The main reason to use Python is that you get a lot more options than what's included in most spreadsheets. Spreadsheets are ...

Syndication 10 simple Python tips to speed up your data analysis October 12, 2020 - 11:39 am Tips and tricks, especially in the programming world, can be very useful.

Unlock deeper analytical capabilities by integrating BQL, Bloomberg’s most advanced data API, with Python via the BQL Object Model. This session will feature practical demonstrations, code ...

Discover how Julius AI simplifies complex data analysis for US businesses and researchers. Learn about its recent $10M ...

Python Integration in Excel TL;DR Key Takeaways : Integrating Python into Excel enhances data analysis by combining Excel’s accessibility with Python’s advanced analytical and visualization tools.

6 min read Scenario Analysis: Python Code Snippets for Forecasting Investment Performance Copy and paste these code snippets to forecast investment performance in any market conditions.

Returns-based style analysis is widely used due to its relatively light input data requirements. All you need are fund returns and premia for the style factors you want to evaluate.

Data rarely comes in usable form. Data wrangling and exploratory data analysis are the difference between a good data science model and garbage in, garbage out.

If you’re unsure about any of this, then Hamlet’s guide on Python data analysis for SEO is the perfect primer. Now, without further ado, let’s jump in. LEN ...