In November 2021, we held a panel event ‘How to get into Business and Data Analytics’ for Social Science and Law students. Read on for essential tips to ‘get in’ and ‘get on’ in roles which are growing in demand from graduate employers.
Speakers joined from organisations including Accenture, IG, LV Insurance and Sainsbury’s. Roles in both Business Analytics and Data Science were represented, allowing for deeper insight into what it’s like to work in these distinct, but related fields.
[This blog, written by our Career Peer Support Assistant Ethan Osborn-Clark, recaps their insight into three main sections: job search tips, the skill set needed, and final remarks from guest speakers.]
The Job Search
A STEM degree is not essential
Anybody can access these careers! It’s a case of building on any analysis skills (both qualitative and quantitative) developed through your degree. You don’t need to be studying a STEM subject to work in business and data analytics – just have an open mind to learning new things, such as programming languages.
Find something you are passionate about
Consider what industries and organisations you might want to apply your data analytics skills to, we had examples of travel and aviation, food retail, insurance, and household technology! The exciting thing is, there’s the ability to work in any industry, on any solutions, so long as you have and are willing to develop your Business and Data analytics toolkit of skills.
Take opportunities to broaden your portfolio and experience of business or data analytics work -speakers shared examples of moving between working for a consultancy as well as in-house roles within any sector.
Basically, you’ll perform better in a job and industry you love. Doing what you’re passionate about may take a little experimenting, finding out what you don’t like first. Don’t forget, careers are meandering.
The skill set
It’s not just computer science skills, but they certainly help.
Here we highlight key industry-specific skills, particularly for Data Science jobs.
- Applying for a job with knowledge of using a programming language (e.g., Python, SQL) is beneficial but not necessarily essential / there will be plenty of on-the-job learning too.
- Machine learning – where you use python and SQL to build a prediction model using the stats knowledge you have.
- Having an awareness of cloud technology – this is the future.
Soft Skills – cutting across both Business Analyst and Data Science roles
Don’t forget them! Here are a few examples that came up in the session:
- Translating complex data into easy-to-understand accessible language – companies need to know what a model is for and how it works.
- Intellectual curiosity & problem-solving. What does a company want & why do they need it? What opportunities does it present them with? When do they want it by and how does it fit into their short- and long-term planning? Ask these sorts of questions.
- Business intelligence. What are the company’s key performance indicators? How do you improve processes? How does your skill set compliment the opportunities you have identified?
Final remarks from speakers
- Keep learning. These are constantly changing fields. Be aware of the changes to give yourself the best chance of success.
- Technical skills. Understand programming, machine learning and cloud technology.
- Capitalise on all opportunities presented to you. Check out Bristol Hub for opportunities.
- Everyone has a different route into the sector. There is no standard way.
Find out more
Watch the How to get into Business and Data Analytics event recording.
Check out role profiles for Business Analyst, Data Scientist and Data Analyst.
Learn the latest skills like business analytics, data science and programming with Coursera. The Bristol Data Science Society also runs workshops such as ‘Beginners’ Python’.
Explore Careers Service application resources and resources for finding jobs and work experience to increase your chance of success.
Blog written by Ethan Osborn-Clark, a final year BSc Geography student and Career Peer Support Assistant. Edited by Karl Anton (Faculty Employability Adviser).