Data scientists are in demand, and candidates with the right mix of skills will be rewarded with a long-lasting and lucrative career. Here are some things to keep in mind when pursuing a career in data science.
Data is the new currency for businesses as digitalization sweeps across all horizontal and vertical markets around the world. The impact on the data science sector is significant and as a result a range of new roles and skills are in demand.
Simply put, a data scientist sifts through massive amounts of unstructured and structured data to provide insights and help address 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 visualizer are all required in data science, for example.
It is also a very lucrative career. The average salary for a data scientist was over $111,000 in 2016, and the Bureau of Labor Statistics predicts that jobs in this field will grow 11% by 2024.
According to Glassdoor’s report of America’s 50 Best Jobs, “data scientist” is also ranked as the best job across all industries, so you’ll be working in a rewarding profession.
Clearly, the data science industry is and will continue to be a highly competitive market.
If you want to stand out from the crowd to take advantage of the opportunities a data science career has to offer, here are six global trends you need to be aware of.
1. All industries are open, but you should try to specialize
Data scientist roles are not limited to one dominant industry.
The financial services, manufacturing and logistics sectors are all emerging markets, along with a recent growth in the popularity of government-focused data scientist roles. However, we expect the role of the data scientist to be pervasive across all industries.
That said, companies are looking for industry-specific experience, so be sure to research your preferred industry and hone your skills to make your resume stand out to recruiters.
For example, data security specialists are in high demand in the financial services industry because the account and transaction data used in this industry is a high-value target for potential data breaches.
For data scientists in the financial services industry, security and compliance, as well as fraud detection, are top concerns.
2. Balance strong academic achievement 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 essential for all data scientist roles, it will catch the eye of potential employers, as half of those working in data science have a PhD, while less 2% of people in the United States are over the age of 25. years have a doctorate.
You will also need to develop certain skills to meet specific industry needs by participating in professional development courses, online courses, and bootcamps.
Additionally, you might want to take a more proactive approach and consider big data certification to really boost your resume.
Improving skills is very important in terms of growth and candidates should be familiar with the latest technologies and trends.
As mentioned earlier, you need to research your market of interest and know what you want to specialize in. Attending meetups and classroom training are two great ways to do this, and try to balance formal training with on-the-job learning.
3. Experience in data analysis is essential, machine learning helps
Data analyst roles are particularly in demand in the field of data science. Indeed, companies want to manipulate and clean their data to create reports that give a clear overview of their activity.
Quantitative analysis is an important skill for analyzing large data sets. This will help you improve your ability to run experimental analytics, scale your data strategy, and implement machine learning.
As a broad discipline, data science often overlaps with machine learning, AI, and deep learning.
You may want to research these related disciplines further and borrow techniques from them to help you better manage the large, unstructured datasets you will need to work with as a data scientist.
4. GDPR increases the demand for data governance
As businesses strive to comply with the impending General Data Protection Regulation (GDPR) on May 25, 2018, the demand for data governance experience is increasing.
The GDPR will strengthen data protection rights for all individuals within the European Union, but any business working with a European country must comply with it, so the effects are far-reaching.
The regulation is expected to create demand for at least 75,000 data protection officer positions globally, a study finds.
Within data science, the GDPR places limits on data processing and consumer profiling, and increases accountability for organizations that store and manage personal data.
This is essential legislation and as a data scientist you need to understand its impact.
5. Make sure you have a solid foundation in business intelligence
While data science is considered by many to be the next evolution in business intelligence (BI), those working in this sector need to maintain some basic BI skills.
For example, communication is an essential soft skill. You must be able to describe the data you are working with and explain the analyzes and insights you have extrapolated from this work.
Conveying complex technical information to non-technical professionals requires clear and effective communication.
For your technical skills, SQL programming skills show no signs of waning in popularity as a basic method for managing data, and Tableau is a key BI tool for data visualization that crosses the data science sector. data.
6. Keep your technical skills up to date
You don’t have to put all your stock in one technology or platform if you want to carve out a career as a data scientist.
From a modeling perspective, SAS, R, and Python are the common industry standards, and Apache Hadoop is emerging as the common framework. Many organizations 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 cutting-edge tools in the data management industry that you should be familiar with.
The complexity of data science means you need to demonstrate the most relevant skills and experience for this industry.
If you can achieve this by proactively improving your skills and expanding your experience, you will be rewarded with a lucrative and fulfilling career.
By Adam Shapley
Adam is senior regional manager for Hays in Australia and New Zealand. He is responsible for the strategic direction of Hays’ IT specialty in the region.