In many ways, Machine learning and data science might sound like the two sides of the two coins and yet they may demonstrate extremely different traits, especially if they are applied to various applications and projects. Therefore, it’s very important to distinguish between data science and machine learning and how machine learning is used in data science projects.
So let’s work it out together.
By definition, data science is the practical application of scientific knowledge of working with data, by volume, variety, and veracity. It entails the extraction of insights from various types of data, even if it means the application of automation and AI or machine learning tools.
By definition, machine learning is the scientific domain within AI that is built on algorithms related to computer’s ability to extract data based insights from structured and unstructured data. In its advanced levels, machine learning can do a variety of tasks, including data management and data analytics for Big Data teams. The applications can be understood very well through practical classes organized by Machine Learning training in Bangalore.
So, how Machine Learning applies to Data Science?
Experts explain data science as a combination of IT architecture, networking concepts, and software development to efficiently handle data science approaches for business management purposes. Researchers and academics place extreme importance on the role of Machine Learning training for data science programs.
Why? Because machine learning simplifies and exemplifies the way techniques and tools can be used by data scientists to teach computers to learn from available data. It’s called supervised learning.
We are slowly moving toward an era where machines can learn on their own using cognitive intelligence and augment this learning to extract insights for decision making — all these are part of the development of ‘unsupervised learning’ -This is where the role of machine learning training in data science come into play.
Machine Learning is not the Silver Bullet — Data is not the oil
Together, machine learning and data science hold extreme positions in the IT journeys. The ability to extract insights from data science depends not just on the ability to deploy machine learning, but also on the ability to augment machines to extrapolate results without manual inputs.
What Skills do I need to excel in Data Science projects?
Apart from data management and statistics, you need focused training on ML projects.
Here is a list of skills you should be able to master-
- Computer science basics
- Statistical modeling
- Data evaluation and modeling Programming
- NLP, Chatbots, and RPA
- Data architecture design
Therefore, we are looking at machine learning training in Bangalore to create useful insights with minimal intervention, and yet require highly skilled data analysts and machine learning engineers, and programmers to optimize ML projects for data science programs. The real value is derived from a mix of IT tools, ML development software, and human resources — that all come together to simplify processes, and not complicate them.