As industry leaders increasingly turn towards analytics, people who can investigate and interpret data are becoming more and more attractive to employees.
Here, with the help of an industry insider, we offer tips and advice on how to get ahead as a data scientist. Looking to make that move into this sought-after role? You’ll find plenty of insights and tips that could help pave the way to your new career right here.
Before we get started, what does the role of a data scientist entail? Put simply, they collect, manage and analyse data using a synthesis of their organisational, mathematic, statistical and programming skills. They translate data into a format that’s easy to understand and work with, presenting it in a way that allows decision-makers to interpret the findings in a visual context.
Through the use of business systems analysis, the data scientist answers queries and questions a business poses, using data and statistical modelling. These business analytics not only explain what the effects of data will have on a company in the future, but also help to devise solutions to potential challenges.
To give you an idea of life as a data scientist, we spoke to Mohamed Hamza Encibi, a Data Scientist at SEFE Energy France about his main responsibilities.
Data processing and analysis with Python – Python is an interpreted, multi-paradigm and multiplatform programming language. It allows us to process and analyse a very large amount of data and to identify the most interesting aspects of its structure.
Data integration with Talend – Talend is an ETL (exploit, transform, load) system which helps perform massive synchronizations of information from one data source (most often a database) to another.
Data visualization with BI tools – Microsoft Power BI is a business intelligence platform that provides non-technical business users with tools to aggregate, analyse, visualise and share data. The Power BI user interface is fairly intuitive for users familiar with Excel and its deep integration with other Microsoft products makes it a very versatile self-service tool that requires little initial training.
Customer data management with Salesforce – Salesforce is a CRM (customer relationship management). It allows us to capture, process and analyse information related to customers and prospects, with the aim of retaining them by offering services based on their activity and preferences.
Development of Prediction and Scoring algorithms – Develop ML (Machine Learning) algorithms with the view to predicting the future, by analysing the past and present. Understanding the laws of determinism can help you determine the future based on the past.
The role is a natural fit for anyone with a mind for numbers, mathematics and computer science. Critical thinkers and problem solvers succeed in data science, tailoring their smarts to the analytical nature of their duties. Data science isn’t always black and white; those with the ability to think outside the box, and the naturally curious who look for innovative solutions to age-old problems will thrive in positions like this.
In terms of education, a BA in maths, statistics or engineering is near-essential, while master’s and Ph.D.-level education strengthens your chances. Callum Staff, Lead Data Scientist of food at Marks & Spencer notes: “To those looking for opportunities, it’s vital they have prior experience of ‘doing data science’, but it doesn’t have to be in-depth, PhD-level knowledge.
“When I started managing my previous team, I would often get really nervous about not knowing the statistical ins and outs of techniques my team was using – I’m now confident that that’s not the point of my role.”
Alongside your education and knowledge of data management, an online portfolio stocked with demonstrable skills in machine learning, statistical theory and programming languages such as R and Python, are all hugely beneficial. Consider completing a placement year to gain experience too, as this can be a great way to get your foot in the door.
If you’re currently in another line of work, then don’t fret, there are plenty of opportunities for other professionals to transfer into data science roles. Increasingly, data-related projects require teams of people from a variety of disciplines, while more and more business also require managers and leaders to have data analysis skills in their repertoire.
If a career switch into data science interests you, but your skills are lacking, then incorporating elements of the discipline and adding more data-driven decision making into your current role can help with the transition. Callum certainly agrees with this, stating: “A lot of data science learning is best done on the job, so whilst ‘proper’ qualifications help, they aren’t the be-all and end-all. However, as data scientists work ever closer with developers, deploying machine learning models in apps, developing wider skills such as agile project management wouldn’t go amiss – it’s my current area of focus.”
Additionally, immersing yourself in the data science community can also pay off. Where possible, attend events where you can meet others and receive technical mentorship. Callum notes: “Go to talks or hackathons, try bits of code out at 10pm at night. It’s these places that spawn the ideas for projects, and serve to really add value to your skillset.”
As well as technical skills, the right data scientist is someone who can work collaboratively with people across a business. Therefore, you’ll need to make sure your communication skills have been fine-tuned and refined, as you’ll have to present complicated data to others in a way that’s simple and easy to understand. As Callum says, there’s a need to: “Marry up data science techniques and business problems, and then be the salesperson that promotes and embeds.”
To this end, you may work with data that doesn’t always spell good news for the business. Here, your diplomatic mettle will be tested; any data scientist worth their salt should be savvy enough to break the news gently, and then sway people in the right direction in order to show where things need to change.
In addition, they should be aware that data analytics is only useful when it ties in with the needs of the business. The data scientist knows the business’ strengths and weaknesses, as well as where it’s been and where it’s heading. To this end, although qualifications are useful, a thorough reading and understanding of a business’ USP, its place in the industry and what it needs to do to stay competitive are essential.
How can you leverage trends for the benefit of the business? The mark of this role is showing your ability to make these kinds of things work.
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