Software developers and cyber security experts have long fought the good fight against vulnerabilities in code to defend against hackers. A new, subtle approach to maliciously targeting machine learning models has been a recent hot topic in research, but its statistical nature makes it difficult to find and patch these so-called adversarial attacks. Such threats in the real-world are becoming imminent as the adoption of machine learning spreads, and a systematic defense must be implemented.
Extracting immediate predictions from machine learning algorithms on the spot based on brand-new data can offer a next level of interaction and potential value to its consumers. The infrastructure and tech stack required to implement such real-time systems is also next level, and many organizations -- especially in the US -- seem to be resisting. But, what even is real-time ML, and how can it deliver a better experience?
As data becomes the new ‘Gold’ for businesses, data scientists are set to find their value in this gold. This write-up clearly defines the job requirements and company expectations that this phenomenally evolving role entails.
Machine learning may appear like the go-to topic to start learning for the aspiring data scientist. But. thinking these techniques are the key aspects of the role is the biggest misconception. So much more goes into becoming a successful data scientist, and machine learning is only one component of broader skills around processing, managing, and understanding the science behind the data.
Geoff Hinton has lived at the outer reaches of machine learning research since an aborted attempt at a carpentry career a half century ago. He spoke to Craig Smith about his work In 2020 and what he sees on the horizon for AI.
A Data Scientist must be a jack of many, many trades. Especially when working in broader teams, understanding the roles of others, such as data engineering, can help you validate progress and be aware of potential pitfalls. So, how can you convince your analysts to realize the importance of expanding their toolkit? Examples from real life often provide great insight.
Many online tools and platforms exist to help you establish a clear and persuasive online profile for potential employers to review. Have you considered how your go-to online code repository could also help you land your next job?
In this blog, I shared my story on getting 4 data science job offers including Airbnb, Lyft and Twitter after being laid off. Any data scientist who was laid off due to the pandemic or who is actively looking for a data science position can find something here to which they can relate.
Agile is not reserved for software developers only -- that's a myth. While these effective strategies are not commonly used by data scientists today and some aspects of data science make Agile a bit tricky, the methodology offers plenty of benefits to data science projects that can increase the effectiveness of your process and bring more success to your outcomes.
These are some obstacles the author faced in their data science learning journey in the past year, including how much time it took to overcome each obstacle and what it has taught the author.
So, what do you do for a living? Sometimes, the answer to that question can feel like, "everything!" Well, for the Data Scientist, an extreme sense of being a "jack of all trades" is common. In fact, four such trades can be defined that a top-quality Data Scientist will iterate through during any one project.
These are the lessons and best practices I learned in many years of experience in data blending, and the software that became my most important tool in my day-to-day work.
These trends broadly cover the three themes of: Where will businesses adopt AI in 2021? How will AI become more accessible? How will AI capabilities evolve?
Marketing data science - data science related to marketing - is now a significant part of marketing. Some of it directly competes with traditional marketing research and many marketing researchers may wonder what the future holds in store for it.
Data engineering skills are currently in high demand. If you are looking for career prospects in this fast-growing profession, then these 10 skills and key factors will help you prepare to land an entry-level position in this field.
When a company decides that they want to start leveraging their data for the first time, it can be a daunting task. Many businesses aren’t fully aware of all that goes into building a data science department. If you're the data scientist hired to make this happen, we have some tips to help you face the task head-on.
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How to Get a Job as a Data Scientist, by Devin Partida - Jan 27, 2021.
Here’s a step-by-step guide to starting your career in data science.
Want to Be a Data Scientist? Don’t Start With Machine Learning, by Terence Shin - Jan 26, 2021.
Machine learning may appear like the go-to topic to start learning for the aspiring data scientist. But. thinking these techniques are the key aspects of the role is the biggest misconception. So much more goes into becoming a successful data scientist, and machine learning is only one component of broader skills around processing, managing, and understanding the science behind the data.
How to Get a Job as a Data Scientist
Want to Be a Data Scientist? Don’t Start With Machine Learning
How I Got 4 Data Science Offers and Doubled my Income 2 Months After Being Laid Off