What is Data Science?
Data Science is an amalgamation of data interference, algorithm development, and technology that enables professionals to solve analytically complex problems.
Data Science is essentially analyzing and churning out findings or insights from data. This scrutiny of data helps organizations in improving and making smarter decisions.
What is Machine Learning?
Machine Learning is the science of enabling computers or systems to act without being explicitly programmed. It stresses on the development of computer programs that can access and learn from data.
The relationship between Machine Learning and Data Science
The ultimate goal of Data Science is to predict, automate and perform transactions in real time. Examples include purchasing internet traffic or automatically generating content.
Machine Learning helps analyze huge volumes of data and eases the tasks of data scientists by automating processes. Machine Learning has been able to uplift the processes of data extraction and interpretation by replacing the traditional methods to automated ones.
It is also important for data scientists to keep polishing their Machine Learning skills in order to stay ahead by adapting to changes and use Machine Learning techniques and tools rather than other redundant methods.
Evolution of Data Science with the popularity of Machine Learning
Machine Learning and Data Science go hand in hand. Machine Learning itself involves the generalization of data. Thus, machines learn from data. Usage of Machine Learning in any industry will automatically increase the relevance of Data Science. In the coming years, data scientists will be requiring at least the basic levels of Machine Learning algorithms. Thus, we can say that the evaluation of Machine Learning is one of the most important Data Science skills.
Some real-world examples of Data Science and Machine Learning
Netflix uses a blend of Data Science and Machine Learning while:
- Recommending movies
- Deciding personalized artwork for the movies
- Picking the best frames from movies for the editors
- Experimenting with algorithms
- Optimizing various stages of production
At Facebook, Data Scientists employ a number of methods to complete tasks, the prominent one being Machine Learning. One of the tasks is creating audience interests for ad targeting.
Machine Learning has played an important role across core Twitter products that were previously not driven by Machine Learning and the company uses Data Science to handle the humongous volumes of data that it receives. The other major task that it carries out is the cropping of photos to highlight the most interesting part of the photo.
- Retail websites
Retail websites uses these techniques to predict and recommend products and understand when to announce discounts.
- Cyber Security Organizations
Cyber security organizations are leveraging both fields to detect fraud, prevent phishing and ward off cyber-attacks.
What do Data Scientists say?
Data Science is all about infrastructure, testing, Machine Learning for decision making and data products.
Here are some excerpts taken from an article published in Harvard Business Review.
Emily Robinson, a Data Scientist at Data Camp, segregates data scientists into two categories, one of them being the set of data scientists involved in building Machine Learning models.
Jonathan Nolis, a Data Science consultant, breaks Data Science into three components, namely business intelligence, decision science and Machine Learning.
Basis the details and examples given above, it can be observed that Machine Learning is a crucial part of Data Science. Also, both the domains are highly in-demand within the industry. Thus, a combination of polished skill sets and relevant experience can boost your career in these trending domains.