Data Science vs Machine Learning: Which is Better?

Table of Contents(TOC):

• Introduction
• Difference Between Data Science And Machine Learning
• Which Is Better? Data Science Vs Machine Learning
• How To Learn Data Science?
• How To Learn Machine Learning?
• Conclusion
• Bonus Points

Introduction

The rise of Big Data and the new emerging AI technologies has created a demand for Data Scientists and Machine Learning professionals. While these two do share some similarities and common ground, it is still important to understand their individual nuances. This article will compare Data Science vs Machine Learning and help you understand which one is right for you.

Difference Between Data Science And Machine Learning

The main difference between Data Science and Machine Learning is that Data Science is a field that focuses on processing data and extracting insights from various data to help other fields like ML and AI. Whereas, Machine Learning is a field that focuses on developing models and technologies using data and artificial intelligence.

A lot of the skills of Data Scientists and Machine Learning professionals overlap. But here are some of the more significant differences between the two:

Which Is Better? Data Science Vs Machine Learning

Both Data Science and Machine Learning lead to rewarding careers. But which one suits better for your interests and aspirations?

Data Science is ideal for you if:

• You enjoy working with data and extracting insights.
• You are good at statistical analysis and data visualization.
• You want to keep your options open for working in various industries.

Machine Learning is ideally suited for you if:

• You want to create intelligence systems and models.
• You understand Maths and Programming.
• You want to work with Deep Learning and AI technologies.

How To Learn Data Science?

• Build a Foundation

Data Science is a vast domain, and depending on your interest, you can build the right foundation for a successful career ahead. Learning the Basics of Data Science can help you better understand the field and gain knowledge around topics like Data Analytics, Machine Learning, Data Technology, and more.

• Learn the Skills and Tools

While the Basics course will help you learn the fundamental skills and knowledge, you also need to get familiar with the tools used in the industry. A more comprehensive course like the Executive Diploma in Data Analytics can help you with that.

This course will help you learn about the various types of data and how each can be used in decision-making. You will learn Business Analytics and its various methods and models. This course will also help you improve your MS Excel skills for Data Visualization. You will learn to use tables and charts and create data dashboards in Excel.

• Start Working on Data Science Projects

Practicing your learnings will help you better understand all the theoretical concepts. You can start building your own projects or get work experience by applying to Data Analytics career roles. Not only will this help you create a work portfolio but also get better at problem-solving and data-driven decision making.

How To Learn Machine Learning?

• Build a Foundation

The first step to learning Machine Learning is building a strong foundation in Computer Science, Programming, and Mathematics. These subjects will help you better understand how ML models and algorithms work. After you have a basic understanding of these, you can gain fundamental knowledge about ML Algorithms from a course like the Basics of Machine Learning Algorithms.

This course will help you understand ML topics like Logistic Regression, K-Nearest Neighbors (K-NN), Support Vector Machines (SVM), Decision Trees, Conditional Probability, Bayes Theorem, Naive Bayes analysis, and more.

• Learn the Skills and Tools

Once you have foundational knowledge from these Machine Learning courses, you can focus on learning the more comprehensive tools and skills that will help you in practice. 

The Executive Diploma in Machine Learning course can help you learn Machine Learning from the fundamentals. You will learn about ML Modelling and design, Hypothesis Analysis, Regression Analysis & Grouping Theory, Classification Functionality & Solving Methodology, Naive Bayes Analysis, Neural network topology, Reinforcement Learning, Deep Learning, Python, and more.

• Start Working on Machine Learning Projects

These Machine Learning courses will prepare you to start working on ML projects and that will help you gain practical experience. You can then start your career in Machine Learning roles like Machine Learning Engineer, AI Engineer, AI/ML Researcher, and more.

Conclusion

The major difference between Data Science and Machine Learning is that they lead to different career paths. The good news is that if you have a background in Maths or Computer Science, you can pursue a career in either of these domains.

When it comes to Data Science vs Machine Learning both domains offer great opportunities.

Bonus Points:

• Data Science vs Machine Learning both fields offer promising careers and salaries.
• In Data Science, Data Architects are some of the highest-paid professionals.