Baines Blogging

 💻💻💻 Staying health- and safety-aware can be surprisingly difficult when your job keeps you glued to a screen, focused on reports, dashboards, and deadlines. These simple but effective tips will help you protect your wellbeing while maintaining productivity. 1. Prioritize Proper Desk Ergonomics 💺💺💺 Set up your chair, monitor, and keyboard so your body stays in a neutral, supported position—this reduces strain on your neck, shoulders, and back. 2. Follow the 20-20-20 Rule for Eye Health Every 20 minutes, look at something 20 feet away for 20 seconds to reduce eye strain from extended screen time. 3. Take Scheduled Micro-Breaks Short, regular breaks improve your comfort and concentration—set reminders to stand, stretch, or walk for a minute or two. 4. Keep Your Workspace Clutter-Free A tidy area prevents trip hazards, spilled drinks on electronics, and the stress that comes from a chaotic environment. 5. Stay Hydrated Throughout the Day Drinking enough water keeps yo...

  Test Yourself then Check Yourself Pushing myself to grow has always been at the heart of my journey through the world of IT, computing, and data analytics. These fields evolve at lightning speed, and I’ve learned that staying still is the fastest way to fall behind. Whether it’s a new programming concept, a data modeling technique, or a tool I’ve never touched before, I’m constantly seeking out ways to stretch my abilities. But with that drive comes a responsibility to recognize when I’m approaching my limits. The tech world can be intense, and curiosity can quickly turn into overload if I’m not careful. Early on, I realized that challenging myself doesn’t mean pushing to exhaustion—it means being intentional about growth, not reckless. 🙏🙏🙏 One of the biggest influences on how I manage that balance came from an extracurricular course I once took: “Connecting with Yourself on a Spiritual Level.” It might sound far removed from coding or data analytics, but the meditation te...

  Imperial measurement system converter app  I decided to spend an evening making a unit converter app without using an api to do the heavy lifting for me *and* with a python based gui because I haven't really made anything with one of them before and this is the result: 👍👍👍      I installed Tinkter to get the GUI ( graphical user interface ) This is the GUI it produces when it's running.     The next job is to tidy up the code, it needs it.  Then I want to add some more functionality to it.  I can add volume conversion or weight .  I've always wanted to do some research into old money so I can add that to the app. After I've added a bit more functionality to it I'll find a way to host it on the public internet.     This is my advice to any other programmers out there:  Once you've got something working make the most of it because it's easier to add things onto something that's already working than startin...

  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 Windows ...