There’s no such thing as a standard end-to-end data science journey.
With organisations across every industry facing a series of specific challenges when it comes to managing their data strategy, each has their own specific requirements. Tech stacks, datasets, and objectives will vary depending on the size and strengths of each business – which means that even the best data scientists won’t necessarily be equipped to handle every challenge.
Leading organisations are those developing strong data science teams that can adapt regardless of the circumstances. That often requires professionals from different backgrounds, with three archetypes coming to mind.
Firstly, there’s the programmer-turned-data scientist, who specialises in the language of data for programming. Then there’s the mathematician turned data scientist who is best placed to analyse quantitative data via statistical methods. Finally, you’ve got the ‘texpert’ data scientist, combining technological literacy with technical expertise to grapple with data.
Think of each type of data scientist as one side of a triangle. With so much expertise to absorb and so many different scenarios to navigate, it’s extremely difficult for one individual to cover every base. With that in mind, organisations would ideally include all three archetypes when building futureproof data teams.
Data science is about analysing information for insights and preparing it for more advanced applications, such as Machine Learning (ML). This can’t be done without strong coding expertise, with various languages and associated libraries being a necessity for data-related tasks.
Python is an industry-standard language for data science that’s easy to learn, and contains important libraries like Pandas for data analysis, Matplotlib for data visualisation, and Scikit-learn for ML. R is also important for a wide array of functions allowing users to analyse and develop statistical software. Furthermore, there are recent languages such as Julia – which accelerates the data analysis with its natural speed – surfacing and gaining traction in the data science community.
Programmers are also uniquely well-positioned to code UDFs (User Defined Functions). These scripts allow organisations to programme their own analysis and perform other operations within analytics databases, enabling them to address problems that can’t be solved through a sole reliance on SQL (Structured Query Language). While SQL can be used to administer databases and retrieve information, it’s less versatile without additional coding functions programmable via UDFs.
Without context of this kind, data science becomes virtually impossible. No matter the mathematicians in your team, or the scientific brains capable of interpreting the results, you need to be able to manipulate data – and with increasing size of data, this manipulation needs to happen at scale.
Of course, you also can’t afford to make decisions before you’ve got the facts. This means you’ll need analysts to interpret quantitative data, whether it’s sales figures, inventory levels, or customer satisfaction surveys. This isn’t lost on a range of organisations that are leveraging data for higher revenues, personalised customer interactions, and more.
To realise these benefits, you’ll need to identify which business questions need answering.
