As technology continues to develop at a rapid rate, the skills needed to work with that technology need to evolve faster or they become obsolete. This means that candidates have to continually upskill and self-educate to keep up to date on what’s happening in the industry after they have completed a formal qualification.
One job in particular that has grown rapidly in importance in recent years is that of a data scientist. With the rise of big data comes the need for more analytical and highly skilled people to interpret and mine that data for businesses. Learning data science can be intimidating, but that didn’t stop Harvard Business Review from calling it “the sexiest job of the 21st century” back in 2012. Glassdoor has also named it the best job in the US for the past three years in a row.
If you’d like to start a career in Data Science, here are 5 essential tips to follow.
1) CHOOSE THE CORRECT ROLE
Choosing a career in data science is not straightforward. There are a number of varied roles available that include a machine learning expert, a data engineer, a data visualization expert, a data architect and many more that you could get into if you have the experience. Your choice of a role will be dependent on your work experience and background as, for example, a software developer would find it somewhat easier to move into a data engineering role.
When starting out, it may not be clear which path you should take and what skills you to hone, so to get a better grasp your available options, a few things we suggest are:
Talk to people who are already working in the industry to identify what roles are available and what each of them entails.
Figure out what your strengths are and what role closely aligns with your field of study and interests.
Find a mentor who can set aside a small amount of time to walk you through the steps you need to take.
It’s important to fully understand what each role requires, rather than hastily jumping into applying for it and finding it’s not a good match for where you want to go in your career.
2) TAKE A COURSE
Once you have settled on a role, the next step is to set time to fully understand the requirements of the role and what qualifications you may need for it. As the demand for data scientists is outstripping the number available, there are hundreds of courses and resource materials available so you can learn whatever you want to. Some we recommend you take a look at include:
Coursera – Data-Driven Decision Making
This course is provided by PwC so unsurprisingly it’s weighted more towards business applications than theory. However, it covers the full spectrum of techniques and tools that are being utilized by businesses today to tackle data challenges.
EdX – Data Science Essentials
This course is provided by Microsoft and is part of their Professional Program Certificate in Data Science. You will need to have beginner level knowledge of Python or R before taking this course though.
Udacity – Intro to Machine Learning
Machine learning is without a doubt the hot topic in data science right now. If you want to land a role in machine learning, this course will give you a full overview of it from theory to practical application.
3) KEEP LEARNING BY BUILDING PROJECTS
Are you spending most of your time looking for a job? While it’s important to put the time into your search, it’s also every data scientist’s primary responsibility to keep learning. New tools are constantly coming out, the skills that are defined as “data science skills” are constantly shifting, so by learning, you will stay on top of these skills, and improve your desirability to any potential employers.
The theory is important, but to set yourself up for getting a job, you also need to set time aside to work on projects. They will allow you to practice what you’ll be creating in a data science job, help to improve your portfolio and build your confidence when attempting to score an interview.
Starting a project isn’t difficult. You need to:
Identify a dataset which is interesting enough to make charts about. It also shouldn’t have too many columns or rows so it’s easy to work with.
Create a list of questions you want the dataset to answer.
Use a tool to explore and analyze the data (e.g. Jupyter Notebook).
Use Github to store your notebooks.
4) ATTEND MEETUPS AND EVENTS
No matter what stage you’re at in your career, it’s important to have industry peers to lean on for advice and support. Why is this important? This is because a peer group can help keep you motivated, overcome hurdles, and avoid the same pitfalls of others who have come before you. If you’re new to the industry, it can be hard to meet like-minded individuals, so you should set time aside to find meetups and events that are relevant to your career.
In Dublin, one of the more prominent data science meetups is “Dublin Data Science” who host monthly meetups that are free to attend in Dublin city center. With a community that has over more than 1,800 members, their events are a great opportunity to meet data scientists at different stages in their careers.
The “DatSci Awards” also offer a great opportunity for you to hear success stories from some of the industries best data scientists in Ireland, along with the chance to mingle with leading technology companies who are searching for people like you.
5) BUILD YOUR PROFILE: SHOW OFF YOUR WORK
As an aspiring data scientist, it’s important to build your profile in the industry as it will make it easier to access new opportunities. One way to do this is through sharing your work with others. There are a number of data science and programming communities that value tutorial or project walkthrough posts that you should contribute your work to:
/r/machine learning — machine learning related articles and tutorials
/r/data science — anything that relates to data science
/r/python — most of the articles are about Python
DataTau — basically Hacker News for data science
/r/learn python — articles or tutorials about learning Python
/r/learn programming — articles or tutorials about learning programming.
As with any community, it’s important to engage in the community before you consider sharing your own content. Read content submitted by other members, add relevant and valuable comments, and ensure you’re following the rules of the community. People tend not to like others who focus only on their own selfish interests.
Sharing your can not only generate traffic to your blog but can also result in:
Recommendations about how to improve your work.
Potential leads from recruiters who are searching for candidates.
Direct opportunities from companies who are searching for people like you.
Industry peers for you to turn to for support throughout your career.
