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8 Trends in the Data Science Job Market 2022 & 23
Data scientists are going to be the new rock stars - they're the ones who help companies collect, store, and analyse large amounts of data! But wait…what is "big data"? You know how your computer can sometimes run slowly because it's trying to process too much information at once? Well, big data happens when you have so much information that it takes a lot of processing power to deal with it all at once.
And that's where data scientists come in! They use their skills and knowledge to find patterns in all these huge mounds of data, which helps companies make better decisions about how they do business and what products they should sell.
Trends that will shape the future of the data science job market
Here are the 8 data science trends in the job market that are important to watch out for in the coming years.
#1. Jobs in data science are being affected by quantum computing
With the advent of quantum computing, there's no denying that jobs in data science will be affected at some point.
Quantum computing is a new way of doing things that uses quantum properties to perform calculations. The most notable feature of quantum computers is that they can perform many computations simultaneously, which means they can solve problems more efficiently than classical computers.
#2. Financial and insurance companies recruit more data scientists
The financial and insurance industries are increasingly looking to hire data scientists, according to a recent report from the McKinsey Global Institute. The report found that the demand for data scientists will continue to grow.
Financial institutions are in a constant state of change. As they face new challenges, they need to adapt to the changing market. This means that they need to make changes to their product offerings and customer experience.
They are recruiting data scientists to help them make better decisions about their risk management and investments. The need for these experts is also growing in the healthcare industry, where hospitals and clinics are looking for ways to improve patient outcomes by analysing their records.
#3. Inadequate preparation of companies to hire and manage data scientists
Many companies are still struggling to make the shift from having no data science team at all (or one or two people who don't have enough time), to having a fully-functioning team that can produce meaningful results.
The main reason this happens is that companies don't know how to properly train and support their employees who are new to data science. Many companies think they can just hire someone with a PhD or master's degree and expect them to know everything they need to know about the field—but this isn't true!
As a result, they're missing out on opportunities to optimise their processes and make better decisions about how they do business.
#4. An Increase in cloud organisation
Cloud organisation is a type of business structure that uses cloud computing technology to provide services over the internet. Cloud organisations typically offer software-as-a-service (SaaS), platform-as-a-service (PaaS), and infrastructure-as-a-service (IaaS). Cloud organisations can also be categorised as public, private or hybrid.
Cloud organisation is an emerging trend in the data science job market. It is predicted that by 2020, 50% of all computing will be done in the cloud. This means that companies will need to hire more data scientists to work with big data and artificial intelligence (AI) applications on the cloud.
#5. An unimaginable amount of data is going to be generated in the next decade
The amount of information that we generate and store on a daily basis is growing at an unprecedented rate. According to IDC, the world generated 44 zettabytes of data by 2020—that’s 44 trillion gigabytes. That’s more than double what was generated in 2018. In fact, IDC predicts that the amount of data being generated will increase five times every five years through 2025, and then double again by 2030.
As a result, we are facing an explosion in demand for people who can manage this data efficiently and effectively. Data scientists are becoming increasingly important as organisations need to be able to analyse their data and make sense of it.
#6. Human-Centred Data Science & Analytics
Human-centred design is a philosophy that aims to ensure that people are at the centre of any product development project. This means thinking about how users will interact with a product, and then building it so that they can do so easily and intuitively.
In the past few years, we've seen a rise in the number of data science jobs that require human-centred skills. This means that in addition to the technical skills needed to understand and analyse data, candidates must also be able to communicate those insights to help their organisations make decisions.
To give you an idea of what this might look like in practice, let's say you're building a new product for your company. You'd want someone who can take all the raw data about your customers' behaviours and preferences and use it to build a model that predicts how they will behave when presented with your new product. But you wouldn't just hire someone who could do this technical work—you'd also want someone who knows how to present these findings in a way that makes sense for your business and its goals.
#7. Predictive analytics marketing is the new revolution.
Predictive analytics is also known as predictive modelling, forecasting and statistical analysis. It uses historical data to predict future events and trends. The goal of predictive analytics is to create models that can be used to forecast future outcomes. It is used in a wide range of applications such as sales, marketing, finance and risk management.
This rapid growth has created a huge opportunity for data scientists who can use their skills to help companies identify potential customers, engage with them in real-time, optimise campaigns based on customer behaviour, and predict what will happen next.
#8. Companies facing a shortage of data science experts this year
According to a recent report from McKinsey & Company, almost half of companies surveyed said they were experiencing a shortage of data science talent; in fact, one in five companies reported having "extreme" difficulty finding qualified candidates.
In order to fill these roles, companies are looking at alternative ways to recruit new talent or retrain existing employees. One way is through boot camps: intensive programs that train students in areas like data science and machine learning.
A Sneak Peek into Data Science Careers
You might be wondering how you can get started on this exciting new path. Well, we've got some tips for you!
Top Technical Skills For Data Science Career
The following technical skills are essential for data scientists:
Data Visualisation
Deep Learning
Processing large data sets
Data Wrangling
Mathematics
Programming
Statistics
Big Data
Statistical analysis and computing
Machine Learning
Top Non-Technical Skills For Data Science Career
There are some non-technical skills that can help you succeed in this field as well. These include:
Communication skills
Great Data Intuition
Problem-solving skills
Critical thinking skills
Project leadership skills