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Five Data Science and Machine Learning Trends That Will Define Job Prospects in 2020

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Data Science and ML have been one of the most talked-about trends in 2019 and without any surprise, they will continue to be so in 2020 as well. From shopping online, hailing rides, ordering food to show binging and digital courses, today everything you indulge in is regulated or influenced by AI and Data Science in known and unknown ways.

This dramatic adoption of AI & Data Science in recent years has transitioned this trend from a niche into a mainstream. And with 2020 fueling this transition further, these two trends are going to be the norm of every industry soon.

This transitional phase is perfectly described in a Harvard Business Review article as,

 “Sooner or later, every technology transition from an elite niche to a mainstream tool. AI is now undergoing a similar transformation.… We’re entering an age in which just about anyone can leverage the power of intelligent algorithms to solve the problems that matter to them.”

To keep up with the trend and demand, businesses are also increasing their appetite for AI resources, resulting in an upsurge of AI jobs. According to Indeed, a leading job portal, AI job postings rose 57.9% from May 2017 to May 2018 and a whopping 136.3% between May 2016 and May 2017.

Given that data science and machine learning jobs will become the staple of every industry in 2020, these numbers are only expected to rise further. Among all the available jobs, machine learning engineer, deep learning engineer, senior data scientist, computer vision engineer, data scientist will be the top 5 sought-after – high demand and a fat paycheck – career opportunities.

Now that you know, AI, ML and Data Science resources will be at the heart of every tech business in 2020. Let’s dive deeper into understanding the trends that will command this change.

1.    Data Science in the Cloud

Due to a large number of companies increasingly moving to the cloud, the volume of data accumulated every day has also increased. This upsurge is thus pushing companies to invest their resources in not only storing data but also extracting meaningful information out of it.

To achieve this, companies are on a lookout for data scientists who can dive into data reservoirs to deduce meaning from the heap of structured and unstructured information.

Skill-wise data scientists are experts at mining, filtering and analyzing data in unprecedented ways. These resources have a high mathematical bent of mind which helps them to decipher complex and hidden patterns easily and accurately. The demand and supply gap in the data science field makes data scientists in-demand and high-paying resources.

2.    Natural Language Processing

The intelligence of a system can be marked by its capacity to simulate human-like behavior. With the inception of AI, businesses are now focused on training their machine to emulate human intelligence in its day to day tasks such as customer interaction, speech to text synthesis and more.

As languages are highly ambiguous in nature, thus training machines to understand the language without being stuck in the ambiguity is a tough task to achieve. Because of the complex nature of this work, NLP resources are hard to find and thus are counted in the niche category.

To start with, you can check if the candidate has prior experience working on any of the below NLP systems.

  • Google Natural Language API
  • Microsoft Linguistic Analysis API & Text Analytics API
  • Watson Natural Language Understanding
  • Stanford Core NLP
  • Natural Language Toolkit (NLTK)

3.    Machine Learning as a Service

As companies are increasingly dependent on the cloud for different aspects of their business, leading cloud providers are now taking it in their stride to constantly ramp up their deliverables. And providing ML intelligence is their utmost priority in the current tech scenario.

Gone are the times when companies were betting on IaaS, PaaS and SaaS resources to give an edge to their business and product. Now, the talks have shifted to ML resources and Machine Learning as a Service (MLaaS) model.

Not only that, the prominence of ML is set to gain pace in the forthcoming years. Stats also cite that, Machine Learning as a Service (MLaaS) market is expected to witness a CAGR of over 43% during the forecast period (2019-2024).

Thus, skilled resources with relevant ML experience or certification would be highly prominent in the company resource requirement.

4.    Embedded Analytics

Analytics has been the hottest trend in the chart of the tech industry for the last few years. However, with the inception of 2020, this trend would go a notch higher in its implementation with ‘embedded analytics’.

Embedded analytics allows a business to implant analytics in their home-developed tools, instead of purchasing an external analytics tool from the market. It makes the regular processes smart and intuitive with in-build dashboards and reports that are automatically created to present insight and actions based on every day changing data.

Here, the analytics options can span from static data dashboards, grid reports to dynamic data visualizations, interactive information charts. Many providers also give the reporting power in the hand of customers to enable them to create ad-hoc reports on their own as per requirement. For instance, HR analytics is one of the top usages of analytics where an increasing number of HR personnel are largely utilizing analytics by creating internal employee hiring, retention and skill trend reports on their own.

To provide embedded analytics, companies are looking for Business Intelligence Experts, Data architects and more. These resources are well-versed data architecture including importing data, filtering data, creating complex business reports, dashboards and visualizations.

5.    Data Privacy and Cyber Security

Data security and cyber-attacks are the most dreadful terms in the tech space. With countries rolling out different data security and protection regulations – GDPR and Data Protection Law –to prevent data theft and cyber-attacks, more and more companies have started heavily investing in the cybersecurity space. Moreover, with all the data present on the cloud, the vulnerability of cyber-attack has tremendously increased.

This is where companies are taking rescue of AI space. They are using AI, ML and Data Science to detect potential threats and data leaks in order to protect companies from data law breaches and security attacks. They’re collecting data constantly to monitor and curb any potential risks.

Due to this high-risk zone, companies are looking for cloud experts with data security knowledge. However, the resource crunch in this sector has been huge. Stats suggest that there is a zero-percent unemployment rate in cybersecurity, which means the opportunities in this field are endless.

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