12 Hottest Trends in Data Science You Need to Watch Out For

Every year we are witnessing that Artificial Intelligence (AI) is booming and now there are many startups formed based on Artificial Intelligence. It is clear to everybody by far that Data Science is a problem-solving field.

With the availability of data everywhere, the science of using the data in a better way is gaining prominence. One can find lots of job offers in the field of Data Science by just doing a job search on any job portal. Let us now discuss the latest trends in Data Science.

Top Data Science Latest Trends 2019

1. Rapidly Growing IoT Industry

It is estimated that the worldwide technology spending on the Internet Of Things will cross $1 trillion by 2022, according to International Data Corporation (IDC) at an annual growth of 13.6%. It is also predicted that the IoT of the cellular industry will reach $3.5 billion in 2023 with a growth rate of 30% annually, according to Ericsson.

Now it has already become a common thing that we can control our home appliances like the air conditioner, television, etc. by just using our smartphones which have become possible only because of the Internet Of Things. Many companies are now investing in technology development due to the rapid growth trend of IoT devices such as smart devices like Microsoft Cortana and Google Assistant to automate regular things at home.

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All these rapid technological growth will lead to a vast amount of data collection, which will also lead to finding better means of managing and analyzing data in a proper and better manner. This will create a massive demand in the field of data science and also for data scientists.

2. Accessibility of Artificial Intelligence

Both small and large companies have been able to improve and enhance their overall business processes very efficiently by using Artificial Intelligence or AI. More complex tasks can be performed in a more precise and faster manner than humans by artificial intelligence.

Another best part about artificial intelligence is that it eliminates any chance of human error. It also improves the overall workflow along the way. Humans are now able to invest their time and focus more on critical tasks which in return enhances the quality of their service. Read: Real-world AI applications.

3. Predictive Analysis Evolution

Businesses can achieve their goals faster and are having a better competitive edge by including Big Data analysis in their crucial business strategies and decision making. Companies can find the reason for any specific events in real-time by using various tools in analyzing big data. The Predictive Analysis is very crucial in predicting what can happen in the future, which is done by analyzing the data.

Businesses are now able to create smarter business strategies by predicting customer behaviour using predictive analysis from the data collected. Thus, companies can retain the present number of customers and also target new customers in a better way.

4. Migration of Dark Data to Cloud

Dark Data is the type of data that is not transformed into digital format. This is a vast data reservoir that is not tapped yet. The dark data is going to be migrated to the cloud for predictive analysis which will be used by businesses to help them in more accurate future predictions.

5. Machine Learning

It is estimated that 40% of the work of data science will be on automation by 2020. There has been a rapid growth in the technology of machine learning, and this is the main factor for automation in machine learning. Businesses can extract smart and unique insights from Big Data by smartly using the combination of automation and powerful machine learning tools which cannot be obtained by skilled data analysts alone.

6. Rise of Regulations

GDPR has changed its policies related to data governance, and lots of companies are struggling to comply due to its fast implementation. These policies and regulations have affected data security, data handling, data processing, and consumer profiling. Now Businesses are needed to understand the impact of these regulations and policies on operations of future and current. Businesses take the help of Data scientists as they have proper knowledge about these rules and regulations related to Data Governance.

7. Competitive Edge

Those businesses which are up to date with present technology have a competitive edge in the present and future and are more likely to sustain because of their adaptability to the new technological trend. One should never stop at one toolset, platform or technology to become a good data analyst as the technology and solutions will keep evolving at a faster pace than ever before. There will be more demand in the market for experienced and skilful Data Science professionals.

8. Data Visualization and Storytelling

Data visualization and storytelling are reaching the next level every year, and many companies are moving to cloud from conventional data warehouses. Data will be more synchronized with the increasing use of cloud-based data platforms and integration tools within the organization. There will be higher accuracy in the storytelling as everyone will have only one version of the truth within the organization.

9. DataOps

The data pipeline is becoming more sophisticated, and now it requires even more governance and integration tools. DataOps is relatively a new concept which is growing faster. DataOps is a process of delivering enhanced quality of data and data analysis, implementation of automated testing, automating the examination, analysis of data, preparation of data and collection of data.

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10. Blockchain

Blockchain technology has become very popular, thanks to Bitcoin. But many people don’t know the applications of blockchain other than in cryptocurrency. Blockchain is one of the most secured ledgers in the world which has many varieties of applications. For Data Security, Blockchain will be extensively used, and it has a far way to go in the future. Read about reasons you should learn blockchain technology.

11. Artificial Intelligence and Quantum Computing

Quantum computing is the most trending topic nowadays, which is being very actively researched by large companies like Google. As of now, Google claims to have to build a Quantum Computer which can do the calculation of 10 years by a supercomputer within 200 seconds by a quantum computer. Quantum computing has the potential to become the most significant quantum leap since the invention of the machine itself. All these indicate the extensive use of Big Data in the future in a much faster, efficient and straightforward manner.

12. Right to Explanation

In the future, lots of things are going to be on automation, and automated decision making will be one of those. To make the decision making fully automatic, it has to be explainable. There are two significant components in Artificial Intelligence (AI) which is very important to make a fully automated decision. Firstly, Artificial Intelligence should adhere to all the principles, core values, applicable regulations, and fundamental rights to ensure ethical practices and purpose. Secondly, Artificial intelligence should be reliable and robust, technically so as not to cause any unintentional harm.

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Federated Learning

Federated learning accesses dispersed data with machine learning algorithms via edge devices like mobile phones or servers. It is one of the rising data science trends where the initial data is never forwarded to a centralized server. It remains connected to the tool. This strategy guarantees data security and privacy since no one else can access the data. Local data is used to train the localized versions of the algorithm. The learning outputs are later shared with a centralized server to generate a “”global”” model or algorithm. The edge devices can keep re-sharing data to continue learning.

Robotic Process Automation

Robotic process automation or software robotics leverage automation technologies to perform the tedious and repetitive tasks of human workers. The process combines APIs and UI interactions to perform monotonous tasks like moving files, filling in forms, extracting data, and more. 

RPA tools focus on deploying scripts that can emulate human processes. As a result, these tools can autonomously execute different activities and transactions across unrelated software solutions. While AI is data-driven, robotic process automation is primarily process-driven. 

Natural Language Processing

NLP revolves around providing computers with the ability to comprehend and decode natural language like human beings. It is one of the recent trends in data analytics that combines computational linguistics with machine learning, deep learning, and statistical models. NLP is the driving force behind computer programs that can translate one language into another, provide answers to spoken commands, and summarize large texts within a short span. 

In our daily lives, we come across the applications of NLP in voice-operated GPS systems, digital assistants, customer service chatbots, speech-to-text dictation software, and more. NLP also plays a huge role in streamlining business operations, increasing employee productivity, and more. 

TinyML and Small Data

Big data refers to the massive amount of digital data that we create, gather, and evaluate. The different algorithms used for processing this data are huge and not just big. It is one of the most recent trends in data science, with approximately 175 billion parameters. Therefore, it is the most complex and extensive system suitable for simulating human language. 

Big data is great when you are working with cloud-based systems with limitless bandwidth. But it’s not suitable in every scenario where machine learning has massive benefits. Therefore, small data is the solution for processing data faster and cognitively amidst time-sensitive, bandwidth-constrained situations. 

The concept of small data is closely associated with edge computing. While trying to avoid traffic collisions during an emergency, self-driving cars are not allowed to depend on a centralized cloud server for sending and receiving data. That’sThat’s where TinyML algorithms come to the rescue.

These algorithms are created to occupy the least amount of space and operate on low-powered hardware. Some common applications of TinyML include:

• Keyword spotting
• Object recognition and classification
• Machine monitoring
• Gesture recognition
• Audio detection

In the future, TinyML will be applied in different types of embedded systems, ranging from agricultural machinery to home appliances and wearables. 

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Conclusion

Data science has got a variety of applications and use cases. We hope this article has made clear the latest trends in data science and its benefits.

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