Imagine having the skills of a data analyst and the marketing know-how of a marketing professional. This hybrid role is known as a marketing analyst, and they have a unique blend of marketing and data skills.
They bridge the gap between marketing and analytics by bringing data into business decisions. Through careful study of the market, a marketing analyst makes predictions to help the business grow, then backs up those predictions by gathering data, analyzing it, and presenting it.
They also manage to consistently prove themselves useful to the companies that employ them.
From self-driving lawn mowers for our homes to smart cocktail makers in bars, robots and automation are changing our lives in a myriad of different ways. At the forefront of this revolution we see robotics engineers innovating in every industry imaginable.
If you’re interested in changing the world or just love working with futuristic tech, then a career in robotics engineering may be for you. Read on as we explore what it is that robotics engineers do.
With the rise of machine learning (ML) and associated technologies, the demand for robotics engineers is growing each year. It’s projected that the number of jobs in the field will grow 9% between 2016 and 2026, leading to a shortage of qualified engineers. As a result, the robotics engineer salary is becoming even more competitive in order to attract top talent.
Here’s what you can expect when it comes to the salary for a robotics engineer.
Security and data breaches are a growing concern globally. Believe it or not, more than 3.2 million records were compromised in the 10 biggest data breaches in the first half of 2020. The data could be anything ranging from Social Security numbers to your credit card details to something as personal as a medical record.
That’s why cybersecurity is top of mind for every company around the world. With the increasing number of data breaches — up 54% in 2019 — the role of a cybersecurity professional has become vital. In fact, the need for cybersecurity specialists has become so paramount, that according to the US Bureau of Labor Statistics, the occupation is projected to grow at a much faster rate — more than 30% between 2018 and 2028 — than the average of all other occupations.
As demand for cybersecurity specialists reaches a fever pitch, Udacity is excited to announce the release of the all-new Introduction to Cybersecurity Nanodegree program — created in collaboration with SecurityScorecard — a great start for anyone interested in this critical field.
Every day, billions of queries are entered into the Google search bar. We’re constantly looking for something — the definition of a word, the bus schedule, the lyrics to that certain song. However, in the realm of computer science the term “search” has a slightly broader meaning. With search algorithms, we’re often not looking for a single item, but for a series of steps: the optimal strategy for solving a given problem. That fact is what makes this family of algorithms so central to so many different applications.
Machine learning is no longer a sci-fi concept, but an actual application of AI technology we use every day. Machine learning engineers focus on developing computer programs that can access data and use it to learn themselves.
Their daily work involves helping machines learn by creating and fine-tuning training datasets, developing machine learning models, and testing these datasets and models on machines. The goal is for the machine to be able to make informed decisions without the direct instruction of a human.
I have been working as a Software Engineer for most of my 15-year career in technology. I progressed from embedded software development, to full-stack web and database development, to cloud-native application development and DevOps. Currently, my work focuses on cloud AI development and data engineering.
In 2015, I was searching for cost-effective ways to advance my career. I firmly believed that machine learning and AI skills were the most coveted and critical digital skills – in addition to cloud and software development. Since I already had extensive software development experience, I focused my development plan on expanding my skillset in data science, machine learning (ML) and AI.