Published:
One of the most consistently expected skills for a Data Scientist today is A/B testing, often referred to as split testing. While it’s sometimes described as a simple optimization technique, in practice it’s much closer to applied science where you have to translate vague business questions into testable hypotheses, design robust experiments, and eventually, turn results into production-ready decisions.
Published:
In the last post I promised a tutorial on how to set up PowerBI connection with LLMs using MCP server. This page provides a step-by-step guide on how to use the Model Context Protocol (MCP) to connect Power BI to AI models for development while maintaining data privacy.
Published:
Lately, it feels like we can’t talk about data without AI taking center stage. For Power BI developers, we’re at a turning point—one where AI can actually build your measures, set up relationships, and do real development work. Not by you copying DAX code into your model, but by AI directly modifying your semantic model while you watch.
Published:
In this blog post, we will discuss the implementation of AI-powered voice and video agents using the Livekit platform. Our experience demonstrates that setting up these agents is a straightforward process, especially with the comprehensive documentation and tutorials available on the Livekit website. We have successfully implemented two versions of these agents: one focused solely on voice interaction and another that incorporates both voice and visual assistance.
Published:
AI is rapidly transforming industries, but at its core lies a critical foundation: data. The quality, organization, and governance of this data directly impact the success and reliability of AI models. This blog post explores what I know and what I’ve learned about the essentials of data management and its integration with AI, from fundamental concepts to practical principles and crucial considerations around security and privacy.
Published:
We are entering the age of Agentic Analytics. As I’ve recently written in my previous 2 posts, with the introduction of the Model Context Protocol (MCP), we’ve moved from “chatting about data” to “deploying agents into our models.” Imagine an AI that doesn’t just suggest a formula, but actually opens your .pbix file, creates your measures, organizes your folders, hides your technical keys, and writes your documentation—all while you watch.
Published:
We are entering the age of Agentic Analytics. As I’ve recently written in my previous 2 posts, with the introduction of the Model Context Protocol (MCP), we’ve moved from “chatting about data” to “deploying agents into our models.” Imagine an AI that doesn’t just suggest a formula, but actually opens your .pbix file, creates your measures, organizes your folders, hides your technical keys, and writes your documentation—all while you watch.
Published:
In the last post I promised a tutorial on how to set up PowerBI connection with LLMs using MCP server. This page provides a step-by-step guide on how to use the Model Context Protocol (MCP) to connect Power BI to AI models for development while maintaining data privacy.
Published:
Lately, it feels like we can’t talk about data without AI taking center stage. For Power BI developers, we’re at a turning point—one where AI can actually build your measures, set up relationships, and do real development work. Not by you copying DAX code into your model, but by AI directly modifying your semantic model while you watch.
Published:
Power BI dashboards rarely start complex. A dataset here, a couple of visuals there, maybe a slicer or two. But give it a few weeks and new measures, refreshed logic, stakeholder tweaks, performance tuning, etc, and suddenly that “simple report” is business-critical.
Published:
Business Intelligence (BI) tools have become mission-critical for data-driven organizations. They enable analysts and decision-makers to transform raw data into actionable insights via dashboards, reports, and visual storytelling. The landscape spans from commercial platforms widely adopted in enterprises to open-source engines popular in technical environments. But in 2026, the Business Intelligence (BI) landscape is no longer just about “making charts.” It is a battle between ecosystem locked-in giants and the rising tide of open-source flexibility. Whether you are a startup founder in Berlin or a data head at a Fortune 500 in New York, choosing the right tool determines how quickly your data turns into a competitive advantage. Let’s take a dive into most used BI tools. Check out this cool interactive webpage that I created for this purpose
Published:
Power BI dashboards rarely start complex. A dataset here, a couple of visuals there, maybe a slicer or two. But give it a few weeks and new measures, refreshed logic, stakeholder tweaks, performance tuning, etc, and suddenly that “simple report” is business-critical.
Published:
The idea for this project stemmed from a collaboration with my friend Jovan on his Bachelor’s thesis. His concept was to use a Siamese Convolutional Neural Network (Siamese CNN) for fingerprint recognition, structured as follows: This blog outlines how we implemented this in Python & Keras, while dealing with dataset augmentation, Siamese architecture, and model validation. You can explore the project’s Git repo and Jupyter Notebooks.
Published:
For data scientists and analysts, basic SQL queries are just the starting point. To truly unlock the power of databases and perform complex analyses, you need to delve into advanced constructs. This blog post explores five essential techniques: Subqueries, Common Table Expressions (CTEs), Views, Temporary Tables, and Create Table As Select (CTAS). These tools enable you to write more efficient, readable, and powerful SQL code.
Published:
For data scientists and analysts, basic SQL queries are just the starting point. To truly unlock the power of databases and perform complex analyses, you need to delve into advanced constructs. This blog post explores five essential techniques: Subqueries, Common Table Expressions (CTEs), Views, Temporary Tables, and Create Table As Select (CTAS). These tools enable you to write more efficient, readable, and powerful SQL code.
Published:
We are entering the age of Agentic Analytics. As I’ve recently written in my previous 2 posts, with the introduction of the Model Context Protocol (MCP), we’ve moved from “chatting about data” to “deploying agents into our models.” Imagine an AI that doesn’t just suggest a formula, but actually opens your .pbix file, creates your measures, organizes your folders, hides your technical keys, and writes your documentation—all while you watch.
Published:
I’ve been working in Automotive manufacturing for more then a year now and there is one concept that is holy grail in this industry, and that is Six Sigma. It’s a methodology for achieving near-perfect quality in manufacturing. But how can you could leverage Six Sigma tools right from your Python environment? That’s where the manufacturing package comes in.
Published:
Power BI dashboards rarely start complex. A dataset here, a couple of visuals there, maybe a slicer or two. But give it a few weeks and new measures, refreshed logic, stakeholder tweaks, performance tuning, etc, and suddenly that “simple report” is business-critical.
Published:
A well-constructed portfolio is your golden ticket to showcasing your data analysis skills and landing your dream job. It serves as a window into your expertise, showing potential employers not just what you’ve done but how you think, solve problems, and communicate results. Let’s break down the essential elements of a standout data analyst portfolio and explore how to build one that truly stands out.
Published:
A well-constructed portfolio is your golden ticket to showcasing your data analysis skills and landing your dream job. It serves as a window into your expertise, showing potential employers not just what you’ve done but how you think, solve problems, and communicate results. Let’s break down the essential elements of a standout data analyst portfolio and explore how to build one that truly stands out.
Published:
As data analyst, people often ask me where do I spend most of my work time, besides scrolling through the internet. When people think about data analysis, they often imagine building predictive models or creating dazzling visualizations. But beneath the surface of every successful data project lies an essential yet often underestimated step: data cleaning. This critical process lays the foundation for trustworthy insights, making it one of the most valuable skills for any data professional.
Published:
As data analyst, people often ask me where do I spend most of my work time, besides scrolling through the internet. When people think about data analysis, they often imagine building predictive models or creating dazzling visualizations. But beneath the surface of every successful data project lies an essential yet often underestimated step: data cleaning. This critical process lays the foundation for trustworthy insights, making it one of the most valuable skills for any data professional.
Published:
One of the most consistently expected skills for a Data Scientist today is A/B testing, often referred to as split testing. While it’s sometimes described as a simple optimization technique, in practice it’s much closer to applied science where you have to translate vague business questions into testable hypotheses, design robust experiments, and eventually, turn results into production-ready decisions.
Published:
As the data landscape continues to evolve in 2024, becoming a data scientist requires a multi-faceted approach, encompassing coding, mathematical proficiency, data analysis, and machine learning. This guide outlines a comprehensive roadmap to becoming a proficient data scientist, integrating essential skills and modern practices such as working with Large Language Models (LLMs) and prompt engineering.
Published:
I’ll be honest, I’ve always been a bit of a Databricks fanboy. Their approach to solving data problems is just elegant. The way they’ve pushed the lakehouse architecture forward, their genuinely thoughtful implementation of AI features, the speed of their platform, it all just clicks. So when my company asked me to get certified (quotas, you know how it goes), I was actually excited about it.
Published:
In analytics engineering, tooling discussions are often presented as an either-or choice: use a transactional database like Postgres, or go all-in on a distributed engine like Spark. In practice, though, a huge share of analytical work lives somewhere in the middle. The data easily fits on a laptop or a single VM, but the queries themselves are anything but trivial—wide tables, joins across multiple fact datasets, window functions, and time-based aggregations are the norm.
Published:
Power BI dashboards rarely start complex. A dataset here, a couple of visuals there, maybe a slicer or two. But give it a few weeks and new measures, refreshed logic, stakeholder tweaks, performance tuning, etc, and suddenly that “simple report” is business-critical.
Published:
As the data landscape continues to evolve in 2024, becoming a data scientist requires a multi-faceted approach, encompassing coding, mathematical proficiency, data analysis, and machine learning. This guide outlines a comprehensive roadmap to becoming a proficient data scientist, integrating essential skills and modern practices such as working with Large Language Models (LLMs) and prompt engineering.
Published:
Lately, it feels like we can’t talk about data without AI taking center stage. For Power BI developers, we’re at a turning point—one where AI can actually build your measures, set up relationships, and do real development work. Not by you copying DAX code into your model, but by AI directly modifying your semantic model while you watch.
Published:
In this blog post, we will discuss the implementation of AI-powered voice and video agents using the Livekit platform. Our experience demonstrates that setting up these agents is a straightforward process, especially with the comprehensive documentation and tutorials available on the Livekit website. We have successfully implemented two versions of these agents: one focused solely on voice interaction and another that incorporates both voice and visual assistance.
Published:
Santiago Valdarrama recently called out a trend many of us have felt firsthand: serious testing is quietly slipping out of software development. As he noted, we’re in an age of impressive demos, where slick presentations often matter more than whether the code is actually solid. This shows up especially when working with Large Language Models (LLMs) or doing what’s commonly called “vibe-coding”—an exploratory, trial-and-error style of writing code.
Published:
In the last post I promised a tutorial on how to set up PowerBI connection with LLMs using MCP server. This page provides a step-by-step guide on how to use the Model Context Protocol (MCP) to connect Power BI to AI models for development while maintaining data privacy.
Published:
I’ll be honest, I’ve always been a bit of a Databricks fanboy. Their approach to solving data problems is just elegant. The way they’ve pushed the lakehouse architecture forward, their genuinely thoughtful implementation of AI features, the speed of their platform, it all just clicks. So when my company asked me to get certified (quotas, you know how it goes), I was actually excited about it.
Published:
In this blog post, we will discuss the implementation of AI-powered voice and video agents using the Livekit platform. Our experience demonstrates that setting up these agents is a straightforward process, especially with the comprehensive documentation and tutorials available on the Livekit website. We have successfully implemented two versions of these agents: one focused solely on voice interaction and another that incorporates both voice and visual assistance.
Published:
In the last post I promised a tutorial on how to set up PowerBI connection with LLMs using MCP server. This page provides a step-by-step guide on how to use the Model Context Protocol (MCP) to connect Power BI to AI models for development while maintaining data privacy.
Published:
We are entering the age of Agentic Analytics. As I’ve recently written in my previous 2 posts, with the introduction of the Model Context Protocol (MCP), we’ve moved from “chatting about data” to “deploying agents into our models.” Imagine an AI that doesn’t just suggest a formula, but actually opens your .pbix file, creates your measures, organizes your folders, hides your technical keys, and writes your documentation—all while you watch.
Published:
In the last post I promised a tutorial on how to set up PowerBI connection with LLMs using MCP server. This page provides a step-by-step guide on how to use the Model Context Protocol (MCP) to connect Power BI to AI models for development while maintaining data privacy.
Published:
Lately, it feels like we can’t talk about data without AI taking center stage. For Power BI developers, we’re at a turning point—one where AI can actually build your measures, set up relationships, and do real development work. Not by you copying DAX code into your model, but by AI directly modifying your semantic model while you watch.
Published:
The idea for this project stemmed from a collaboration with my friend Jovan on his Bachelor’s thesis. His concept was to use a Siamese Convolutional Neural Network (Siamese CNN) for fingerprint recognition, structured as follows: This blog outlines how we implemented this in Python & Keras, while dealing with dataset augmentation, Siamese architecture, and model validation. You can explore the project’s Git repo and Jupyter Notebooks.
Published:
In recent years, there has been a surge in research in classical Molecular Dynamics and force-field parameterization using advanced machine learning like Neural Networks. Since this has been the topic of my PhD work, I wanted to explore the field and try to summirize recent advancing in this field. For this I wanted to test Gemini Research feature. Thus, this post is written by Gemini, and the results are very interesting.
Published:
In recent years, there has been a surge in research in classical Molecular Dynamics and force-field parameterization using advanced machine learning like Neural Networks. Since this has been the topic of my PhD work, I wanted to explore the field and try to summirize recent advancing in this field. For this I wanted to test Gemini Research feature. Thus, this post is written by Gemini, and the results are very interesting.
Published:
In analytics engineering, tooling discussions are often presented as an either-or choice: use a transactional database like Postgres, or go all-in on a distributed engine like Spark. In practice, though, a huge share of analytical work lives somewhere in the middle. The data easily fits on a laptop or a single VM, but the queries themselves are anything but trivial—wide tables, joins across multiple fact datasets, window functions, and time-based aggregations are the norm.
Published:
Business Intelligence (BI) tools have become mission-critical for data-driven organizations. They enable analysts and decision-makers to transform raw data into actionable insights via dashboards, reports, and visual storytelling. The landscape spans from commercial platforms widely adopted in enterprises to open-source engines popular in technical environments. But in 2026, the Business Intelligence (BI) landscape is no longer just about “making charts.” It is a battle between ecosystem locked-in giants and the rising tide of open-source flexibility. Whether you are a startup founder in Berlin or a data head at a Fortune 500 in New York, choosing the right tool determines how quickly your data turns into a competitive advantage. Let’s take a dive into most used BI tools. Check out this cool interactive webpage that I created for this purpose
Published:
In recent years, there has been a surge in research in classical Molecular Dynamics and force-field parameterization using advanced machine learning like Neural Networks. Since this has been the topic of my PhD work, I wanted to explore the field and try to summirize recent advancing in this field. For this I wanted to test Gemini Research feature. Thus, this post is written by Gemini, and the results are very interesting.
Published:
Sepsis. The word itself carries a weight of urgency. Learning that this condition, recognized as a global health priority by the World Health Assembly, is essentially our body’s own defense system going haywire in response to an infection – leading to potential widespread damage and even death [1] – really struck a chord with me. Millions affected globally each year, and the stark reality that every hour of delayed treatment increases mortality risk [2]… it’s a problem screaming for solutions.
Published:
In recent years, there has been a surge in research in classical Molecular Dynamics and force-field parameterization using advanced machine learning like Neural Networks. Since this has been the topic of my PhD work, I wanted to explore the field and try to summirize recent advancing in this field. For this I wanted to test Gemini Research feature. Thus, this post is written by Gemini, and the results are very interesting.
Published:
Power BI dashboards rarely start complex. A dataset here, a couple of visuals there, maybe a slicer or two. But give it a few weeks and new measures, refreshed logic, stakeholder tweaks, performance tuning, etc, and suddenly that “simple report” is business-critical.
Published:
A well-constructed portfolio is your golden ticket to showcasing your data analysis skills and landing your dream job. It serves as a window into your expertise, showing potential employers not just what you’ve done but how you think, solve problems, and communicate results. Let’s break down the essential elements of a standout data analyst portfolio and explore how to build one that truly stands out.
Published:
As data analyst, people often ask me where do I spend most of my work time, besides scrolling through the internet. When people think about data analysis, they often imagine building predictive models or creating dazzling visualizations. But beneath the surface of every successful data project lies an essential yet often underestimated step: data cleaning. This critical process lays the foundation for trustworthy insights, making it one of the most valuable skills for any data professional.
Published:
We are entering the age of Agentic Analytics. As I’ve recently written in my previous 2 posts, with the introduction of the Model Context Protocol (MCP), we’ve moved from “chatting about data” to “deploying agents into our models.” Imagine an AI that doesn’t just suggest a formula, but actually opens your .pbix file, creates your measures, organizes your folders, hides your technical keys, and writes your documentation—all while you watch.
Published:
In the last post I promised a tutorial on how to set up PowerBI connection with LLMs using MCP server. This page provides a step-by-step guide on how to use the Model Context Protocol (MCP) to connect Power BI to AI models for development while maintaining data privacy.
Published:
Lately, it feels like we can’t talk about data without AI taking center stage. For Power BI developers, we’re at a turning point—one where AI can actually build your measures, set up relationships, and do real development work. Not by you copying DAX code into your model, but by AI directly modifying your semantic model while you watch.
Published:
Power BI dashboards rarely start complex. A dataset here, a couple of visuals there, maybe a slicer or two. But give it a few weeks and new measures, refreshed logic, stakeholder tweaks, performance tuning, etc, and suddenly that “simple report” is business-critical.
Published:
Business Intelligence (BI) tools have become mission-critical for data-driven organizations. They enable analysts and decision-makers to transform raw data into actionable insights via dashboards, reports, and visual storytelling. The landscape spans from commercial platforms widely adopted in enterprises to open-source engines popular in technical environments. But in 2026, the Business Intelligence (BI) landscape is no longer just about “making charts.” It is a battle between ecosystem locked-in giants and the rising tide of open-source flexibility. Whether you are a startup founder in Berlin or a data head at a Fortune 500 in New York, choosing the right tool determines how quickly your data turns into a competitive advantage. Let’s take a dive into most used BI tools. Check out this cool interactive webpage that I created for this purpose
Published:
I’ve been working in Automotive manufacturing for more then a year now and there is one concept that is holy grail in this industry, and that is Six Sigma. It’s a methodology for achieving near-perfect quality in manufacturing. But how can you could leverage Six Sigma tools right from your Python environment? That’s where the manufacturing package comes in.
Published:
I’ll be honest, I’ve always been a bit of a Databricks fanboy. Their approach to solving data problems is just elegant. The way they’ve pushed the lakehouse architecture forward, their genuinely thoughtful implementation of AI features, the speed of their platform, it all just clicks. So when my company asked me to get certified (quotas, you know how it goes), I was actually excited about it.
Published:
A well-constructed portfolio is your golden ticket to showcasing your data analysis skills and landing your dream job. It serves as a window into your expertise, showing potential employers not just what you’ve done but how you think, solve problems, and communicate results. Let’s break down the essential elements of a standout data analyst portfolio and explore how to build one that truly stands out.
Published:
As data analyst, people often ask me where do I spend most of my work time, besides scrolling through the internet. When people think about data analysis, they often imagine building predictive models or creating dazzling visualizations. But beneath the surface of every successful data project lies an essential yet often underestimated step: data cleaning. This critical process lays the foundation for trustworthy insights, making it one of the most valuable skills for any data professional.
Published:
I’ve been working in Automotive manufacturing for more then a year now and there is one concept that is holy grail in this industry, and that is Six Sigma. It’s a methodology for achieving near-perfect quality in manufacturing. But how can you could leverage Six Sigma tools right from your Python environment? That’s where the manufacturing package comes in.
Published:
I’ve been working in Automotive manufacturing for more then a year now and there is one concept that is holy grail in this industry, and that is Six Sigma. It’s a methodology for achieving near-perfect quality in manufacturing. But how can you could leverage Six Sigma tools right from your Python environment? That’s where the manufacturing package comes in.
Published:
In analytics engineering, tooling discussions are often presented as an either-or choice: use a transactional database like Postgres, or go all-in on a distributed engine like Spark. In practice, though, a huge share of analytical work lives somewhere in the middle. The data easily fits on a laptop or a single VM, but the queries themselves are anything but trivial—wide tables, joins across multiple fact datasets, window functions, and time-based aggregations are the norm.
Published:
The idea for this project stemmed from a collaboration with my friend Jovan on his Bachelor’s thesis. His concept was to use a Siamese Convolutional Neural Network (Siamese CNN) for fingerprint recognition, structured as follows: This blog outlines how we implemented this in Python & Keras, while dealing with dataset augmentation, Siamese architecture, and model validation. You can explore the project’s Git repo and Jupyter Notebooks.
Published:
I’ve been working in Automotive manufacturing for more then a year now and there is one concept that is holy grail in this industry, and that is Six Sigma. It’s a methodology for achieving near-perfect quality in manufacturing. But how can you could leverage Six Sigma tools right from your Python environment? That’s where the manufacturing package comes in.
Published:
Business Intelligence (BI) tools have become mission-critical for data-driven organizations. They enable analysts and decision-makers to transform raw data into actionable insights via dashboards, reports, and visual storytelling. The landscape spans from commercial platforms widely adopted in enterprises to open-source engines popular in technical environments. But in 2026, the Business Intelligence (BI) landscape is no longer just about “making charts.” It is a battle between ecosystem locked-in giants and the rising tide of open-source flexibility. Whether you are a startup founder in Berlin or a data head at a Fortune 500 in New York, choosing the right tool determines how quickly your data turns into a competitive advantage. Let’s take a dive into most used BI tools. Check out this cool interactive webpage that I created for this purpose
Published:
Business Intelligence (BI) tools have become mission-critical for data-driven organizations. They enable analysts and decision-makers to transform raw data into actionable insights via dashboards, reports, and visual storytelling. The landscape spans from commercial platforms widely adopted in enterprises to open-source engines popular in technical environments. But in 2026, the Business Intelligence (BI) landscape is no longer just about “making charts.” It is a battle between ecosystem locked-in giants and the rising tide of open-source flexibility. Whether you are a startup founder in Berlin or a data head at a Fortune 500 in New York, choosing the right tool determines how quickly your data turns into a competitive advantage. Let’s take a dive into most used BI tools. Check out this cool interactive webpage that I created for this purpose
Published:
I’ll be honest, I’ve always been a bit of a Databricks fanboy. Their approach to solving data problems is just elegant. The way they’ve pushed the lakehouse architecture forward, their genuinely thoughtful implementation of AI features, the speed of their platform, it all just clicks. So when my company asked me to get certified (quotas, you know how it goes), I was actually excited about it.
Published:
We are entering the age of Agentic Analytics. As I’ve recently written in my previous 2 posts, with the introduction of the Model Context Protocol (MCP), we’ve moved from “chatting about data” to “deploying agents into our models.” Imagine an AI that doesn’t just suggest a formula, but actually opens your .pbix file, creates your measures, organizes your folders, hides your technical keys, and writes your documentation—all while you watch.
Published:
Santiago Valdarrama recently called out a trend many of us have felt firsthand: serious testing is quietly slipping out of software development. As he noted, we’re in an age of impressive demos, where slick presentations often matter more than whether the code is actually solid. This shows up especially when working with Large Language Models (LLMs) or doing what’s commonly called “vibe-coding”—an exploratory, trial-and-error style of writing code.
Published:
For data scientists and analysts, basic SQL queries are just the starting point. To truly unlock the power of databases and perform complex analyses, you need to delve into advanced constructs. This blog post explores five essential techniques: Subqueries, Common Table Expressions (CTEs), Views, Temporary Tables, and Create Table As Select (CTAS). These tools enable you to write more efficient, readable, and powerful SQL code.
Published:
A while back, I was given an interesting assignment: build a model to detect anomalous HTTP requests. The goal was to identify malicious web traffic by analyzing patterns in normal and anomalous requests. This led me to explore the CSIC 2010 dataset, a well-known benchmark for HTTP anomaly detection.
Published:
Behind every robust data pipeline or analytics project lies a powerful foundation of automation and efficient data handling. While high-level tools like SQL engines and data visualization platforms get much of the spotlight, it’s often the low-level tools—like Bash scripts, rsync, find, and others—that keep the data world running smoothly.
Published:
As a data scientist who is supposed to be working closely with developers, I constantly find myself forgetting Git commands, especially when switching between feature branches, stashing changes, or pushing to remotes. While Git is integrated into VSCode and offers a visual module for staging, committing, and syncing, I still prefer the command line. It gives me more control and a clearer understanding of what’s happening under the hood.
Published:
Hey there!
Published:
I’ll be honest, I’ve always been a bit of a Databricks fanboy. Their approach to solving data problems is just elegant. The way they’ve pushed the lakehouse architecture forward, their genuinely thoughtful implementation of AI features, the speed of their platform, it all just clicks. So when my company asked me to get certified (quotas, you know how it goes), I was actually excited about it.
Published:
Behind every robust data pipeline or analytics project lies a powerful foundation of automation and efficient data handling. While high-level tools like SQL engines and data visualization platforms get much of the spotlight, it’s often the low-level tools—like Bash scripts, rsync, find, and others—that keep the data world running smoothly.
Published:
As a data scientist who is supposed to be working closely with developers, I constantly find myself forgetting Git commands, especially when switching between feature branches, stashing changes, or pushing to remotes. While Git is integrated into VSCode and offers a visual module for staging, committing, and syncing, I still prefer the command line. It gives me more control and a clearer understanding of what’s happening under the hood.
Published:
Data is everywhere, like a river flowing into a city. But raw data, like river water, isn’t always ready to use. We need to clean it, process it, and get it where it needs to go so it can be helpful. That’s why data pipelines are important.
Published:
As a data scientist who is supposed to be working closely with developers, I constantly find myself forgetting Git commands, especially when switching between feature branches, stashing changes, or pushing to remotes. While Git is integrated into VSCode and offers a visual module for staging, committing, and syncing, I still prefer the command line. It gives me more control and a clearer understanding of what’s happening under the hood.
Published:
Behind every robust data pipeline or analytics project lies a powerful foundation of automation and efficient data handling. While high-level tools like SQL engines and data visualization platforms get much of the spotlight, it’s often the low-level tools—like Bash scripts, rsync, find, and others—that keep the data world running smoothly.
Published:
Data is everywhere, like a river flowing into a city. But raw data, like river water, isn’t always ready to use. We need to clean it, process it, and get it where it needs to go so it can be helpful. That’s why data pipelines are important.
Published:
I’m in the job hunting mode at the moment and one thing has become crystal clear: presenting a portfolio of projects in a clean, professional, and industry-standard format is crucial. My own journey of wrangling personal projects – juggling data, code, notebooks, models, and results – highlighted the need for better organization and reproducibility. How do you transform scattered scripts and notebooks into something easily understandable and verifiable by potential employers or collaborators? The answer lies in standardized project structures and robust version control.
Published:
In the world of data analysis, SQL (Structured Query Language) is the fundamental rock. It’s the language that allows you to communicate with databases, extracting, manipulating, and analyzing data with precision. Whether you’re a seasoned analyst or just starting your journey, a solid grasp of SQL is essential for uncovering meaningful patterns and driving data-informed decisions. This blog post will cover the fundamental concepts of SQL, basic query structures, and the software tools that empower data analysts. But first let’s answer what is a database and what are two main categories of databases.
Published:
In today’s data-driven world, choosing the right storage protocol and architecture is crucial for performance, scalability, and cost efficiency. Over the years, I’ve worked with various storage systems—from NoSQL databases during my PhD to CRM systems, data warehouses, and data lakes in manufacturing. In this post, I’ll break down key storage protocols (NFS, SMB, S3) and explain the differences between data warehouses, data lakes, and the emerging data lakehouse paradigm.
Published:
Behind every robust data pipeline or analytics project lies a powerful foundation of automation and efficient data handling. While high-level tools like SQL engines and data visualization platforms get much of the spotlight, it’s often the low-level tools—like Bash scripts, rsync, find, and others—that keep the data world running smoothly.
Published:
Data is everywhere, like a river flowing into a city. But raw data, like river water, isn’t always ready to use. We need to clean it, process it, and get it where it needs to go so it can be helpful. That’s why data pipelines are important.
Published:
AI is rapidly transforming industries, but at its core lies a critical foundation: data. The quality, organization, and governance of this data directly impact the success and reliability of AI models. This blog post explores what I know and what I’ve learned about the essentials of data management and its integration with AI, from fundamental concepts to practical principles and crucial considerations around security and privacy.
Published:
In today’s data-driven world, choosing the right storage protocol and architecture is crucial for performance, scalability, and cost efficiency. Over the years, I’ve worked with various storage systems—from NoSQL databases during my PhD to CRM systems, data warehouses, and data lakes in manufacturing. In this post, I’ll break down key storage protocols (NFS, SMB, S3) and explain the differences between data warehouses, data lakes, and the emerging data lakehouse paradigm.
Published:
AI is rapidly transforming industries, but at its core lies a critical foundation: data. The quality, organization, and governance of this data directly impact the success and reliability of AI models. This blog post explores what I know and what I’ve learned about the essentials of data management and its integration with AI, from fundamental concepts to practical principles and crucial considerations around security and privacy.
Published:
Data is everywhere, like a river flowing into a city. But raw data, like river water, isn’t always ready to use. We need to clean it, process it, and get it where it needs to go so it can be helpful. That’s why data pipelines are important.
Published:
I’m in the job hunting mode at the moment and one thing has become crystal clear: presenting a portfolio of projects in a clean, professional, and industry-standard format is crucial. My own journey of wrangling personal projects – juggling data, code, notebooks, models, and results – highlighted the need for better organization and reproducibility. How do you transform scattered scripts and notebooks into something easily understandable and verifiable by potential employers or collaborators? The answer lies in standardized project structures and robust version control.
Published:
In today’s data-driven world, choosing the right storage protocol and architecture is crucial for performance, scalability, and cost efficiency. Over the years, I’ve worked with various storage systems—from NoSQL databases during my PhD to CRM systems, data warehouses, and data lakes in manufacturing. In this post, I’ll break down key storage protocols (NFS, SMB, S3) and explain the differences between data warehouses, data lakes, and the emerging data lakehouse paradigm.
Published:
One of the most consistently expected skills for a Data Scientist today is A/B testing, often referred to as split testing. While it’s sometimes described as a simple optimization technique, in practice it’s much closer to applied science where you have to translate vague business questions into testable hypotheses, design robust experiments, and eventually, turn results into production-ready decisions.
Published:
One of the most consistently expected skills for a Data Scientist today is A/B testing, often referred to as split testing. While it’s sometimes described as a simple optimization technique, in practice it’s much closer to applied science where you have to translate vague business questions into testable hypotheses, design robust experiments, and eventually, turn results into production-ready decisions.
Published:
In analytics engineering, tooling discussions are often presented as an either-or choice: use a transactional database like Postgres, or go all-in on a distributed engine like Spark. In practice, though, a huge share of analytical work lives somewhere in the middle. The data easily fits on a laptop or a single VM, but the queries themselves are anything but trivial—wide tables, joins across multiple fact datasets, window functions, and time-based aggregations are the norm.
Published:
In the world of data analysis, SQL (Structured Query Language) is the fundamental rock. It’s the language that allows you to communicate with databases, extracting, manipulating, and analyzing data with precision. Whether you’re a seasoned analyst or just starting your journey, a solid grasp of SQL is essential for uncovering meaningful patterns and driving data-informed decisions. This blog post will cover the fundamental concepts of SQL, basic query structures, and the software tools that empower data analysts. But first let’s answer what is a database and what are two main categories of databases.
Published:
As a data scientist who is supposed to be working closely with developers, I constantly find myself forgetting Git commands, especially when switching between feature branches, stashing changes, or pushing to remotes. While Git is integrated into VSCode and offers a visual module for staging, committing, and syncing, I still prefer the command line. It gives me more control and a clearer understanding of what’s happening under the hood.
Published:
The idea for this project stemmed from a collaboration with my friend Jovan on his Bachelor’s thesis. His concept was to use a Siamese Convolutional Neural Network (Siamese CNN) for fingerprint recognition, structured as follows: This blog outlines how we implemented this in Python & Keras, while dealing with dataset augmentation, Siamese architecture, and model validation. You can explore the project’s Git repo and Jupyter Notebooks.
Published:
Hey there!
Published:
In recent years, there has been a surge in research in classical Molecular Dynamics and force-field parameterization using advanced machine learning like Neural Networks. Since this has been the topic of my PhD work, I wanted to explore the field and try to summirize recent advancing in this field. For this I wanted to test Gemini Research feature. Thus, this post is written by Gemini, and the results are very interesting.
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A while back, I was given an interesting assignment: build a model to detect anomalous HTTP requests. The goal was to identify malicious web traffic by analyzing patterns in normal and anomalous requests. This led me to explore the CSIC 2010 dataset, a well-known benchmark for HTTP anomaly detection.
Published:
As a data scientist who is supposed to be working closely with developers, I constantly find myself forgetting Git commands, especially when switching between feature branches, stashing changes, or pushing to remotes. While Git is integrated into VSCode and offers a visual module for staging, committing, and syncing, I still prefer the command line. It gives me more control and a clearer understanding of what’s happening under the hood.
Published:
I’m in the job hunting mode at the moment and one thing has become crystal clear: presenting a portfolio of projects in a clean, professional, and industry-standard format is crucial. My own journey of wrangling personal projects – juggling data, code, notebooks, models, and results – highlighted the need for better organization and reproducibility. How do you transform scattered scripts and notebooks into something easily understandable and verifiable by potential employers or collaborators? The answer lies in standardized project structures and robust version control.
Published:
A while back, I was given an interesting assignment: build a model to detect anomalous HTTP requests. The goal was to identify malicious web traffic by analyzing patterns in normal and anomalous requests. This led me to explore the CSIC 2010 dataset, a well-known benchmark for HTTP anomaly detection.
Published:
One of the most consistently expected skills for a Data Scientist today is A/B testing, often referred to as split testing. While it’s sometimes described as a simple optimization technique, in practice it’s much closer to applied science where you have to translate vague business questions into testable hypotheses, design robust experiments, and eventually, turn results into production-ready decisions.
Published:
Data is everywhere, like a river flowing into a city. But raw data, like river water, isn’t always ready to use. We need to clean it, process it, and get it where it needs to go so it can be helpful. That’s why data pipelines are important.
Published:
I’m in the job hunting mode at the moment and one thing has become crystal clear: presenting a portfolio of projects in a clean, professional, and industry-standard format is crucial. My own journey of wrangling personal projects – juggling data, code, notebooks, models, and results – highlighted the need for better organization and reproducibility. How do you transform scattered scripts and notebooks into something easily understandable and verifiable by potential employers or collaborators? The answer lies in standardized project structures and robust version control.
Published:
AI is rapidly transforming industries, but at its core lies a critical foundation: data. The quality, organization, and governance of this data directly impact the success and reliability of AI models. This blog post explores what I know and what I’ve learned about the essentials of data management and its integration with AI, from fundamental concepts to practical principles and crucial considerations around security and privacy.
Published:
Sepsis. The word itself carries a weight of urgency. Learning that this condition, recognized as a global health priority by the World Health Assembly, is essentially our body’s own defense system going haywire in response to an infection – leading to potential widespread damage and even death [1] – really struck a chord with me. Millions affected globally each year, and the stark reality that every hour of delayed treatment increases mortality risk [2]… it’s a problem screaming for solutions.
Published:
A while back, I was given an interesting assignment: build a model to detect anomalous HTTP requests. The goal was to identify malicious web traffic by analyzing patterns in normal and anomalous requests. This led me to explore the CSIC 2010 dataset, a well-known benchmark for HTTP anomaly detection.
Published:
I’ve been working in Automotive manufacturing for more then a year now and there is one concept that is holy grail in this industry, and that is Six Sigma. It’s a methodology for achieving near-perfect quality in manufacturing. But how can you could leverage Six Sigma tools right from your Python environment? That’s where the manufacturing package comes in.
Published:
I’m in the job hunting mode at the moment and one thing has become crystal clear: presenting a portfolio of projects in a clean, professional, and industry-standard format is crucial. My own journey of wrangling personal projects – juggling data, code, notebooks, models, and results – highlighted the need for better organization and reproducibility. How do you transform scattered scripts and notebooks into something easily understandable and verifiable by potential employers or collaborators? The answer lies in standardized project structures and robust version control.
Published:
Sepsis. The word itself carries a weight of urgency. Learning that this condition, recognized as a global health priority by the World Health Assembly, is essentially our body’s own defense system going haywire in response to an infection – leading to potential widespread damage and even death [1] – really struck a chord with me. Millions affected globally each year, and the stark reality that every hour of delayed treatment increases mortality risk [2]… it’s a problem screaming for solutions.
Published:
AI is rapidly transforming industries, but at its core lies a critical foundation: data. The quality, organization, and governance of this data directly impact the success and reliability of AI models. This blog post explores what I know and what I’ve learned about the essentials of data management and its integration with AI, from fundamental concepts to practical principles and crucial considerations around security and privacy.
Published:
Hey there!
Published:
Data is everywhere, like a river flowing into a city. But raw data, like river water, isn’t always ready to use. We need to clean it, process it, and get it where it needs to go so it can be helpful. That’s why data pipelines are important.
Published:
Hey there!
Published:
Santiago Valdarrama recently called out a trend many of us have felt firsthand: serious testing is quietly slipping out of software development. As he noted, we’re in an age of impressive demos, where slick presentations often matter more than whether the code is actually solid. This shows up especially when working with Large Language Models (LLMs) or doing what’s commonly called “vibe-coding”—an exploratory, trial-and-error style of writing code.
Published:
In analytics engineering, tooling discussions are often presented as an either-or choice: use a transactional database like Postgres, or go all-in on a distributed engine like Spark. In practice, though, a huge share of analytical work lives somewhere in the middle. The data easily fits on a laptop or a single VM, but the queries themselves are anything but trivial—wide tables, joins across multiple fact datasets, window functions, and time-based aggregations are the norm.
Published:
Santiago Valdarrama recently called out a trend many of us have felt firsthand: serious testing is quietly slipping out of software development. As he noted, we’re in an age of impressive demos, where slick presentations often matter more than whether the code is actually solid. This shows up especially when working with Large Language Models (LLMs) or doing what’s commonly called “vibe-coding”—an exploratory, trial-and-error style of writing code.
Published:
In the world of data analysis, SQL (Structured Query Language) is the fundamental rock. It’s the language that allows you to communicate with databases, extracting, manipulating, and analyzing data with precision. Whether you’re a seasoned analyst or just starting your journey, a solid grasp of SQL is essential for uncovering meaningful patterns and driving data-informed decisions. This blog post will cover the fundamental concepts of SQL, basic query structures, and the software tools that empower data analysts. But first let’s answer what is a database and what are two main categories of databases.
Published:
The idea for this project stemmed from a collaboration with my friend Jovan on his Bachelor’s thesis. His concept was to use a Siamese Convolutional Neural Network (Siamese CNN) for fingerprint recognition, structured as follows: This blog outlines how we implemented this in Python & Keras, while dealing with dataset augmentation, Siamese architecture, and model validation. You can explore the project’s Git repo and Jupyter Notebooks.
Published:
Behind every robust data pipeline or analytics project lies a powerful foundation of automation and efficient data handling. While high-level tools like SQL engines and data visualization platforms get much of the spotlight, it’s often the low-level tools—like Bash scripts, rsync, find, and others—that keep the data world running smoothly.
Published:
Behind every robust data pipeline or analytics project lies a powerful foundation of automation and efficient data handling. While high-level tools like SQL engines and data visualization platforms get much of the spotlight, it’s often the low-level tools—like Bash scripts, rsync, find, and others—that keep the data world running smoothly.
Published:
AI is rapidly transforming industries, but at its core lies a critical foundation: data. The quality, organization, and governance of this data directly impact the success and reliability of AI models. This blog post explores what I know and what I’ve learned about the essentials of data management and its integration with AI, from fundamental concepts to practical principles and crucial considerations around security and privacy.
Published:
Sepsis. The word itself carries a weight of urgency. Learning that this condition, recognized as a global health priority by the World Health Assembly, is essentially our body’s own defense system going haywire in response to an infection – leading to potential widespread damage and even death [1] – really struck a chord with me. Millions affected globally each year, and the stark reality that every hour of delayed treatment increases mortality risk [2]… it’s a problem screaming for solutions.
Published:
For data scientists and analysts, basic SQL queries are just the starting point. To truly unlock the power of databases and perform complex analyses, you need to delve into advanced constructs. This blog post explores five essential techniques: Subqueries, Common Table Expressions (CTEs), Views, Temporary Tables, and Create Table As Select (CTAS). These tools enable you to write more efficient, readable, and powerful SQL code.
Published:
In the world of data analysis, SQL (Structured Query Language) is the fundamental rock. It’s the language that allows you to communicate with databases, extracting, manipulating, and analyzing data with precision. Whether you’re a seasoned analyst or just starting your journey, a solid grasp of SQL is essential for uncovering meaningful patterns and driving data-informed decisions. This blog post will cover the fundamental concepts of SQL, basic query structures, and the software tools that empower data analysts. But first let’s answer what is a database and what are two main categories of databases.
Published:
Behind every robust data pipeline or analytics project lies a powerful foundation of automation and efficient data handling. While high-level tools like SQL engines and data visualization platforms get much of the spotlight, it’s often the low-level tools—like Bash scripts, rsync, find, and others—that keep the data world running smoothly.
Published:
In today’s data-driven world, choosing the right storage protocol and architecture is crucial for performance, scalability, and cost efficiency. Over the years, I’ve worked with various storage systems—from NoSQL databases during my PhD to CRM systems, data warehouses, and data lakes in manufacturing. In this post, I’ll break down key storage protocols (NFS, SMB, S3) and explain the differences between data warehouses, data lakes, and the emerging data lakehouse paradigm.
Published:
For data scientists and analysts, basic SQL queries are just the starting point. To truly unlock the power of databases and perform complex analyses, you need to delve into advanced constructs. This blog post explores five essential techniques: Subqueries, Common Table Expressions (CTEs), Views, Temporary Tables, and Create Table As Select (CTAS). These tools enable you to write more efficient, readable, and powerful SQL code.
Published:
Santiago Valdarrama recently called out a trend many of us have felt firsthand: serious testing is quietly slipping out of software development. As he noted, we’re in an age of impressive demos, where slick presentations often matter more than whether the code is actually solid. This shows up especially when working with Large Language Models (LLMs) or doing what’s commonly called “vibe-coding”—an exploratory, trial-and-error style of writing code.
Published:
Business Intelligence (BI) tools have become mission-critical for data-driven organizations. They enable analysts and decision-makers to transform raw data into actionable insights via dashboards, reports, and visual storytelling. The landscape spans from commercial platforms widely adopted in enterprises to open-source engines popular in technical environments. But in 2026, the Business Intelligence (BI) landscape is no longer just about “making charts.” It is a battle between ecosystem locked-in giants and the rising tide of open-source flexibility. Whether you are a startup founder in Berlin or a data head at a Fortune 500 in New York, choosing the right tool determines how quickly your data turns into a competitive advantage. Let’s take a dive into most used BI tools. Check out this cool interactive webpage that I created for this purpose
Published:
A while back, I was given an interesting assignment: build a model to detect anomalous HTTP requests. The goal was to identify malicious web traffic by analyzing patterns in normal and anomalous requests. This led me to explore the CSIC 2010 dataset, a well-known benchmark for HTTP anomaly detection.
Published:
Santiago Valdarrama recently called out a trend many of us have felt firsthand: serious testing is quietly slipping out of software development. As he noted, we’re in an age of impressive demos, where slick presentations often matter more than whether the code is actually solid. This shows up especially when working with Large Language Models (LLMs) or doing what’s commonly called “vibe-coding”—an exploratory, trial-and-error style of writing code.
Published:
For data scientists and analysts, basic SQL queries are just the starting point. To truly unlock the power of databases and perform complex analyses, you need to delve into advanced constructs. This blog post explores five essential techniques: Subqueries, Common Table Expressions (CTEs), Views, Temporary Tables, and Create Table As Select (CTAS). These tools enable you to write more efficient, readable, and powerful SQL code.
Published:
In this blog post, we will discuss the implementation of AI-powered voice and video agents using the Livekit platform. Our experience demonstrates that setting up these agents is a straightforward process, especially with the comprehensive documentation and tutorials available on the Livekit website. We have successfully implemented two versions of these agents: one focused solely on voice interaction and another that incorporates both voice and visual assistance.