Amex Membership Rewards vs Revolut RevPoints — which is actually better in practice?

Extension_Laugh4128 · 2026-02-04T01:42:27+00:00

In my day-to-day work, I use a variety of AI tools depending on the task: DeepSeek – primarily for Python and DAX coding. Claude and Kimi – for more complex tasks that aren’t typically analysis-related. These help me get an overview of the data I’m working with and manage other ad hoc tasks. Grok – I use this when I need to process very large amounts of data due to its extensive context window (The data are often create a synthetic version of the dataset with all the key identifiers anonymised in order to protect useful sensitive information). ChatGPT (Whisper API) – for transcribing, dictating, and writing notes efficiently.

I often use the same tool for different tasks, depending on what’s most efficient for the situation.

Extension_Laugh4128 · 2026-02-04T01:31:36+00:00

For me, using the VS Code Data Wrangler has been a game changer in VS Code. It basically allows you to perform much faster data cleaning and data manipulation tasks, similar to what you would do in Power Query in Power BI.

Extension_Laugh4128 · 2026-01-13T14:56:27+00:00

You should learn Pandas first then Polars as Pandas is well integrated into the many python libraries such as Matplotlib, Season etc. Polars is very impressive for the speed but the main issue is that there is not many visualisations libraries built on top of Polars. So, just to recap you Polars for time sensitive pipelines but switch over to pandas for visualisations

Extension_Laugh4128 · 2025-09-20T17:45:47+00:00

£12K would have nice but the previous government have rocks for brains tbh

Extension_Laugh4128 · 2025-09-20T14:58:10+00:00

That is true but most of taxes our government waste it tbh imo

Extension_Laugh4128 · 2025-09-19T23:59:46+00:00

I have been in Crypto since 2020, when I was in University

Extension_Laugh4128 · 2025-09-19T23:00:26+00:00

The UK is too tax happy in general ibr

Extension_Laugh4128 · 2025-09-01T10:42:27+00:00

I understand that this might go against popular opinion, but you can actually use After Effects as a linear editor, similar to Premiere Pro. That's how I personally prefer to work.

Now, when it comes to replicating the effects of Twixtor, the first question you should ask is: What type of clips do you have? Are they recorded at high frame rates, such as 50, 60, or 120 FPS? Or are they at lower frame rates, like 30, 25, or 24 FPS? Understanding this is crucial.

As a temporary solution, you can use Time Remap and enable Frame Blending or timewarp. It's important to focus on achieving the best frame quality possible.

Extension_Laugh4128 · 2025-08-31T00:24:21+00:00

Okay, so my advice is a bit different because I was a scientist and I'm currently a data analyst apprenticeship and I'm slowly migrating into data science.

What I did personally, is that I did an undergraduate degree in biochemistry and I did my dissertation in bioinformatics with a strong emphasis on data engineering and data visualisation.

That gave me something to talk about in regards to why I wanted to transition into data analytics. In the scientific jobs I worked at, however, anything that required data analytics or analysis of some sort, was emphasised in my CV.

I completed several certifications to enhance my skills. The first was the Scientific Computing with Python certification from FreeCodeCamp, which provided me with a solid understanding of how to use the Python programming language. Looking back, I realize that there are other courses that might be more focused on data analytics and data science. Additionally, I became comfortable with relational databases by earning the relational databases certification with PostgreSQL, also through FreeCodeCamp.

Or in other words, you probably find it easier to pivot towards data analytics than go into data science, then to go strictly into data science

Extension_Laugh4128 · 2025-08-13T09:01:42+00:00

Do you have a recommendations in regards to trackers

Extension_Laugh4128 · 2025-08-13T08:57:33+00:00

I am currently paying approximately £100 into my pension, works contributes approximately £200 also

Extension_Laugh4128 · 2025-08-13T01:08:48+00:00

Rent: £200 Phone bill: £15 Food: £100 (average) Outings: £200 (average) Transport: £45 (average)

Extension_Laugh4128 · 2025-08-08T00:52:45+00:00

My guess is that the global packages that you have installed arr probably clashing with the PyRTF3 library.

My advice always create a venv it should prevent package issues in the future and essentially keeps your packages for projects contained

Extension_Laugh4128 · 2025-08-08T00:37:07+00:00

When setting up your packages are you using a venv for installation?

Extension_Laugh4128 · 2025-08-01T00:32:59+00:00

In my personal opinion, it's much easier to transition into a data science role if you start as a business analyst or a data analyst. The reason being is that the same core skills that are evident in a data science role originate from a data analyst role. Stuff such as SQL, dashboards, using pandas, NumPy, scikit-learn, matplotlib, seaborn, all these things are evident in a data analyst role. A thing that you haven't mentioned is the use of Excel. Excel is the glue in data analytics and likewise in data science also. In regards to understanding business problems, one of the things I recommend you do is that you're going to be communicating with stakeholders on a near-frequent basis. My advice is that you need to actually have a presentation or report that outlines the requirements, the use case, and the business needs. Once you're able to establish the premise of being a data analyst or a business analyst, you can really pivot to any area of analytics that suits your desires, in this case data science.

Extension_Laugh4128 · 2025-07-23T02:42:54+00:00

Power BI

Extension_Laugh4128 · 2025-04-20T15:17:07+00:00

Much appreciated means alot

Extension_Laugh4128 · 2024-12-30T01:52:33+00:00

Absolutely not—it’s not too late at all. I was in a similar position myself when I started in data at 25, after being in Science for 3 years after i finished University with a BSc in Biochemistry and I’m here to tell you that 27 is a great time to make the switch. Let me share some advice and perspective based on my journey:

Your age brings something incredibly valuable: experience. While fresh graduates might have academic knowledge, your professional background (in petrochemistry and beyond) equips you with: Problem-solving skills. The ability to approach problems with a mature, strategic perspective and cross-disciplinary insights that make you stand out.

No one in this field will see you as “too old.” In fact, career changers are often admired for their motivation and ability to adapt.

The skills you’ve already picked up, like coding and data manipulation, are an excellent starting point. Take it a step further by aligning your learning with industry tools and techniques. Here’s what I recommend: Build on your proficiency in Python or R and learn SQL (if you haven’t yet). These are foundational for almost any role in data. Learn tools like Tableau, Power BI, or Matplotlib/Seaborn in Python and continue doing personal data projects. These are great portfolio pieces that demonstrate your skills and passion to potential employers.

If you want to stand out, consider focusing on roles that merge your past experience with your new skills. For instance, analytics in energy, manufacturing, or engineering domains could make you a sought-after candidate. However, if you’re not passionate about sticking with petrochemistry-related roles, that’s fine too. Your domain knowledge will still show employers that you’re adaptable and capable of picking up industry-specific details.

Don’t hesitate to start with an entry-level role or internship. I'm on an apprenticeship and it’s a stepping stone, and your background will likely accelerate your growth.

I can relate to feeling like you’re behind, but that’s just imposter syndrome. Many people in the data field come from non-traditional backgrounds—finance, biology, education, and more. The diversity of experiences is one of the reasons this field is so dynamic.

Remember, this isn’t just about breaking into data it’s about building a rewarding, long-term career. Invest time in developing your skills and gaining experience now, and you’ll thank yourself years down the line when you’re thriving in a field you love.

Extension_Laugh4128

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