Baines Blogging

  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...

📶💻💹 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 he...

 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 closures in JavaScript” or “write a CLI tool in Python.” Small, focused wins a...

  I Graduated with a 2:1 in Computing from Sheffield Hallam University! I'm incredibly proud to share that I’ve officially graduated from Sheffield Hallam University with a 2:1 in BSc Computing! It’s been a challenging, rewarding, and ultimately fulfilling journey, and I’m grateful for all the experiences, both technical and personal, that have shaped me along the way. From learning core computing principles to diving deep into specialist areas, my time at university has equipped me with the tools and confidence to move forward in the tech world. One of the standout modules for me was Data Management. I found it fascinating to explore how data is structured, stored, and retrieved, and how good design can make or break the efficiency of a system. This module really laid the groundwork for many of my later projects, giving me the practical skills and theoretical understanding to handle data effectively and responsibly—something that proved crucial in my final year work. My Artific...

Unlocking the Power of Data Analysis: Tools and Techniques for Understanding User Feedback Data analysis is the backbone of decision-making across countless industries, providing insights that drive business strategy, marketing decisions, and customer experience improvements. With the explosion of user-generated content on platforms like YouTube, analyzing user comments has become a powerful tool to understand public sentiment, track brand reputation, and improve user engagement. Step 1: Data Collection – Pulling User Comments from YouTube Before any analysis can happen, we first need the data. In the case of YouTube, user comments are stored publicly on each video. To collect them, we use the YouTube Data API . This API allows us to programmatically retrieve comment data, such as the text of the comments, the date posted, and user metadata (like user ID or username). Here’s how it works: Set up the API : First, you’ll need to create a project on the Google Cloud Console, enab...