5 Key Trends in Data Science and Machine Learning Careers in 2019

Tips for securing a job or advancing a career in rapidly growing fields

We’re midway through 2019 and it is clear that the world of data science has already grown significantly since 2018. This is because data science, machine learning, and other forms of Artificial Intelligence will be a crucial part of the developing plans of organizations where data play a significant role.  Most organizations have already started to implement advanced data science and machine learning technologies. The obvious result? Data science and machine learning experts are high in demand.

Source: Indeed.com

A data scientist has to work with a massive amount of organized and unorganized data in order to give proper insight and help meet all business requirements and achieve company goals.

It’s a highly lucrative career path, with the average salary for a data scientist is more than $125,000.  Job opportunities in this field are expected to grow by 11 percent by the end of 2o21. According to Glassdoor’s top 50 jobs, data scientist and machine learning experts rank among the top 10. If you are an aspiring data scientist, one thing you should know for sure is that you will be working in a highly rewarding profession.

The image above is a survey done by Deloitte that clearly shows 65.2 percent of respondents use some forms of analytics to meet their business needs.

We are all already in the rapidly-evolving world of data science, machine learning, and AI and it is vital to stay competitive by keeping up with the current trends. Below are the five top trends in data science and machine learning in 2019.

Specialize in one specific industry

The role of data scientist are not limited to one industry. We all know that industries like financial services, retail, products manufacturing, and logistics services are in a dire need of advanced data science and machine learning systems. It is expected that the role of data scientists and machine learning experts will be commonplace in most industries. A large number of companies are looking for industry-specific experience. So, it is important to do some serious research on the industry in which you would like to work and hone your skills to make your CV stand apart from the crowd.

Balance academic achievements with on-job training

A lot of data scientist roles require having mathematics or statistics as a primary educational background. However, it is not mandatory to specialize in these subjects. You can adopt some specific skill sets to fulfill the requirements of specific industries and you do so easily by attending a professional development course or online institute. It is best to take a career-focused approach and go for certification on big-data and machine learning to give a strong boost to your resume.

Implement machine learning with the data analytic experience

Most organizations love to manipulate and do data cleansing by themselves. Why? To build effective reports that can give an insightful overview of their business. This is exactly why data analysts play a critical role in the field of data science.  When it comes to analyzing big data sets, quantitative analysis plays a major role. The quantitative analysis skill will help you in several ways. For example, enhancing your knowledge to do experimental analysis, scaling up the entire data strategy, and implementing machine learning.

GDPR is the reason behind highly sophisticated data protection demand

General Data Protection Regulation (GDPR) is highly focused on the data protection rights of all the individuals in the European Union. If any company is working with the data of European individuals, the company is accountable for managing and storing personal data in more advanced and secure ways. The impact of this GDPR is far-reaching and as a data scientist, you must understand this impact and use data wisely, especially when you are into an e-commerce web development venture that include audiences from the European countries.

Update Your Technical Skills

If you wish to climb the ladder to the top and succeed in the field of data science, you should not be limited to one technology or platform. R & SAS are slowly losing popularity in product industries. The reason is very simple — lack of production grade solutions. Meanwhile, Python is becoming more in demand. These days, professionals skilled in Python are becoming much more in demand ever than before.

Glassdoor published an analysis of the 10 crucial software skills for data  scientists on their site. Here’s the comparison of how often the term “Data Scientist” appeared on their website compared to the to the average on LinkedIn, Indeed, SimplyHired, and Monster in October 2018.

Careers in data science have become a latest trend across the world and all the credit goes to the skyrocketing importance of data-focused decision in consumer oriented businesses.