The Legit Baines Blog

  Let’s be honest — when people hear “data analysis” , they often picture spreadsheets, endless rows of numbers, and someone yawning behind a laptop at 2 a.m. But that’s a myth. Data analysis isn’t dull — it’s detective work, creativity, and storytelling rolled into one. If you’ve ever dismissed it as boring, it’s time to think again. Here are 7 reasons why data analysis is anything but boring: 1. You Get to Solve Real-World Mysteries Data analysts are like modern-day detectives. Every dataset hides clues that reveal why something happened or how it could change. Whether it’s uncovering why sales dipped last quarter or predicting the next big trend, you’re constantly piecing together a puzzle — and few things are more satisfying than cracking the case. 2. It’s a Gateway to Understanding Human Behavior Data tells stories about people — what they buy, how they think, where they spend their time. Analysing data gives you a front-row seat to human patterns and decision-making...

🧭 1. Workplace Health & Safety Assessment Purpose  👍 To identify workplace hazards, evaluate associated risks, and plan control measures to maintain a safe and healthy environment for all employees — even in a primarily office or remote data analytics setting. Step 1: Identify Hazards For a data analytics start-up, typical hazards include: Category Examples of Hazards Potential Impact Physical Environment Poor lighting, trip hazards from cables, ergonomic issues Eye strain, back/neck pain, falls Equipment & Electrical Overloaded sockets, faulty devices, overheating laptops Electrical fires, equipment damage Workstation Setup Poor chair posture, screen height Musculoskeletal disorders Psychosocial Workload stress, long screen time, isolation (remote workers) Burnout, reduced mental well-being Fire Safety Inaccessible exits, lack of fire extinguisher Injury, property damage Health & Hygiene Poor ventilation, inadequate first ai...

Beginners guide for using the Machine Learning and/or AI tools on google.colab for a complete beginner:  AI and ML is all about analysing data so first you need to decide what kind of data you want to analyse. ( I refer you back to my previous blog post beginners guide to AI and ML terms. .) Basically do you want to analyse numerical data , text or images ? On this occasion I’m going to keep it simple and create some data visualisations  (graphs) You can get free open source data from this website kaggle.com I downloaded this file in .csv format: s-p-500-time-series-forecasting-with-prophet/input   Next find this website colab.research.google.com https://colab.research.google.com This is when you can go to chatgpt and ask it to write the code for google colab to analyse the data in  the way you want for example you could ask it to write the code to create a dataframe from that data:    Drag and drop the csv file into google.colab Change the labels s...

1. Why a Proper Setup Matters Before diving into code, a structured setup helps you avoid “spaghetti” projects that become hard to maintain. Using a virtual environment ensures your dependencies are isolated (so you don’t clash with system Python or other projects). Having a clear skeleton (separating imports, variables, functions, and “main program logic”) gives you and future readers a map of where each piece lives. Many software engineering experts emphasize that good architecture up front can save you enormous friction later — clean structure is one of the foundations of maintainability . ( Ciklum ) 2. Creating a Virtual Environment  💾💻💹 Here’s a typical workflow: Open your terminal / command prompt in your project folder (or create a new folder). Run (for Python 3): python3 -m venv venv This creates a directory venv/ (or whatever you name it) containing the isolated Python environment. Activate it: On macOS / Linux: source venv/bin/activate On Window...

📶💻💹 Artificial Intelligence (AI) and Machine Learning (ML) are terms that are used by a lot of people but do you know about the technical nuts and bolts of how they actually work? If you don't that's OK, — I’ve broken down some of the most common terms you might hear in the world of AI/ML into simple explanations anyone can understand. Plus, if you're using Google Colab , there's a built-in AI assistant called Gemini that can help you understand and write code but you need to know the right questions to ask it first.   1. NLP (Natural Language Processing) 🎤🎧 NLP is a branch of AI that helps computers understand and work with human language. Think of it as the technology behind things like chatbots, language translators, and voice assistants like Siri or Alexa. It allows machines to “read” and make sense of text or speech, just like we do. 2. BERT (Bidirectional Encoder Representations from Transformers) BERT is a special AI model developed by Google that ...

If you're a young IT professional, you're probably no stranger to long hours, screen fatigue, and the eternal quest for something meaningful to do outside of GitHub, Jira, and terminal windows. You’ve tried the gym (too sweaty), gaming (you already do that all day), or craft beer tasting (fun, but your wallet is crying). What if I told you there's a hobby that mixes relaxation, social interaction, light physical activity, and just enough competition to keep your brain sharp? Let’s talk about Bar Billiards — the criminally underrated pub game that might just be the perfect hobby for the modern techie. First, What Is Bar Billiards? Bar Billiards is a traditional British pub game that dates back to the 1930s. Unlike regular pool or snooker, Bar Billiards is played on a table with no side or corner pockets. Instead, you pot balls into holes on the table surface, each with different point values, while avoiding the dreaded skittles (tiny pins that act like mines — knock one...

Stay Focused on Raising Yourself to the Next Level Software Development  In the world of tech, it's easy to fall into the trap of comparing yourself to others. Maybe it's the developer on your team who seems to solve problems twice as fast, or that person on Twitter who contributes to five open-source projects and still has time for a blog and a podcast. But the truth is, trying to be the “best” in class is a moving target—and a distracting one. A more sustainable and rewarding mindset is to focus on raising your own level, day by day. Progress, not perfection, is what actually moves you forward. One of the most powerful habits in tech is deliberate practice . It's not just about writing more code; it's about writing it thoughtfully, reflecting on what went wrong, and iterating. Whether you're learning a new language, framework, or debugging a tricky issue, take time to understand why things work the way they do. Set mini-goals for yourself like “understand closur...