The latest release of Posit Package Manager introduces several enhancements, including: Python Git Builders: Build Python packages (wheels) directly from Git. Blocklists: Easily block specific packages or versions. Improved Documentation: Cleare...

On Day 29, we conducted our out-of-sample test on the four strategies and found that the adjusted strategy came out on top. We made this conclusion after ranking a cross section of the following metrics: cumulative return, Sharpe Ratio, and max draw...

Pyomo is another flexible Open Source optimisation modelling language for Python. It can be used to define, solve, and analyse a wide range of optimisation problems, including Linear Programming (LP) and Mixed-Integer Programming (MIP), nonlinear programming (NLP), and differential equations. 📢 The book Hands-On Mathematical Optimization with Python (available free online) ...

In a previous post, we explored the plotnine package in Python in Excel, highlighting how its implementation of the Grammar of Graphics allows for an almost limitless array of possibilities in creating any plot imaginable: In this post, we’ll focus on how to craft layered (i.e., multiple elements ...

Python in Excel offers powerful tools for creating impactful visualizations, and one standout option is the plotnine package. In this post, we’ll delve into the origins and philosophy of plotnine, explore its core building blocks, and demonstrate some easy and impressive visualizations you can create—perfect for Excel users ...

Data science interviews present unique challenges that blend theoretical knowledge, practical coding skills, and business acumen. After helping hundreds of candidates prepare for roles at top tech companies, I’m sharing key strategies and tools that can streamline your preparation process. Building a Solid Foundation Before diving into specific interview ...

CVXPY is a powerful, Open Source optimization modelling library for Python. It provides an interface for defining, solving, and analysing a wide range of convex optimization problems, including Linear Programming (LP), Quadratic Programming (QP), Second-Order Cone Programming (SOCP), and Semidefinite Programming (SDP).

SciPy is a general-purpose scientific computing library for Python, with an optimize module for optimisation.

In a previous post, we explored the Seaborn library and highlighted its fundamental plot types, showcasing how it simplifies the creation of visually appealing and insightful data visualizations directly in Excel using Python: In this post, we’ll take it a step further by diving into advanced, statistically driven plots—...