Data is a goldmine for organisations wishing to leverage the digital revolution. We are sweeping into a world where pretty much everything is tracked and analysed. The impact on data science and its sector is huge. New roles and requirements are popping up everywhere in response to the growth of data.
Here are some tips for getting, and staying, in the world of data science.
What is a data scientist?
The job of ‘data scientist’ falls into different camps. The job titles of data analyst, test manager, and data engineer are all jobs that can be categorised under the umbrella of ‘Data Science.’
Broadly speaking, data scientists work their way through both unstructured and structured data to provide insights. These insights are then analysed and applied to help organisations meet specific business needs and goals.
With this being such a wide discipline, data science captures the hearts and minds of many tech and digital professionals. There is a route for everyone. Perhaps this is why data science is so popular.
We are contacted by more candidates requesting advice for routes into data science than any other discipline.
Data Science is incredibly popular, but it’s also incredibly competitive. Whether you are already a data scientist or an aspiring one, there are some key industry trends you’ll need to be aware of to make sure you stand out in a saturated market.
Data Science industry trends.
Specialise.
Every industry can harness the business gains that its data can reward. As a result, no specific industry is off limits. However, in the long run, companies will be looking for industry-specific experience. With that in mind, it’s advisable that you pick an industry and refine your skills within that market to make your CV stand out.
Currently, the financial services, manufacturing, and logistics sectors are all emerging markets. There has also been a recent growth in the popularity of government-focused data scientist roles.
If you are looking for a sector that has extreme potential for continued growth, the financial services sector may be an area to target.
The volume of account and transaction data used in this industry is a high-value target for potential data breaches. Security, compliance, and fraud detection are areas of major concern for organisations within the FS sector.
Team academic qualifications with industry learning.
Grabbing the attention of a hiring manager or recruiter for a data science job often begins with holding a high level of academic qualification in maths or statistics. A Ph.D. or at the very least an MSc is often at the top of most wish lists.
However, that’s only the beginning.
Upskilling is critical and you’ll also be expected to meet the needs of the industry. You can do this through consistently topping up your skill set. This includes attending professional development courses and boot camps, for example. Your priority will be to keep up to date with all the latest data science trends and technologies.
By specialising in a particular area, you’ll be able to make sure your professional development choices are relevant to the path you wish to travel.
Keep your technical skills up to date.
The world of data science is complex. You need to demonstrate the most relevant and up-to-date skills to make sure you stand out to employers. By proactively taking the initiative to upskill and extend your experience you will reap the rewards and enjoy a lucrative and fulfilling career.
From a modelling perspective, SAS, R, and Python are the common industry norms. Python is the number 1 programming language for data science and machine learning.
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. Data scientists should make sure they are well equipped in these areas.
Business Intelligence.
Business Intelligence is emerging as the next-gen evolution in data science. People within the data science world will need BI skills.
Effective Business intelligence requires the ability to readily and effectively communicate with non-tech professionals who need to work with the insights of your data. Marketing or logistics teams for example. You’ll need to interpret the data in a clear way and explain the analytics and insights you have gleaned from the work.
In terms of hard BI skills, SQL programming skills show no signs of decreasing in popularity as a way of managing data. Meanwhile, Tableau s a key BI tool for data visualisation that crosses over into data science.
We hope this helps you on your path to becoming a data scientist.
For more advice on securing a data scientist job, you may like to head to our related blogs.
How to prepare for a data science interview.
CV Essentials. Data Scientist.
Part 1. The challenges associated with Big Data and how to solve them.
Part 2. The challenges associated with Big Data and how to solve them.
Data Science vs AI. What’s the difference?
