Getting into a career in Data Science
A place to come for careers help and guidance on getting into the industry
It’s the time of year when many will have either finished their exams/assessments or will be approaching them. If only Uncle Ben could’ve been there to help Peter with his exams...No matter how much work you’ve put into your revision or coursework, stress is part of the programme. But you don’t have to be alone. Whether you’ve just submitted your paper, you’re approaching an exam or you’ve just finished either — let’s talk. How do you pass assessments?Undergraduate, masters, cloud certification or any other course — it doesn’t matter: Have you already done all exams and assessments? How did you get through them? Share the love/knowledge Got an exam or assessment coming up? Any topics/questions you’re struggling with? Ask here Are you one of those people who aren’t struggling at all? How? Please tell us how!! 🙏🏻 Have you been procrastinating, doing almost everything but revision? Ask the Community for tips on how to overcome procrastination Have you just finished an exam or
Many people leave academia for a role in data science, often from a wide variety of subject areas. Did you make the move from academia into data science? What did you find easy/hard about the transition? Is there any advice you’d give to others in a similar position? I’ve written a blog on this topic if you want to read my take!
Data science events and conferences can be a great place to network, learn about new tools, hone your skills and even uncover best practices to make your work life easier. With that in mind, what are the top, must-attend Data Science events of the year?
We often talk about the Data Science projects we do purely from a business perspective. Predicting customer behaviour, forecasting demand and so on. But Data Science can be used in a huge variety of ways.What’s the most “fun” thing you’ve done with Data Science?For me it was using NLP and Audio analysis (via Tensorflow) to compare and contrast the music of Radiohead with Little Mix. Then present it at EARL Conference!
Conversations with my teammates have shown me that they paths to a successful datascience career are as diverse as python packages. Four years ago, I would have probably waved off the sheer thought of pursuing a career in data science, I laughed tiredly. My interests at that time were more focused on political science and history. However, after a few detours I found my passion for the field and today I couldn't imagine doing anything else. I would be super interested to hear how other people ended up in their current positions and what convinced them to take this path.
Good advice can be simple, practical, inspiring, thought-provoking or even life-changing. Some of the best advice I ever received:It’s never too late to completely change your career path. Our hobbies and interests change and so do our values and priorities. It’s okay to change your job to fit your current situation. It’s okay to not know the answer to an interview question. Just be honest and communicate that you are eager to learn (this was particularly helpful in technical interviews). Work should fit your life, not the other way round. If you have to change everything about your life to fit your work, it may not be for you.The worst advice was to apply for every single position even remotely related to what I wanted to do to increase my chances of finding a job (even if it’s not a job I wanted). It’s too difficult to keep track of so many applications, it becomes too stressful and your work is too important to compromise for the first position you’re offered. I think it’s better to
Working at Peak, we have such a diverse team of data scientists, and I’m always interested in hearing about people’s routes into data science, particularly the less conventional routes! I worked in various roles in business intelligence and data engineering before deciding to move into data science, and I did a two year part-time MSc in data science to get me into my new career. This got me up-to speed with the maths and statistics knowledge that I lacked from my non-technical undergraduate degree.What career(s) did you have in a previous life? I’d love to hear about how you made the transition into data science and what skills from your previous jobs were transferable?
If you’re reading this then you’re probably thinking about or maybe already in the process of applying to a position in Data Science…..what a great decision that is! 🥳 From personal experience, I know that the application process for a DS-job can be quite daunting: Data challenges, live coding…..the list goes on. To give people an idea on what to expect, how to prepare accordingly...and maybe just to have a bit of fun, I thought it’d be useful to share a few of our experiences. As a start I’ll give a quick rundown of the application process for the Peak graduate scheme. Following the usual CV submission and the answering of a few questions we got sent a data challenge that consisted of the following problem: We were tasked with coming up with a classification algorithm to a set business problem. On the subsequent interview during the assessment day we then had to prove the commercial applicability of our recommendations & insights. In retrospective, my top tip would be to not worr
Data science is a broad, ill defined, and ever growing field. Out of that mess, though, more defined career paths are slowly arising. These are the 7 most common that I have picked up on - in no way a complete list, or even a good one, but I think it’s good to know what your options are. I’ve tried to order them from more code focussed to more people focussed, but all roles will require skills in both.Data engineering involves getting the data into the right place at the right time, and in the right format. It’s crucial for any sustainable solutions. Data engineering is the more structured side of this, most of them come from a compsi background and you need qualifications. Machine Learning engineers are often your more advanced ‘data scientists’, and they work on deploying algorithms that scale with volume and deliver outputs quickly. A lot of ML engineers are also involved with research, although it isn’t a requirement. Most of the DS that work at the likes of Deepmind, OpenAi, or Vi
There are different ways you can start a career in Data Science and they don’t necessarily require you to have completed a degree in Data Science or Computer Science. With the rapid expansion of the Data Science community, many resources have become available online for self learning; from blog posts, to videos, to free books, to entire courses.However, navigating in this sea of information and deciding where to start can be overwhelming. I found myself in the same situation when I first started my journey in the Data Science world, after finishing my Masters in Astrophysics and having very little knowledge on the topic. In this blog post I would like to share what I learned from my personal experience to everyone who would like to start a career in Data Science from scratch. The basics of data science Data Science is a very wide field and in continuous evolution with different areas you can specialise in. However, there are core skills required in a data scientist role that you sho
Welcome to the Getting into Data Science Q&A part of the Community 👋Some ideas for discussion for this section:Asking what kinds of subjects people studied to get into data science Sharing online courses you have found useful Asking for advice on a particular path into data science given your background, for example if you’re changing career and not sure how to make the switch And any other questions you may have!In general, we suggest you to use the Discussion topic type for the most part as most questions in this space do not have a single correct answer but if you have a question that does have a single answer then feel free to use the Question topic type.
From our experience, there are three key areas of skills you will need to develop in order to get a job in data science; commercial, theoretical, and programming. Currently you may have limited experience in these areas, but this blog post should provide some resources and ideas to help you practice and get you one step closer to landing that first data science job! CommercialIn business, the role of a data scientist means that you get to work with lots of different people; from engineers to sales teams, as well as your customers. This means that you need to develop your commercial skills in order to communicate the impact of Data Science and be able to explain your work to others. Practice presenting in front of others: Find opportunities to communicate or present a technical project to a non-technical audience. This is something that we do day-to-day at Peak with our customers. It’s easy to slip into technical jargon without realizing, so the more you practice, the better! Data vi
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