blog
Empowering analytical teams with the tools to thrive
Posted 26 October 2023
Written by Jack Russell
The domain of Data Analytics can be confusing to navigate. As the field continues to develop and new specialisms emerge, the list of job titles keeps getting longer with Data Analysts, Data Scientists and Data Engineers now joined by Machine Learning Engineers and ML Ops Engineers amongst others.
With a growing proportion of business decisions and processes being driven by increasingly advanced data techniques, it is important to understand the differences between these roles and crucially, how the people in each can be empowered with the right tools to solve real-life organisational problems.
Broadly speaking, Data Analytics focuses on using data to draw actionable insights and support decision-making processes. It primarily involves analysing historical data to discover patterns, trends, and relationships that can predict future outcomes and thus inform business strategies, product choices and customer experiences. Data analysts employ statistical methods, data querying, and visualization techniques to explore data, identify trends, and communicate their findings to stakeholders, working with databases, spreadsheets, and data visualization tools to process and present these findings in a meaningful way. In addition to traditional tools such as SQL and Excel, python’s powerful ecosystem of data-centric libraries such as pandas, numpy and matplotlib has emerged recently as the platform of choice for doing advanced data analytics.
As Machine Learning has gone from providing experimental proofs-of-concept to powering key components in production systems, a standardised machine learning workflow and associated roles have started to emerge.
Within Data Analytics, the term “Data Science” has come to imply the use of advanced statistical and computer science techniques such as Machine Learning. This involves the development of algorithms and models that enable computers to automatically learn to make accurate predictions without being explicitly programmed, given a large enough set of “training data”. As Machine Learning has gone from providing experimental proofs-of-concept to powering key components in production systems, a standardised machine learning workflow and associated roles have started to emerge. Data Engineers focus on getting data from its source into a format ready to model.
Data Scientists are responsible for building, training and fine-tuning the models themselves, whilst ML Engineers ensure that they can be reliably deployed and maintained. Finally, the ML Ops Engineer has most recently emerged as a role which coordinates this whole process. As for tools, here again, the platform provided by python’s ecosystem of open-source libraries has become the industry standard for building Machine Learning applications.
Finally, whilst Machine Learning approaches can yield powerful results given enough data to train on, for the many contexts where data is scarce, another set of less well-known but equally powerful techniques can be applied. Bayesian Methods provide a principled framework for reasoning with limited data, allowing the integration of this data with prior knowledge or beliefs to make inferences and predictions in the presence of quantifiable uncertainty. A solid foundation in statistics and probability is key to unlocking the value here, although python again provides some powerful tools that help with the computational heavy-lifting. Adding proficiency with these techniques to your organisation’s toolkit can help elevate your analytical capabilities by addressing the problems which traditional analytics or machine learning are poorly placed to solve.
Whatever your context, at Neueda, we have the knowledge and experience to guide you through this ever-changing landscape and equip your teams with the practical data skills they need to succeed.
About the Author
Jack Russell
View Jack Russell’s profile
