It’s a brand new year with new calendar pages! If 2020 looks no different than 2021 from the perspective of the data world, then you are very wrong. 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? Well, data science and machine learning experts are high in demand.
You might be curious to learn who a data scientist is, right? Well, a data scientist has to work with a massive amount of organized and unorganized data. Thinking why? Simply in order to give proper insight and help meet all business requirements to achieve the goal.
No wonder, it’s a highly lucrative career path and the average salary for a data scientist was more than 6 lacs per annum. Job opportunities in this field are expected to grow by 11% within 2o21. According to Glassdoor’s top 50 jobs in India, 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 fulfilling profession.
You are already in the fast-evolving world of data science, machine learning and AI. Therefore, you must stay competitive by keeping up with the current trends. Now, this article tells you about the five top trends in data science and machine learning in 2019.
You need to specialize in one specific industry:
The roles of a 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 system. We can really expect the role of data scientists and machine learning experts will be inevitable in most of the organizations. A major number of companies are looking for industry-specific experience. So, it is important to do some serious amount of research on the industry you would like to work for and hone your skills to make your CV stand apart from the crowd.
2. Balance your 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 will adopt some specific skill sets to fulfill the requirements of specific industries and you can easily do it by attending a professional development course or online institute. It will be best if you take a more career-focused approach and go for certification on big-data and machine learning to give a strong boost to your resume.
3. Implement machine learning with the data analytic experience:
Most organizations love to manipulate and do data cleansing by themselves. Why? To build effective reports which can give a prominent overview of their business. This is exactly why data analyst plays a critical role in the field of data science. When it comes to analyzing big datasets, quantitative analysis plays a major role. You need to develop this skill. 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.
4. 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 Indian 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.
5. Always 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. If you have been noticing the trend carefully then you will realize a very prominent change. 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 years, professionals skilled in Python are becoming much more popular in the hiring industry ever than before.