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Index

16/11/2022

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AI: Career advice
  • What does the future of AI look like? [New]
  • Advice for people looking for Data Science Jobs now [New]
  • Q&A on Careers in AI (Industry Insiders for UCL Alumni) [Video] 
  • AI in India (Industry Insiders for UCL Alumni) [Video] 
  • Careers in AI [Video]  
  • AI Career Advice - Part 1 [Video] 
  • AI Career Advice - Part 2 [Video]  
  • Advice for people who want to do research in AI​

AI: Interview preparation
  • What questions should data science candidates ask in job interviews? [New]
  • How much Mathematics is required for Data Science and how do I prepare? 
  • How do I prepare for Machine Learning Engineer job interviews? 
  • How do I crack data science interviews, and should I learn DSA for the same?
  • Machine Learning System Design Mock Interview for Robinhood [Video]  
  • Deep Learning Mock Interview for Amazon [Video] ​
​
AI: Career FAQs
  • How Data Science helps Businesses? 
  • ​​How to conduct innovative, applied AI research? [Video]  
  • How has the field of AI evolved? [Video] ​​
  • How crucial is starting early in data science?​​​​​​​

Q&A on Quora
  • What is the success rate of data science projects making it to production?
  • What is the difference between a data scientist and a machine learning engineer?
  • How is Data Engineering as a career in today's time?
  • Can a non-engineer become a data scientist?
  • Do I need a Master's degree to get a data scientist, ML, or AI job?
  • What are the best questions to ask data science candidates in job interviews?​
  • Is it worth become a machine learning engineer when you are over 50 years old?
Comments

How does the Future of AI look like?

15/11/2022

Comments

 
The field of AI has changed dramatically over the last decade. Consequently, the role of a data scientist has also transformed and evolved into multiple specialized roles like data engineer, machine learning engineer, research scientist, applied scientist, AI product manager, and so on. I believe that we are still in the early days of AI, and it is as good a time as ever to break into data science.

Data science is also becoming more engineering-focused as companies realize that business value cannot be realized until a robust infrastructure is in place to deploy, monitor, and maintain data science models in production. As a result, data science offers an opportunity for software engineers to transition laterally and work more closely with data and models apart from code.

Additionally, data science has matured as a field with the advent of several tools and products that make the entire data science life cycle more efficient, transparent, and reproducible. The organizational time, effort, and resources needed from conceptualization to production of machine learning models are reducing, enabling data scientists to drive more significant business impact.

Another trend is the focus on deriving business value from massive amounts of unstructured business data like images, text, audio, and video apart from structured, tabular data. For such applications, deep learning models are particularly relevant. We are currently witnessing a tremendous amount of innovation and advances in this area, with groundbreaking models like BERT, GPT-3, DALL-E 2, Imagen, and Whisper, to name a few.
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We will see a more significant business impact of innovative AI R&D where startups and large companies leverage these technologies to build new products and services. It is, therefore, even more exciting to be at the forefront of AI innovation and build a long-term career in data science and AI.
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Advice for people looking for Data Science Jobs right now

15/11/2022

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If you have a quantitative background in computer science, engineering, physics, finance, and related disciplines, you already have the core technical skill set to transition and excel in data science.
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Candidates from a non-technical domain, on the other hand, have the advantage of domain knowledge. Doing well in data science requires a deep understanding of both the data (and the business domain) as well as the scientific aspects of analyzing data. I have seen and coached several candidates from non-traditional backgrounds in transitioning to data science and becoming successful practitioners and experts in the field.

​My general advice to candidates interested in data science is to realize that they might already have several skills relevant to the data science industry. You only need to bridge the gap in the skills you lack or are less confident in to crack jobs at top tech companies and startups successfully.
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