data science

In many ways, AI and finance are made for each other. Machine learning and other techniques make it easier to identify patterns that might otherwise not be detected by the human eye, and finance is quantitative to begin with so that it’s hard not to find traction. Financial firms have also invested heavily in AI in the past, and more are starting to tap into the  financial applications of machine learning (ML) and deep learning. This week, we’re joined by CEO and Co-founder of Kavout&nbsp;Alex Lu, whose company offers AI trading applications for enterprises and individuals. Lu speaks today about the kinds of patterns that traders now have access to in finance, and he gives examples of ways Kavout and other institutions are using artificial intelligence in stock trading to build better and more personalized products and services. &nbsp;

Artificial Intelligence in Stock Trading - Future Trends and Applications

Data science and analytics are hot topics in business these days, but for a lot of folks looking to bring data into their organization, it can be hard to know where to start and what it looks like when they're succeeding. That was the motivation for writing a whitepaper on the analytics maturity of an organization, and that's what we're talking about today. In particular, we break it down into five attributes of an organization that contribute (or not) to their success in analytics, and what each of those mean and why they matter. 

Whitepaper here:
bit.ly/analyticsmaturity

Analytics Maturity

In this episode of the SuperDataScience Podcast, I chat with the aspiring data scientist, Mike Taveirne. You will discuss the five stages of data science process for starters, get valuable tips for people who want to learn and transition to data science, and know how to apply your data science knowledge to your hobbies and interests.

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/159

data science

viktorcirbus