Features Tutorials Opinions Tops Jobs Submit a blog Image of the week In this issue: Are Data Scientists and ML Engineers Are Luxury Employees? KDnuggets Top Blogs Reward Program will pay to the authors of top blogs each month. Reposts accepted, but original submissions get 3x the rate of reposts. Check our guidelines and submit your blog soon! Features Data Scientists and ML Engineers Are Luxury Employees, by Adrien Biarnes 5 Lessons McKinsey Taught Me That Will Make You a Better Data Scientist, by Tessa Xie Managing Your Reusable Python Code as a Data Scientist, by Matthew Mayo GitHub Copilot: Your AI pair programmer - what is all the fuss about?, by Matthew Mayo A Learning Path To Becoming a Data Scientist, by Sara Metwalli Tutorials, Overviews ROC Curve Explained, by Zolzaya Luvsandorj Predict Customer Churn (the right way) using PyCaret, by Moez Ali Semantic Search: Measuring Meaning From Jaccard to Bert, by James Briggs High-Performance Deep Learning: How to train smaller, faster, and better models - Part 3, by Gaurav Menghani Prepare Behavioral Questions for Data Science Interviews, by Zijing Zhu How to Use NVIDIA GPU Accelerated Libraries, by Kevin Vu Learning Data Science Through Social Media, by Susan Sivek From Scratch: Permutation Feature Importance for ML Interpretability, by Seth Billiau Opinions How To Transition From Data Freelancer to Data Entrepreneur (Almost Overnight), by Lillian Pierson Ethics, Fairness, and Bias in AI, by Aditya Aggarwal Top Stories, Tweets Top Stories, Jun 28 - Jul 4: 5 Lessons McKinsey Taught Me That Will Make You a Better Data Scientist, by KDnuggets Jobs See our recent jobs in AI, Analytics, Data Science, Machine Learning You can post a free short entry on KDnuggets jobs page for an industry or academic job related to AI, Big Data, Data Science, or Machine Learning, email - see details at kdnuggets.com/jobs

I started in Data Science back in 2015. It was not an intended move but the answer to the needs of my employer. I was working for a company providing automation services to Spanish corporations and we had the need to leverage data to automate complex tasks whose rules could not be easily hard-coded. I had recently graduated as an engineer in the middle of a terrible economic crisis, had some statistical modeling knowledge and was proficient using MATLAB. In 2015 there was not specialized Data Science degrees or boot-camps to jump-start in the field (at least, in Spain) and the naturally closest studies you could have were, in this order: Mathematics (in Spain with a strong focus in becoming a teacher/professor in the public education system) or Software Engineer (most of them more interested in App Development or creating the new Uber of "X" than in boring Data Science stuff back then).

I am a data scientist and avid reader (and writer) of articles about data science and machine learning. It takes time to read journal articles, listen to podcasts, analyze interesting data, and play with new machine learning packages. Blog posts offer much of this knowledge in a condensed form. When browsing, I look for articles that will teach me something new and applicable. Where is the field of machine learning heading?