The Role of Machine Learning in Data Science?

Role of machine learning in data science

Table of Contents

The curious you must be thinking that “What is the Role of Machine Learning in Data Science?”

Let us hear a quick story to target this query of yours.

In school, you did so many science projects. In those projects, your parents used to guide you with the technical aspects of the machines, connect wirings, etc., your teacher helped you make the related concepts clear, your library helped you with the required necessary study materials, and so on.

If we want to replace the tasks of all these mentors with one technological method, then Data Science is the answer. It is a mega-field study involving advanced tools, techniques, and processes of Maths and Statistics, Data Visualisation, Data Analytics, and so on.

Machine Learning (ML) is the process utilised to run mathematical calculations automatically, self-learns from them and form meaningful predictions.

Let us understand the topic of our interest in detail here in this blog. Keep reading further.

Key Takeaways

  • Know if Machine Learning is a Part of Data Science or Not.
  • Understand the Difference Between Data Science VS Machine Learning.
  • Learn the Role of Machine Learning in Data Science.

Is Machine Learning Part of Data Science?

To address the topic in discussion properly, let us get a better understanding of the key terms Data Science and Machine Learning first.

In the technology field, Data science is the discipline which aims to bring out critical, meaningful information from unstructured data, like huge chunks of text, numbers, and audio-visuals. This helps in better decision-making and project modelling for a business.

A Data scientist uses scientific tools and techniques like statistics, pattern recognition, data analytics, machine learning, data mining, data processing, visualisations, and so on, and has proficiency in programming languages like Python, R, Scala, SAS and SQL coding skills.

On the other side, ML is a different technique where its algorithms use processes like Deep Learning, Neural Network Algorithms, Statistical Algorithms, Computer Vision, and many more to autonomously learn and self-teach the machine from the big data, and make meaningful patterns, do predictive analysis, and give solutions for complex problems that the business or research is experiencing or may stumble upon in future. 

Now, the Relationship Between Data Science and Machine Learning can be understood as follows:

Machine Learning and Data Science are different disciplines with overlapping tools and techniques, i.e., ML uses techniques of Data Science in its processes. Likewise, Data Science utilises the tools and methods involved in ML.

You can better understand this using the following Venn diagram:

So, we can understand that one has to learn the concepts involved in Data Science, Machine Learning, and many other disciplines to become a Data Scientist or a Machine Learning Programmer. They are not just an isolated study of the subjects.

Must Read: Difference Between Artificial Intelligence, Machine Learning And Deep Learning

Data Science VS Machine Learning

The difference between ML and Data Science is as follows:

DATA SCIENCEMACHINE LEARNING
1. Data Science techniques extract, process, and interpret meaningful data from an unstructured bulk of data.

2. It handles and assesses data and manages the data-based models.

3. It requires the techniques of data analytics, statistical models, data visualisation tools, and many more.

4. It includes the steps of data gathering, data manipulation, cleaning, data analysis, and so on. 
Example: Myntra uses data science to predict its valuation or revenue.
1. ML enables the programming machine to learn, form patterns and, make predictions by making sense of a given bulk of data.

2. It handles data networks to learn, relearn, and predict solutions.

3. It utilises deep learning algorithms, neural networks, statistical models, data science, and so on.

4. It includes different type of machine learning methods, like Supervised, Unsupervised and Reinforcement.
Example: Google car, Alexa, Ola apps, and recommendation engines of Instagram and Netflix, etc., use ML to predict the next steps or choices in real-time.

What is the Role of Machine Learning in Data Science?

Machine learning plays a big part in the working methods of Data Science; the top 7 of them are as follows:

  1. Earlier, data scientists deduced solutions based on trial and error. The super-fast ML techniques extract and interpret the voluminous data within seconds, analyse it and find patterns and multiple ways to handle a situation. 
  1. The continuously evolving tools and techniques in ML contribute to rise of major transformations in data science and the world of businesses.
  1. ML offers room for data scientists to manoeuvre with data and predict highly precise solutions for businesses.
  1. ML catalyses real-time processing for data-driven or non-standard models. 
  1. Personalised recommendations by the recommendation engine allowing more customer interaction and customer satisfaction, which is beneficial for businesses.
  1. High-density data is handled quickly and efficiently by ML-based systems, letting data scientists focus on more important tasks.
  1. It helps remove any possibility of human-based errors.

Conclusion 

Technologies are shifting from solving basic numerical problems to searching for new planets and solving complex models of social problems like city road-network planning, finding cures for cancer, and so many more. 

Supercomputers solve complex problems in minutes to replicate the workings of the human brain. Machines are reaching new levels of ubiquity.

To leverage these technologies, you must get a deep understanding of how they work. Verzeo’s Certification Courses will help you cover all the aspects of Machine Learning and Data Science.

Frequently Asked Questions (FAQs)

Which is better, data science or machine learning?

Both Data Science and Machine Learning are good career choices, depending on your interests in the field. For both, you must have programming language expertise and coding skills, as well as knowledge of techniques like statistical analysis, data visualisation, machine learning, data analytics, and so on.

Is machine learning more data science or computer science?

For Data Science, one must learn Computer Science properly, and for Machine Learning, one must have a complete understanding of Data Science clearly. So depending on the Machine Learning application, the area of overlap between Data Science and Computer Science differs.

Is data science necessary for machine learning?

Yes, a deep knowledge of Data Science is a basic requirement to develop machine learning algorithms.

Who earns more, ML engineer or data scientist?

Although both ML Engineers and Data Scientists earn almost similar salaries, in recent trends, an ML engineer is earning slightly more as the applications and uses of ML have been on therising graph in recent years.

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