In the fast-evolving world of technology, choosing the right academic path is crucial for anyone aspiring to make a mark in data analytics and artificial intelligence (AI).

The view that there is, and will continue to be, high demand for jobs in the space of data analytics is widely supported by several sources, including Gartner, which stated in 2023 that “data analytics, cybersecurity, digitalisation and talent management remain top priorities for chief audit executives”. While the 2024 Morgan McKinley Salary Guide reported strong growth in data science over the past year.

Research into estimated demand for graduates with expertise in the data analytics/business analytics field indicates that high demand for such graduates will continue to grow. In the 2023 BPFI Banking Sector Skills Report, data analytics and AI were key stills gaps identified by respondents in the financial sector, with about 20pc of honour’s ICT graduates finding employment in the sector within one year of graduation.

Data analytics graduates, especially those who have studied at master’s level, can explore a broad range of jobs post-graduation. Many students go on to work in roles such as data analytics and assurance senior associate, data engineering consultant, data and CRM analyst, and many others.

What skills do I need?

Typically, these jobs require graduates to be proficient in a broad range of skills, including statistical programming experience in SAS, R, Python, machine learning – artificial/deep neural networks, GPT, natural language processing, LLMs, time series, recommender systems, mathematics, computer science, statistics and data science, predictive modelling and clustering techniques, and these are just a few examples.

When looking ahead and considering which path is right for them, students should become aware of the specific skills that particular roles focus on should they have a role in mind, as well as knowing what skills are commonly used across various positions so those who are not quite so sure what role they want exactly can create an impressive array of transferable sector skills during their studies.

For example, those interested in becoming a natural language processing (NLP) engineer should pay particular attention to artificial neural networks (ANNs), deep neural networks (DNNs), generative pre-trained transformers (GPT), natural language processing (NLP) and large language models (LLMs), as one of the key aspects of this role is to build applications that process and analyse human language. Some of the projects for students interested in this role include topic modelling, sentiment analysis and text summarisation, among others.

Those interested in becoming a data and CRM analyst should pay particular attention to CRM platforms such as Salesforce, HubSpot or Microsoft Dynamics that are essential for managing customer data, time Series ML models, and develop proficiency in data visualisation tools such as Tableau or Power BI that help in generating insights. This role requires a solid understanding of customer segmentation and marketing analytics. And automation is crucial for optimising campaigns, business strategies and driving customer insights. Students interested in these roles will benefit from, for example, projects that include customer segmentation using clustering, churn prediction model and sales forecasting using time series analysis.

Those who may not yet have a specific job in mind when they begin studying should not worry, because many of these innovative skills are relevant across many roles within the world of data analytics. Students with skills such as Python and R, which are essential for data manipulation and modelling, along with a solid grasp of data mining, machine learning – both traditional and ANN/DNN, statistical analysis, domain application, data visualisation tools and big data are in a very fortunate position as these skills can be applied in different ways in many different roles. For example, these skills are used by those working as data analysts, product analysts, data engineering consultants, performance and optimisation leads, and data scientists.

How to prepare for industry

While gaining the appropriate academic qualifications, it is also crucial that students develop transferable skills so that after graduation, they are industry ready. Those who are given access to the latest technology should take advantage of the opportunity to engage with firsthand experiences that mirror industry demands.

Acting on ideas and nurturing ambitions is a hugely significant aspect of honing skills that will be instantly applicable in the workplace. Students should welcome opportunities to participate in events that allow them to assess their abilities such as challenges or hackathons.

Students should also connect with peers, mentors and industry professionals whenever possible to continuously build a network that will benefit them professionally.

It’s important to realise the power of collaboration early while studying. Utilising knowledge and applying it to everyday work settings is a skill that takes time to master, which is why academics should stretch beyond the classroom. Those who wish to pursue a career in data analytics should be prepared to put themselves out there and embrace feedback whenever possible.

This mix of adapting to industry needs and developing practical learning enables students to thrive in an ever-demanding career landscape.

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Source - https://www.siliconrepublic.com/advice/data-analytics-ai-study-degree-research-skills