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Soft skills matter! A lot! DSBoost - #13
Welcome to the 13th issue of DSBoost, the weekly newsletter where you can discover interesting people in the ML/AI world, get the main takeaways of a relevant podcast, and stay up to date with the latest news in the field!
💬 Interview of the week
This week we interviewed Paul, who is a Data Mining Analyst. Enjoy:
What did you study/are you studying (if your background is different from DS, how did you end up in the field)?
I did my BEng and MEng in Mechanical Engineering at University of Science and Technology in Cracow (AGH), Poland
I also did MSc in Computational and Software Techniques in Engineering at Cranfield University, UK (double degree)
What are your favorite resource sites and books (ML/AI)?
What got you into your current role (portfolio, certification, etc.)?
It was neither portfolio not certificates. I believe the mixture of two things got me the job:
Programming skill: I was able to demonstrate how I’ve developed my skills over the past 3-4 years prior to getting the job. I initially developed code in Matlab, but then transferred all this knowledge into Python and built on it. From writing simple scripts to functions to eventually building modules. I was the only one in my team using programming for the analysis too so all the skills I acquired were pretty much self taught.
Soft skills: having described my programming journey showed my pure passion for self-development and continuous improvement. I was able to demonstrate I can achieve this on my own. But as I was previously working as a project leader, I was bringing other skills to the table too. Things such as: being able to clearly present technical topics to the stakeholders. I was a keen learner and because of that I was also willing to teach my teammates things they might need to do the job efficiently. Being a good communicator helped me too - it’s important to be proactive at work. If you need information, go for it, chase people. But make it in a friendly way.
In general, having technical skill was important but it’s only half of the success. People-related skills and ability to develop were just as important.
What do you enjoy the most in your work?
I work as a data analyst in the automotive industry and I love the fact that every project I work on is slightly different. Another thing that allows me to enjoy my work is the fact that I have freedom in deciding how to solve the problem. I’m given a task and a deadline. There’s no micromanagement or specific way I must follow to complete the job. This is great as I usually learn something new everytime there’s a new project to work on.
What tools do you use the most / favorite tools?
I use the holy trinity of data analysis, that is Numpy, Pandas and matplotlib. I enjoy using those tools. I think I know them quite well but there’s still so much to learn. As much as I enjoy the whole data analysis process, creating visualizations is my favorite part so I’d put matplotlib at the top of the list.
Do you use ChatGPT or other Al tools during your work? If so, how do they help you? Do they change your approach to problems?
There are two ways I’m using chatGPT at the moment and both of them help me do better job:
Learning. I often ask chat GPT to explain some concepts to me and provide multiple programming examples. This helps me learn faster. But I usually apply this to the concepts that I’ve known in the past and need to refresh my memory
Code debugging. Sometimes when I can’t spot the bug or don’t clearly understand the error message, I’d copy my code to chatGPT and ask: what’s wrong with it? This saved me some time on multiple occasions already.
What is your favorite topic within the field?
It’s data visualization. I love creating visualizations as well as learning materials that explain how I do things.
Which one of the recent AI/ML models will have the most significant impact on the industry in your opinion?
I think the whole branch of generative AI looks very promising (I don’t want to say scary, but it is a bit too). One interesting example which I’ve seen recently (relevant to the automotive industry) was the app that creates 3D CAD model of a part from 2D drawing or sketch. That was pretty cool.
What are you currently learning or improving (topics you are interested in nowadays)?
I first learned about Machine Learning at University. This gave me theoretical knowledge which I’ve never had a chance to use in a professional environment. I’m trying to refresh this knowledge at the moment.
I’m also continuously developing my general Python knowledge (learning about OOP, generators, datatypes, less popular modules etc)
What is the biggest mistake you've made? (preferably DS related)
I think the biggest mistake I’ve made was trying to learn a lot in a short time span - this resulted in not learning much after all and wasting the time.
There was a time (especially during the lockdown) where I would cram course after course but never took a proper time to practice what I’ve learnt - watching videos and understanding them gave me the impression I knew the subject. But two weeks later I could barely remember anything. Therefore, right now I prefer to learn at a much slower pace but do more practical/programming examples along my learning journey.
What is your most significant achievement? (preferably DS related)
I’m proud that I’ve managed to transition from Project Leader role to a Data Analysis role. It took me ~3-4 years to achieve that. Lots of learning along the way. It’s not easy to change the career like that but once you’ve got the invitation to a job interview, it’s on you to show you’ve got the necessary skills even if your current job title is not implying it.
I’m also proud of finally starting a tech blog on Medium earlier this year. I’ve been on Medium for over 2 years now, have collected 100s of potential ideas for the articles, wrote some drafts but only in January 2023 I dared to publish my first piece.
Can you share a fun fact about yourself?
I’m not sure if it’s particularly funny, but besides programming I love traveling, playing the guitar and sarcasm.
🎙️ Podcast of the week
Cloud computing allows companies to rent IT infrastructure on an as-needed basis, avoiding the upfront costs associated with physical servers and space, and allowing for more flexibility and agility.
Data scientists are increasingly requiring more compute-intensive resources to train advanced machine learning models with large amounts of data, making cloud resources a necessity rather than just an advantage.
Learning cloud computing for data science is becoming a necessity to keep up with the future of heavy compute-intensive models and increasing numbers of users. It may seem complex at first, but it becomes a no-brainer once you get the intuition of how things work.
The most widely used cloud computing service is AWS. It offers more than 200 services. The four main types of services relevant to data science are compute, storage, databases, and machine learning.
Getting the AWS certification is important because it adds a stamp of approval to one's skills and makes them more employable. It is valid for three years and is worth the extra effort.
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🧵 Featured threads
🤖 What happened this week?
Google has combined its AI research labs, Google Brain and DeepMind, into a new unit called Google DeepMind. The move is aimed at maintaining Google's edge in the competitive AI industry and competing with OpenAI. The new unit will be responsible for spearheading groundbreaking AI products and advancements while maintaining ethical standards.
Google CEO Sundar Pichai has called for a global regulatory framework for artificial intelligence (AI), stating that concerns over the technology keep him awake at night. He warned that AI could be “very harmful” if deployed wrongly and that the competition to develop the technology could lead to safety concerns being ignored.