Data Science and Machine Learning

Data Science and Machine Learning: Broad and Narrow Terminology

First of all, data science is really a broad, overarching category of technology that encompasses many different types of projects and creations.

Machine learning has been one of the biggest advancements in the history of computing, and now it is believed to be capable of taking on significant roles in the field of big data and analytics. Big data analysis is a huge challenge from the perspective of businesses. For example, activities such as making sense of huge volumes of varied data formats, data preparation for analytics and filtering redundant data can consume a lot of resources. Hiring data scientists and specialists is an expensive proposition and not within every company’s means. Experts believe that machine learning is capable of automating many tasks related to analytics – both routine and complex. Automating machine learning can free up a lot of resources that can be used in more complex and innovative jobs. It seems that machine learning has been heading in that direction.

Data science is essentially the practice of working with big data. It emerged as Moore’s law and the proliferation of more efficient storage devices led to enormous amounts of data being collected by companies and other parties. Then, big data platforms and tools like Hadoop began to redefine computing by changing how data management works. Now, with cloud and containerization as well as brand new models, big data has become a major driver of the ways that we work and live.

In its simplest form, data science is the way we manage that data, from cleaning it and refining it to putting it to use in the form of insights.

The definition of machine learning is much narrower. In machine learning, technologies take in data and put it through algorithms, in order to simulate human cognitive processes described as “learning.” Data science Online Course In other words, having taken in the data and trained on it, the computer is able to provide its own results, where the technology seems to have learned from the processes that programmers put in place.

Data Science and Machine Learning Skill Sets

Another way to contrast data science and machine learning is to look at the different skills that are most valuable for professionals in either of these fields.

There’s a general consensus that data scientists benefit from deep analytical and mathematics skills, hands-on experience with database technologies, and knowledge of programming languages like Python or other packages that are used for parsing big data.

Anyone who’s interested in building a strong career in (data science) should gain key skills in three departments: analytics, programming and domain knowledge,for data science Course Going one level deeper, the following skills will help you carve out a niche as a data scientist: Strong knowledge of Python, SAS, R (and) Scala, hands-on experience in SQL database coding, ability to work with unstructured data from various sources like video and social media, understand multiple analytical functions (and) knowledge of machine learning.

Conclusion

Machine Learning and Data Science are more powerful fields which help us in decision making and predicting the future trends.