Cambridge Spark / 
September 16 2022 / 
6 minute read

The breadth of skills required to deliver end-to-end data science projects are complex and constantly evolving. Data science roles are becoming more specialist, as are the tool kits used to excel within them. It can often take organisations a long time to find talent with the very specific skills they need to meet their business requirements. Hence many companies are choosing to upskill existing employees rather than paying for costly recruitment to search for new talent, which in turn goes a long way to retaining top talent.  

But what are the most in-demand topics that data science enthusiasts and working professionals want to learn about in order to enhance their skill sets, advance their careers and bring strategic value to their companies? As a specialist AI & data science capability partner, helping businesses to meet their data science goals is our prerogative, so we decided to dip into our own data to find out.

Finding out what data science skills are most in-demand

Top 5 data science topics

To find out which more advanced data science skills are most sought after from our learners, we took a look at some survey data we routinely collect from our apprentices when they begin our Level 7 AI and Data Science apprenticeship (our most advanced data science apprenticeship, which is the equivalent of a Master’s degree qualification). 

This particular apprenticeship is designed for established data professionals who regularly use the likes of Python who are seeking to advance their careers and deepen their data science knowledge. It’s open to all ages and a range of seniorities from new graduates to heads of departments working in start-ups to FTSE100 companies. 

What follows are the top 5 most mentioned data science topics that our apprentices are excited to learn more about pooled from a total of 420 responses

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1. Neural Networks & Deep Learning

Neural networks

What are neural networks and deep learning?

Neural networks are computing systems made up of interconnected nodes that function similarly to neurons in the human brain. They can identify hidden relationships and patterns in raw data using algorithms, cluster and classify it, and continuously learn and improve over time.

Deep learning refers to more complicated and multilayered neural networks. Neural networks are well-suited to assist people in solving complex problems in real-world situations. They can understand and model complex and nonlinear connections between inputs and outputs; draw conclusions and inferences; uncover hidden relationships, patterns, and predictions; and model highly volatile data and variances required to predict rare events.

Highly beneficial to data scientists who are entrusted with collecting, analysing, and interpreting large quantities of data, deep learning helps to speed up and simplify these processes.

Deep learning in action example:

In days gone by, converting black-and-white images to colour was a laborious manual process. Deep learning algorithms can now use the context and objects in images to colour them, essentially recreating the black-and-white image in colour with surprising and impressive accuracy.

2. Natural language processing (NLP)

Natural language processing NLP

What is NLP?

Natural language processing is an artificial intelligence subfield that helps computers to understand, interpret, and manipulate human language. NLP draws on a wide range of disciplines, which include computer science and computational linguistics among others.

Combining computational linguistics (human language rule-based modelling) with statistical, machine learning, and deep learning models, NLP allows computers to interpret text or voice based human language to 'understand' its full meaning, complete with the original speaker's or writer's intended meaning and sentiment.

NLP is increasingly being used in enterprise solutions to:

  • Improve employee productivity
  • Simplify business processes
  • Expedite business operations

Organisations are growing more and more interested in learning how to use company data to drive improvements. For example, unstructured text is viewed by many as a vast untapped data source with enormous potential for producing crucial insights that could lead to significant business breakthroughs or encourage important social advancements.

NLP in action example: 

Chatbots are a great example of how businesses are investing in NLP to improve customer experience. A good chatbot can understand the intent of a conversation rather than simply communicating and responding to stock queries. Business owners are beginning to feed their chatbots prompts in order to assist them in becoming more humanised and personal in their dialogues.

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3. Model Interpretability and Explainability

model interpretability versus explainability

Interpretability versus performance trade-off given common ML algorithms

Model interpretability vs. explainability: what’s the difference? 

Humans build machine learning models to tackle problems that we can’t solve with fixed rules, so we build machines to solve the problem using data. Often the inner workings of machine learning models are opaque and make decisions that humans find difficult to understand. However, there are countless examples of systems that appear to work and outperform humans despite the fact that we have no idea how they function. The question is, how can we put our faith in models that we don't fully understand? Do we blindly accept decisions we don’t understand and accept that they are fair when we cannot challenge them? 

Explainability and interpretability are two terms used to describe to what degree humans can understand the internal workings of a machine learning model or what factors are used to make a decision.

Interpretability

The accuracy with which a machine learning model can associate a cause to an effect is referred to as interpretability. So, if an organisation wants high model transparency and to understand why and how the model is making predictions, they must examine the inner mechanics of the AI/ML method. This means interpreting the model's weights and features in order to determine the output. When a model has full transparency, it means we can answer the exact why and how of the model’s behaviour.

However, high interpretability generally comes at the expense of performance. If an organisation strives for high performance while still having a broad understanding of the model's behaviour, model explainability becomes more important.

Explainability

Explainability refers to the ability to take an ML model and explain its behaviour in human terms. With complex models, it is impossible to fully comprehend how and why the inner mechanics influence the prediction. However, you can discover meaning between input data attributions and model outputs using model agnostic methods, which allows you to explain the nature and behaviour of the AI/ML model.

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4. Time Series 

Time series analysis

What is time series analysis?

One of the easier concepts on this list to explain, time series analysis involves analysing a series of data points collected at consistent intervals over a defined time period instead of just sporadically or randomly. 

Time is an important measure because it shows how the data changes over time as well as the end outcome. It offers another source of information as well as a predetermined order of data dependencies.

To ensure accuracy and stability, time series analysis typically necessitates a large number of data points. A large data set ensures that your sample size is representative and reveals the most important key trends. It also ensures that any discovered trends or patterns are not anomalies and can account for seasonal variation. Time series data can also be used for forecasting—predicting future data based on past data.

Time series in action example:

Time series analysis is used in organisations to review trends over time and then make decisions based on that insight for all sorts of data. For example, daily stock prices, daily levels of rainfall, the quarterly average house prices in an area, or the percentage of people unemployed in a given month etc.

5. Data Privacy, Ethics and Regulations

Data privacy, ethics and regulations

Data privacy is frequently associated with AI models that use consumer data. Users unsurprisingly have reservations about automated technologies that collect and use their data, which may contain confidential information. Because AI models rely on high-quality data to produce meaningful results, their survival is dependent on privacy protection being built into their design.

Great data and privacy management practises have a lot to do with the company's core organisational values, business operations, and security management, and are more than just a way to assuage customers' fears and concerns. Privacy issues have been extensively researched and publicised, and consumer privacy remains a critical concern.

Our Level 7 AI and Data Science apprentices frequently express interest in learning how to interpret policies, ethics and regulations in relation to AI and data and the programme incudes a dedicated module exploring these topics in-depth.

Data Privacy, Ethics and Regulations in action example:

Organisations need to be up to date and have a thorough understanding of data privacy rules like GDPR, so as to avoid creating risks for a business if the rules are not adhered to correctly.

Wrapping things up

So there you have it, those are the top 5 topics that our AI and Data Science apprentices are excited to learn more about to prepare them for success in the field of data science and analysis.

The paragraphs above give only the briefest of summaries on what are vast and complicated topics which are covered in-depth over the course of our 15 month long apprenticeship programme, along with many other topics including supervised and unsupervised learning, ensemble methods, product management for AI and more. 

Develop your data science skills with Cambridge Spark apprenticeships

At Cambridge Spark, we strive to enable organisations to achieve their business goals by educating their workforce in data science & artificial intelligence. In addition to the Level 7 AI and Data Science apprenticeship discussed in this article, we also offer several other data skills apprenticeship programmes to match any level of interest and ability.

And if your company doesn’t meet apprenticeship requirements, we also offer corporate training courses such as our Data Fluency for Leaders course, our Data Analysis Foundations course and our Digital Leader Executive programme. 

What to find out more? Fill out the form at the bottom of the page and one of our consultants will contact you directly to answer any questions you may have.