Data is of utmost importance these days. As long as data is important, data science is important. Because it is essential to have hidden information out of data. Data Science has had a significant impact on businesses and has revolutionized the way decisions are taken in organizations in order to ensure continual improvement.
The business landscape is constantly evolving and the data generated each second is tremendous. With the increasing human interaction with technology for every possible task, the data is formed in raw format and is of great value to businesses as well as research.
This is why there is a surge in demand for Data Science across the world, across every sector. Below are mentioned some powerful stats that make this clear.
• According to a report by Market Research, the annual job growth for the job roles in Data Science is expected to be at a whopping 28% by the year 2026.
• As per a report by the US Bureau of Labor Statistics, around 11.5 million jobs in data science are expected by the year 2026.
• The median annual salary of a Data Scientist may range anywhere between INR 4.5 lacs to INR 22 Lacs.
• According to a report by IDC, the investment in Data Science and cognitive computing reached USD 52.2 billion last year.
• Numerous big names are looking for Data Scientists, including Apple, Microsoft, Amazon, Facebook, Google, Goldmann Sachs, JPMorgan Chase, and many more.
The main objective of Data Science is to collect, process, analyze, and interpret the findings using data visualization techniques to assist companies in making smart decisions. These are the reasons that professionals find Data Science Certification a great way to advance their careers in the field of Dara Science.
In this article, you will get to know about some crucial data science technologies that are giving shape to the future for the better.
Important Data Science Technologies
1. Artificial Intelligence
CMO reports that around 47% of organizations that are digitally mature have artificial intelligence in line with their processes. AI has delved into organizations so well that it assists in performing tedious tasks with just a few clicks. AI makes the collection and integration of data easier and faster.
Because of the high speed of processing or working with data, it has highly affected our routine as well as our professional lives. From predictive analytics to speech and image recognition to self-driving cars, AI is evolving at an alarming rate. This way, it provides businesses with a competitive edge.
2. Internet of Things (IoT)
With the massively increased use of IoT devices across the globe, a tremendous amount of data is generated by devices, sensors, applications, and other smart gadgets. IoTand Data Science have correlated in the sense that data science is important for the growth of IoT.
IoT algorithms based on data science transform the fuel that IoT operates on—data—into something useful.
3. Cloud Services
Everything is on the cloud these days. Cloud services prove to be cost-effective and powerful ways of storing tremendous amounts of data that are generated every second. Cloud platforms such as AWS (Amazon Web Services), Microsoft Azure, Google Platform, etc., offer services like networking, storage, messaging, content delivery, database, computing, analytics, machine learning, and more. The services are generally provided at affordable costs.
It is the data scientist who analyzes the data present on the cloud.
4. Big Data
Big Data refers to the massive volumes of data that we generate each second. It is characterized by the three Vs, volume, velocity, and variety. This means that Big Data is massive in volume and is generated at an alarming velocity in many different varieties.
Without big data, it would not have been possible to develop technologies like dark data migration and robust cybersecurity. Another outcome of using big data to assess the required information is the creation of intelligent bots. In contrast to being accumulated over a long period of time, according to Big data made simple, around 90% of the world’s data has just been produced in the last two years.
So, it has to be processed and analyzed by data scientists so that it can be used by organizations in order to make informed decisions.
5. AR/VR Systems
Augmented Reality (AR) and Virtual Reality (VR) systems are taking the digital world by storm. These technologies are specially meant to improve the interaction between machines and humans. With the help of natural language processing (NLP) and machine learning (ML) techniques, these systems automate the processes of extracting data insights which makes it easy for data scientists and analysts to extract patterns and trends in data.
6. Machine Learning
Data science extensively uses machine learning algorithms. This is because machine learning algorithms are capable of learning from algorithms by extracting information without requiring human intervention.
Machine learning is used in almost every industry these days, which includes healthcare and pharmaceuticals, logistics, manufacturing, banking, and more. It is accurate in diagnosing deadly diseases at early stages. It is also efficient in detecting fraudulent payments in the banking sector.
Some common use cases of machine learning include the “People You May Know” section on social media platforms, detecting patterns in information, and more.
Data Science and machine learning are highly related to each other.
7. Streaming Analytics
When it comes to processing real-time data, streaming analytics comes into action. These processes help data scientists to analyze information in real time. Since real-time analytics has become the need of an hour for almost every business, data scientists find it helpful to perform streaming analytics in support of making decisions wisely.
Conclusion
Apart from the techniques listed above, there are many more technologies that are widely used in Data Science, depending on different use cases. If you wish to practice these technologies hands-on, take up an online training course from an accredited institute such as Simplilearn. This enables you to learn the ways these techniques are used in different scenarios. Plus, real-life projects based on real-time data make it easy to learn data science technologies.
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