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Top 3 AI career Paths of All Time

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Artificial Intelligence is a part of computer science that empowers computers and other machines to mimic human intelligence. It is focused on simulating the human brain and performing tasks such as decision making, reasoning, text processing, visual perception, pattern recognition, etc. 

AI entails a few sub-field like Machine Learning, robotics, computer vision, and Deep Learning under its umbrella. AI is greatly influencing our society and bringing more positive changes. From big enterprises to daily tasks, AI is involved in almost every aspect of our lives. A few examples of AI applications include virtual assistants, google maps, E-payments, face recognition, text editors, recommendation systems, and more. 

As per the job description, the demand for AI professionals has increased more than 100% in the past few years. But, job-seekers have found to be leveled off when it comes to their technical skills. However, you can achieve technical excellence through Artificial Intelligence Course and make yourself ready for this lucrative career. 

Now, let’s discuss the top 3 career paths in Artificial Intelligence:

 

Data Scientist

Simply put, Data Scientists are responsible for solving company problems using the data. Data science is an interdisciplinary field that involves a huge amount of data being gathered, processed, and visualized to provide meaningful insights. Data Scientists crunch has to dive deep into the ocean of information and use their statistical and programming skills to generate resourceful solutions that benefit their organization.

According to Glassdoor, more than 3 million jobs for data scientists were available in the year 2020. And the demand is expected to grow more than 26% by the year 2026. Data scientists need to be good at both technical and non-technical skills including statistics, probability, AI, machine learning models, and programming languages like Python and R. 

 

Check out this 11 hours Data science course and learn it from scratch: 

Machine learning Engineer

Machine learning engineers work on the core AI projects that will be used for training, research, and data analysis. These professionals use computational mathematics, regression, natural language processions, deep learning algorithms, and other AI processes to work with colossal datasets and train different machines. 

Becoming a machine learning engineer requires an ample amount of knowledge and expertise in mathematics, programming languages, Agile development practices, Artificial intelligence, Neural networks, etc. You can follow up on this Artificial Intelligence tutorial and keep yourself updated with the latest trends going on in the industry. 

 

Business Intelligence(BI) Analyst

The third most popular application of AI has been  Business Intelligence and Analysis. Business intelligence is a collection of AI tools, statistical models, and data science that enables a business to collect raw data from unrelated sources and convert that information into visual insights. The person who deals with business intelligence and creates appealing reports is known as the BI Analyst.

 

BI Analysts have now become an integral part of the organization as they help it to analyze the data, find the relationship between seemingly unrelated datasets, and convert it into visualizations that even a non-technical person can understand. Here, AI enables them to generate reports, ask questions to their data in natural language and find new opportunities by doing market research. Apart from AI and data science, a BI analyst should also have extensive knowledge of cloud computing, SQL Servers, MS Excel, SQL Servers, data mining, and documentation. 

Conclusion

So, these were the top 3 career paths you can go for after learning Artificial Intelligence. However, there are a plethora of job roles available for AI professionals in different fields like Robotics, Product management, Big Data, Research, software development, and transportation. With the right skills and mindset, you can learn how to use AI applications to solve real-world problems and have a bright future. 

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