How to get a job in data scienceBy Amy Sharif and Sorcha Gilroy on March 12, 2021
Looking to get a job in the wonderful world of data science? If so, this blog post – written by two of Peak’s data science team leads, Amy and Sorcha – will help you learn which skills you should look to develop to strengthen your chances of success!
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!
In 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 visualization. In an industry role, charts and visualizations are a useful way to communicate findings in data, and it’s important that you can make high quality charts. ggplot2 is a popular charting library in R that you can practice using. Data visualization is a huge topic in general, so visiting sites like Information is Beautiful can be great inspiration
Turning a real world problem into a data science problem. Projects you may have worked on so far might be very well defined and use small, clean data sets. This often isn’t the case when working in the industry! Kaggle is a good place to start for finding many different datasets and tutorials. Once you’ve gotten comfortable working with data, try to solve a new problem that you’ve thought of yourself.
When applying data science models to commercial problems, it’s important that you understand the mathematics underlying these models. This helps you to understand if you are solving a problem in the best possible way, and will help you to improve your solution if your model isn’t performing as well as expected.
You need a good mathematical foundation – ideally some statistics. You don’t need to have studied maths; other similar subjects like physics and engineering are also great preparation for a job in the data science industry.
Being able to explain how models work is key. As an example, if you’re using a particular algorithm (e.g. Random Forests) or model performance metric, you should understand how they work and why you’re using them. Data scientists at Peak are encouraged to always be curious, and an understanding of how things work enables you to spot risks and address them quickly.
If you come from a less numerate background, it may be worth doing an MSc in data science before applying for jobs. Several MSc programmes have industry placements to give you some commercial experience while you study. We have hired several graduates from the Data Science MSc at Lancaster University.
As a data scientist, you need to be able to put your mathematical and commercial knowledge into practice by writing code. You don’t need to be a fully fledged software engineer, but it’s important that you are comfortable with programming and open to learning new languages and technologies!
Some experience with R or Python is expected in most data science jobs, so it’s good to learn at least one of those before you apply. Some useful resources are R for Data Science for R, and Codeacademy and Numsense for Python.
SQL can be useful, too. It’s not usually a prerequisite but often used a lot in data science jobs. W3Schools is a great resource for this.
If software engineering is something you’re interested in, data science teams often need people who can generalize code and create packages for the team to use. Chip Huyen’s blog is a great place to learn about machine learning engineering.
We hope this blog will help out any aspiring data scientists and offer some practical advice on what you can learn before applying to a job in data science. When you’re ready, make sure you apply to Peak!
🚨 We're currently looking for budding data scientists to join our Peak Data Science Graduate Scheme!
For more information on this scheme, data science roles at Peak, or to learn about our Data Science Mentoring programme, get in touch!