Open the Door to the Data Community: How-to Guide | by David O’Donnell
Data is everywhere. It's one of the “hottest” fields to be working in according to Glassdoor. In fact, The Economist has stated that “The world’s most valuable resource is no longer oil, but data”.
In a field that seems to be ever-expanding, it would feel like it's an easy industry to make your first steps into and get involved in the community. For many, like myself, there is an overwhelming amount of information and forums to be part of. The step into these areas involved navigating through sometimes highly advanced code and statistics that makes you want to close the door as soon as you peeked through it.
I am going to explore in this article ways of taking your first steps through that door and understanding how you can start exploring data within the wider community. The benefits of such will mean: Networking Opportunities, Learning from Others, Seeing the capabilities of data and much more…
Kaggle
This is the best resource to get insight into how you can manipulate data using python. I use it in two different ways that are easily achievable for everyone: Getting Data and Reviewing Code
There are several open datasets that can be downloaded from Kaggle. Traditionally people use Kaggle for machine learning but for beginners, it can be used to explore data and get insights. For example, you can download a dataset on Netflix. You can then use the dataset on your laptop and manipulate it with Excel initially and then move on to Python when you are comfortable. The code of others using python can also be reviewed. You can review their code and make comments to get more insight, allowing for you to be part of that community without writing code yourself.
Wait, LinkedIn is just for finding jobs, right? Yes, it can help you find a job, but it also has amazing resources. Personally, I follow many leading people within the data community who often post interesting articles and documents to help with analysing data problems. With LinkedIn again you can make comments on posts and direct message people to ask about data issues you are having. The community is very open on the platform and is a great resource to tap into.
✨My favourite account to follow is Alex Wang, which provides amazing articles consistently with regard to Data Science. Alex Wang Data Science
YouTube
As basic as it sounds, YouTube is an amazing resource. It has an endless supply of educational videos that you can listen to or code along with. I like to follow certain people and get involved in the comments discussion. On random videos, you may see these are usually the last place you want to visit but for data channels, you get such informative discussions on different approaches and open questions.
✨Some of my favourite accounts to follow are Ken Jee, Alex The Analyst, Tina Huang, and Luke Barousse (in no particular order 😉)
Multiverse
The best for last! Multiverse is an ecosystem of people who are in exactly the same position as you or have been where you are currently. It is the best resource to use and the greatest way to get involved in the data community. They are multiple events hosted online and in person, it’s a safe space for basic questions and it’s a platform where you can make a big difference to others by participating and answering other person's questions.
What to do: download the app, check the platform regularly, sign up for events, reach out to others and be involved. It is as simple as that. 💯
Summary
To summarise, data is everywhere and knowing how people are manipulating it and using it in business cases is critical for everyone regardless of their role. Start now by getting involved in the community: whether you are just watching from afar or getting involved in the details.
🚀Start now by getting involved in Kaggle, LinkedIn, YouTube and Multiverse🚀
David O’Donnell is a Data Fellowship Apprentice at Skanska and is writing for the Apprentice Lens. Here's more about him:
"A Civil Engineer with 8+ years of experience looking to encourage others into the world of Data Analytics. I aim to write informative articles on how to learn new topics, personal growth and being part of the wider Data community."