There are so many new skills to be learned today that it’s often confusing. You keep debating in your head whether it should be data science or artificial intelligence. Then there’s the evergreen question of whether it should be a formal degree or a beginner class or a comprehensive course etc. etc. And there goes the idea of learning a new skill in 2020 into the bin because it’s so much research even before stepping into it. But worry not, we’re here to lay out all the differences between these two technologies. This article will help you to make up your mind about the path you want to take.
Learning of Blog:
- All About Data Science
- Why Choose Data Science?
- All About Artificial Intelligence
- Types of Artificial Intelligence?
- Why Choose Artificial Intelligence?
All about Data Science
With data being produced every millisecond all over the world, data science has emerged as one of the most popular career fields for skilled professionals. Hypothetically data is the new gold and quite rightly so. Data Science basically refers to the field that uses multiple tools like algorithms and machine learning techniques to spot patterns and clues in raw/processed data. For in-depth knowledge on the same, you can sign up for the online certification on Data Science Developer. Primarily there are three methods of tracking, organizing and interpreting these data sets.
- Predictive Casual Analysis – Through this model, you can easily predict the possibility of an event or occurrence happening in the future. This is done by analyzing the record of the past. For example, the credit score of a customer can be easily calculated by running predictive causal analysis on their payment history.
- Prescriptive Analysis – This is an even smarter model. In the prescriptive analysis, along with scrutinising data, it has the ability to modify the data with effective variables. In simpler terms, it advises you better strategies to solve business solutions along with predicting. For example, the data that is collected by smart cars can actually be used to train vehicles.
- Machine Learning Algorithms – Working in data science you have to study datasets to spot patterns for predicting any kind of outcome. This is best done using machine learning algorithms which are also of two types, i.e. supervised and unsupervised. Whether it’s determining future trends or making better sense of a dataset, machine learning algorithms are very well suited to either cause.
Why choose Data Science?
Data Science has been ranked as one of the top jobs in the USA by Glassdoor for three years in a row. As more and more data is piling up, every industry, big or small requires data scientists. The challenge lies in the lack of qualified professionals who are willing to take up these posts. It also means a higher pay package due to the shortage.
Data Scientists are considered to be data-driven professionals with excellent technical skills. Along with a strong background in statistics and algebra, you will become well-equipped with R, Python, Apache Hadoop, MapReduce, Apache Spark, NoSQL Databases, Cloud Computing, D3, Apache Pig, Tableau, Github, iPython Notebooks etc. Moreover, communication and leadership skills come in handy. As a data scientist, you’ll get a chance to work in a result-oriented environment. Data scientists have some excellent skills sets to be called an organizational asset.
All About Artificial Intelligence (AI)
The credit for defining data science since the 1950s goes to the founders of this field, John McCarthy and Marvin Minsky. They described Artificial intelligence as any task that is being performed by a machine, for which a human would have to apply intelligence to accomplish it.
It can also be defined as the evolution of computer systems which are now capable of performing varied tasks that might/might not require any degree of human intelligence. Sample tasks could mean decision-making, solutions to complex-problems, object-detection etc. To learn more about AI, you can sign up for an online certification in Artificial Intelligence Expert or Artificial Intelligence Developer.
Types of Artificial Intelligence
- Artificial Narrow Intelligence (ANI) – Artificial Narrow Intelligence is sometimes referred to as rudimentary or weak artificial intelligence. This is because this type of AI can perform a very narrow set of specified tasks. The machine does not have the ability to think, hence it has limited output. It can only fulfil the functions which have been pre-defined for it.
Some examples of this are the ones you use in your daily life like Siri, Alexa, Sophia, Self-driving cars etc. Maximum research and AI-systems have been successful as ANI’s.
- Artificial General Intelligence – Artificial General Intelligence is known as advanced or strong artificial intelligence. It marks the point of evolution where machines will have the ability to think and make decisions just like any other human being. However, our research has not reached that point yet where we could site examples for this type of AI.
A lot of scientists, including Stephen Hawking, believe that the complete development of artificial general intelligence would be a threat to human beings. That is because machines would redesign themselves at an ever-increasing rate where on the other hand humans are limited by slow biological evolution.
- Artificial Super Intelligence – Artificial Super Intelligence is believed to be the stage of AI where it will completely outlive human intelligence. It’s still believed that this is a hypothetical stage. However, you must’ve seen or read a lot about it in fictional movies and books respectively.
Why Choose Artificial Intelligence?
Artificial Intelligence has a very vast scope because it includes Machine Learning, Deep Learning, Natural Language Processing, Robotics, Expert systems etc. Even functionality-wise AI has different branches. Reactive Machines AI, Limited Memory AI, Theory Of Mind AI and Self-aware AI. Reactive Machines AI only considers the present state of data and can make inferences based on that only without making any future predictions. Limited Memory AI is capable of making better decisions because it can study the past data available in its memory. Theory of Mind AI studies emotional human intelligence to understand and predict human behaviour. This is an advanced form of AI. Self-aware AI is also a hypothetical branch that assumes machines are capable of becoming self-aware. However, there is a lot of scope to develop and improvise if you work in this field. Finding better solutions to real-time problems is an AI-related job.
Conclusion
We hope that you got a brief overview of both technologies and it will help you to decide which one would be the best suited to your professional needs. Either way, signing up for a qualified certification from the Global Tech Council will give you a competitive edge over others.
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