Deep Data Dive

This episode is part 2 of our discussion with Jonathan Bechtel, a data science instructor at General Assembly. In this episode we tackle the following topics: • The job application/hiring process for data science - Do bootcamps and graduate programs offer a career services office? - Training for technical screenings - How long does a job search take? • How is applying into a data science job different now vs 3 or 4 years ago?

Special Guest: Jonathan Bechtel, data science instructor at General Assembly (Worked with multiple companies in NYC and other cities) (Lots of work with programmatic data extraction and pipelining) How did we all get into data science? • Jonathan, Vijay, Laura Different ways to get into data science • Bootcamp programs • Traditional masters • Self Taught

Episode 3 : Data science and Machine Learning In this episode, we juxtapose the different types of Machine Learning: supervised, unsupervised, semi-supervised, and reinforcement. What is the difference between data science and machine learning? Machine learning is a sub-discipline within data science Machine learning is an application of artificial intelligence Machine learning is predictive analytics Prescriptive analytics and recommending a path forward The different types of machine learning: supervised, unsupervised, semi-supervised, and reinforcement learning Supervised machine learning: data is labeled and we know the outcome, using a predictive model and comparing your predicted results to the labeled outcomes. Classic examples of churn and spam vs non-spam Classification vs regression problems and how they differ Unsupervised machine learning: data is not labeled and we don't know the outcome. Clustering using a distance metric is involved, and we can get the attributes of different clusters Latent dich allocation (LDA) for topic modeling (categories not defined beforehand) Semi-supervised machine learning Turning an unsupervised program into a supervised learning problem How do we determine accuracy and precision of semi-supervised machine learning? Reinforcement learning Self-driving cars examples A car will make mistakes, a penalty system will prevent those mistakes from re-emerging in the future The AI agent continuously learns based on a set of penalties that are imposed There will be a growth in reinforcement learning in the future

EPISODE SUMMARY: What is Data Science? Episode 1: What is Data Science? In this episode We discuss the definition of Data and how it relates to Data Science. We also touch upon the ethics of Data Science and the possible futures for it. Brief introductions What is data? Kid's height in doorway example Number of steps in a fitness tracker example Measured record Importance of metadata How does it relate to Data Science? Making sense of data Taking action based on data It's an emerging field in its infancy Data science in the news HBR calling it the sexiest job in the 21st century in 2012 Number of data science job listings per year have exploded Training programs for data science Data and data science are tools used by people