspects of data science: you can make an impact on the entire world. Your impact is yours. If you are looking to make the world a better place, then using your data science skills for data for good projects is a great way to start.

What has your career done to help you create the life that you desire?

It all begins with finding what you love doing and finding a job that interests you. The rest will fall into place. Data science is a way for me to grow, learn, and create so that I don’t feel stuck. SAS helped me move to San Diego because I could do my work anywhere. I am now a data scientist and beach bum. It’s a dream I never had. You can watch the full interview here.

What is your favorite time of day to code?

Although I don’t have a preferred time to code, I do have a favorite mindset. My favorite time to code is when I am working with other people on something new. I love sharing my work with them or combining their work. Collaboration is what I like most and I find that I code better in these situations. I don’t like interrupting the flow of other people’s work by trying to add or change some of mine. I also prefer a novel approach to code, where I have multiple small branches (sub-projects) in my mind that are not connected and each is working on its own project.

Which step of the Analytics Life Cycle are you involved with?

My work is mostly in the model-building area of artificial Intelligence. Although it’s helpful to be able to operationalize models, I often rely on others who are more skilled than me in this area.

Artificial intelligence AI research of robot and cyborg development for future of people living. Digital data mining and machine learning technology design for computer brain communication.

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