If you want to be a data scientist, you need to know about these 6 trends
Happily reflecting on the numerous opportunities within data science. Image: mimagephotography/Shutterstock

If you want to be a data scientist, you need to know about these 6 trends

25 Oct 2017

Data scientists are in demand, and candidates with the right mix of skills will be rewarded with a future-proofed and lucrative career. Here are some things to keep in mind when pursuing a career in data science.

Data is the new corporate currency, as advancing digitisation sweeps every horizontal and vertical market the world over. The impact on the data science sector is far-reaching and, as a result, a range of new roles and skillsets are in demand.

In the simplest terms, a data scientist hunts through massive amounts of unstructured and structured data to provide insights and help meet specific business needs and goals.

A data scientist also wears many different hats. The skills required as a data analyst, IT architect, test manager and data visualiser are all required under the data science umbrella, for example.

It’s also a highly lucrative career. The average salary for a data scientist was more than $111,000 in 2016, and the Bureau of Labor Statistics predicts that jobs in this field will grow by 11pc by 2024.

‘Data scientist’ also ranked as the best job across every industry, according to Glassdoor’s 50 Best Jobs in America report, so you will be working in a rewarding profession.

Clearly, the data science sector is, and will continue to be, a highly competitive market.

If you want to stand out from the crowd to capitalise on the opportunities a career in data science has to offer, here are six global trends you need to be aware of.

1. All industries are open, but you should try to specialise

Data scientist roles are not constrained to one dominant industry.

Financial services, manufacturing and logistics sectors are all trending as emerging markets, together with a recent growth in popularity of government-focused data scientist roles. However, we expect the role of the data scientist to be ubiquitous across all industries.

That said, companies are looking for industry-specific experience, so make sure you research your preferred sector and hone your skills to make your CV stand out to recruiters.

For example, data security specialists are much sought after in the financial services sector, as the account and transaction data used in this industry is a high-value target to potential data breaches.

For data scientists in the financial services industry, security and compliance, as well as fraud detection, are major concerns.

2. Balance robust academic achievements with on-the-job learning

Many data science roles require a PhD in mathematics or statistics from a top university. While this level of academic training is not a must-have for all data scientist roles, it will command the attention of prospective employers, as half of those working in data science have a PhD, whereas less than 2pc of people in the US over 25 years old have a doctorate.

You will also need to develop certain skillsets to meet specific industry needs by attending professional development courses, online classes and bootcamps.

Additionally, you may want to take a more proactive approach and consider a big-data certification to really boost your CV.

Upskilling is very important in terms of growth, and candidates need to familiarise themselves with the latest technologies and trends.

As previously mentioned, you need to research your market of interest and know what you want to specialise in. Attending meet-ups and classroom training are both great ways to do this, and try to balance formal training with on-the-job learning.

3. Data analytics experience is essential, machine learning helps

Data analyst roles are particularly in demand within the data science field. This is because businesses want to manipulate and cleanse their data to build reports that give a clear overview of their business.

Quantitative analysis is an important skill to analyse big datasets. It will help you to improve your ability to run experimental analysis, scale your data strategy and implement machine learning.

As a broad discipline, data science often overlaps with the machine learning sector, AI and deep learning.

You may want to further research these related disciplines and borrow techniques from them to help you better manage the large unstructured datasets you will have to work with as a data scientist.

4. The GDPR is increasing data governance demand

As companies scramble to comply with the impending General Data Protection Regulation (GDPR) on 25 May 2018, demand for data governance experience is escalating.

The GDPR will strengthen the data protection rights for all individuals within the European Union, but any company working with a European country must comply, so the effects are far-reaching.

The regulation is predicted to create demand for at least 75,000 data protection officer positions worldwide, research reveals.

Within data science, the GDPR imposes limits on data processing and consumer profiling, and increases the accountability of organisations storing and managing personal data.

It’s a vital piece of legislation and, as a data scientist, you must understand its impact.

5. Make sure you have a solid business intelligence foundation

While data science is seen by many as the next evolution of business intelligence (BI), those working in this sector need to retain some basic BI skills.

For example, communication is a critical soft skill. You need to be able to describe the data you are working with, and explain the analytics and insights you have extrapolated from that work.

Relaying complex technical information to non-technical professionals requires clear and effective communication.

For your hard skillset, SQL programming skills show no signs of decreasing in popularity as a core method to manage data, and Tableau is a key BI tool for data visualisation that crosses over into the data science sector.

6. Keep your technical skills up to date

You should not put all your stock in one technology or platform if you want to forge a career as a data scientist.

From a modelling perspective, SAS, R and Python are the common industry norms, and Apache Hadoop is emerging as the common framework. Many organisations are also turning to NoSQL, HBase and MongoDB databases to store large volumes of complex data.

Power BI, Teradata, ETL (both Informatica and SSIS) and IBM Db2 are all additional industry-leading tools in the data management sector that you should be aware of.

The complexity of data science means you need to demonstrate the most relevant skills and experience to this industry.

If you can achieve this by proactively upskilling and extending your experience, you will be rewarded with a lucrative and fulfilling career.

By Adam Shapley

Adam is a senior regional director of Hays in Australia and New Zealand. He is responsible for the strategic direction of the Hays IT specialism across the region.

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