AI vs ML: What is the future of data science?

In the past few years, the world has witnessed a steep and continuing rise in data, and in fact, data rules the world we live in. The amount of data that we generate each day is beyond imagination and there is a growing demand for professionals who are capable of organizing this humongous pile of data to offer meaningful insights with the help of data science. So, the future of data science is what decides the future of the business world. But to achieve these high-value predictions that can guide better decisions and smart actions, data scientists need the help of Artificial Intelligence and Machine Learning. 

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So, before diving deep into the future of data science, let’s take a look at Artificial Intelligence and Machine Learning (AI vs ML) in terms of Data science and how it drives the world of data.

AI vs ML

In simple terms, Artificial Intelligence is a field of technology that aims to simulate human intelligence in machines. It is modeled after the natural intelligence that is possessed by animals and humans and it makes use of algorithms to perform autonomous actions. Machine Learning is one of the important areas of Artificial Intelligence in which computers understand, grow, and develop by themselves when they are fed with new data, without the need for human intervention. So, ML is just a sub-field of AI.

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How AI and ML impact the future of data science?

Business logistics

AI and ML play a huge role in maximizing the potential of data analysis. Employing AI and ML in data science has led to the development of many business tools and applications that predict market trends, customer behaviours, and target demographics. Also, AI-driven marketing analytics helps companies maximize their data analysis potential.

Customer interactions

Customer outreach is one of the key areas in which data science will play a huge role in the future. With data science at their disposal, more businesses are asking themselves how best to use customer data. With AI-powered analysis, we can expect to see customer interactions and preference-detection tailored with increasing speed. 

Deep learning

When it comes to data science and analytics, deep learning is often raised as a potential solution to automatically extract meaningful patterns from large datasets for decision making. The application of pre-described rules with specific logic and decisions is still invaluable in helping users uncover meaningful opportunities.

The future of data science

AI and ML, and data science can work hand in hand. The use of ML and AI in many industries will act as a catalyst to push data science to increase relevance. So, basic levels of AI and ML will become a standard requirement for data scientists in the future. Moreover, in the future ML algorithms will hopefully be able to engage in machine unlearning as well. This might be the next step in increasing security with AI. This will have a substantial impact on the future of data science. However, machines are still not truly intelligent in the way that we comprehend intelligence. Machines are a significant asset but they are still only complementary to human intelligence and innovation.

Thus, the future of data science relies on the advancements of AI and ML along with human innovation. With AI and ML being such a craze, data scientists need to learn them. This is why Praxis has introduced a revolutionary, 9-month full-time post-graduate program in Data Science that provides advanced-level training on AI and ML applications and algorithms. With our vast experience in business education, we offer students both the time to understand the complex theory and practice of data science concepts and the guidance from knowledgeable faculty who are available on campus for mentoring. We also have a well-structured campus placement program that ensures interview opportunities with the most significant companies in the field.

Image by Gerd Altmann from Pixabay