This concentration introduces students to the use of financial analytics used by finance practitioners. It provides a strong and rigorous introduction to the use of financial applications in fintech ...

Python is invaluable behind the scenes of the financial services sector, although its role is rarely recognized. Its simplicity, versatility, and rich ecosystem make it indispensable for managing ...

What it takes to become a financial data scientist and why financial institutions are recruiting candidates quickly. By Vivian Zhang, founder and CTO of NYC Data Science Academy and adjunct professor ...

When it comes to working with data in a tabular form, most people reach for a spreadsheet. That’s not a bad choice: Microsoft Excel and similar programs are familiar and loaded with functionality for ...

What first interested you in data analysis, Python and pandas? I started my career working in ad tech, where I had access to log-level data from the ads that were being served, and I learned R to ...

How scenario analysis tools can help investors manage financial risk and evaluate returns. Forecasting is a no-win situation. If you get it right, people go about their business. But if it’s wrong, ...

If you’d like an LLM to act more like a partner than a tool, Databot is an experimental alternative to querychat that also works in both R and Python. Databot is designed to analyze data you’ve ...

Python and R each shine in different areas of data science—Python in machine learning and automation, R in statistical analysis and visualization. By integrating them, you can combine their strengths ...

What if you could turn Excel into a powerhouse for advanced data analysis and automation in just a few clicks? Imagine effortlessly cleaning messy datasets, running complex calculations, or generating ...