The Legit Baines Blog

A Tale of Two Job Specifications  👍👎 Recently I came across two job specifications for roles that were broadly related to data and information systems . On the surface they looked similar enough, both requiring analytical thinking, technical skills and the ability to support organisational objectives. However, once I started reading them closely, the difference in quality between the two documents became immediately obvious. One specification was so vague that I found myself writing comments all over it in red, while the other provided genuine insight into the role and the technologies involved. It was a perfect example of how a well-written job specification can help both employers and candidates. The first job specification described the responsibilities of a Senior Analyst role. It contained plenty of corporate language about supporting stakeholders, analysing data, promoting service improvement and contributing to organisational objectives. While none of these requirements we...

 Is It Time to Move On From VS Code? VS code VS Pycharm/ For the past few years, Visual Studio Code has been my primary development environment. Like many developers, I appreciated its flexibility, extensive plugin ecosystem, and the fact that it could be adapted to almost any programming language or workflow. However, after one of the recent updates, I found myself spending more time fighting with the IDE than actually writing code. What was once a lightweight and straightforward editor now felt increasingly cluttered and distracting.     The biggest issue for me wasn't that new features had been added. Software evolves, and that's a good thing. The problem was that VS Code seemed to be constantly trying to anticipate what I wanted to do before I'd even clicked on anything. Panels would appear, suggestions would pop up, AI features would offer advice, and various automated tools would spring into action. While some developers may find this helpful, I found it disr...

  Why Passion and Personal Investment Create Better Problem Solvers One of the biggest mistakes organisations make when hiring is focusing entirely on technical ability while overlooking whether the person actually cares about the problems they are being asked to solve. Technical skills are important, but genuine investment in the outcome is often what separates average employees from exceptional ones. A person who emotionally connects with the purpose behind the work will naturally go further, think deeper and stay motivated for longer than someone who simply sees the role as a pay cheque. This becomes especially important in analytical and technical roles. For example, if a company is hiring a data analyst to work with information related to medical conditions, ideally they should look for someone who genuinely cares about helping people affected by those conditions. A technically skilled analyst may be able to produce dashboards and reports, but someone who is personally invest...

  The Pitfalls, Opportunities and Best Practices of Starting Your Own IT Start-Up Starting your own IT start-up can be one of the most exciting and rewarding challenges you will ever undertake, but it is also one of the easiest ways to burn through time, money and energy if you approach it without a realistic plan. Many people enter the technology sector believing that having a good idea is enough, when in reality a successful start-up is built on execution, resilience and the ability to solve real-world problems consistently. The modern IT industry offers huge opportunities in areas such as cloud computing , cyber security , AI , automation, accessibility software and embedded systems, but competition is fierce and customers have become far more demanding. The first lesson every founder learns is that technology alone is not enough; you must also understand people, communication, finance and long-term sustainability. One of the biggest pitfalls for new founders is building a pr...

 Why We Need to Rethink Feedback, Criticism, and Being Told We’re Wrong  There is something fundamentally broken in the way many of us think about feedback, criticism, and being told we’re wrong. For a lot of people, these things feel uncomfortable, even personal. We tend to avoid them, soften them, or remove them entirely. But in doing so, we are also removing one of the most important mechanisms for growth. If no one ever tells you that you’re doing something wrong, how are you supposed to get better at it? I’ve experienced this problem firsthand through applying for a wide range of roles with Sheffield City Council . These roles span different departments and skillsets, and on paper, many organisations like this emphasise inclusivity and openness to candidates from alternative backgrounds. That sounds great in theory. But in practice, I’ve consistently received little to no feedback when unsuccessful . Just a rejection and that’s the end of the process. The issue here isn’t...

I have always been good with numbers, when I was at school I did my Maths GCSE a year early and got a B. Unfortunately I had a lot of bad things going on in my life at the time so I didn’t go forward with University at that time.  Fast forward a decade or two and now I’m a recent graduate from Hallam University , I went there as a mature student to do a degree in computing and the course involved loads of data related modules. During my Computing course at Hallam University I spent a lot of time learning about data, databases, data cleaning , data processing, data management, data analytics and data visualisations . The course also involved a number of assignments that included a power point presentations in front of tutors and answering their questions about the work afterwards. Presentations were something I was very comfortable doing. I learned to collect raw sensor data using a Raspberry Pin (or from a pico using micropython/circuitpython), I learned that it is absolu...

  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...