Data Science vs. Machine Learning: What's the Difference?

Data Science vs. Machine Learning: What's the Difference?

published on
Jul 2, 2024
4 Min REad
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Data Science and Machine Learning are two of the most talked-about fields in the world of technology today. As a fresh graduate or young professional looking for career guidance, you may wonder what they mean, their differences and similarities, or which one is better suited for your skills and interests.

In this article, we'll answer all these questions and more to help you make an informed decision.

Data Science vs. Machine Learning: Roles

Data science and machine learning are two closely related but distinct fields in the world of data and analytics. While data science is a broader field that encompasses a range of techniques and methods used for handling, analysing, and interpreting large datasets, machine learning is a more specialised field that focuses on building algorithms that can learn from data to make predictions or decisions.

 Here are some key differences between data science and machine learning:

Data Science

Machine Learning

Focuses on extracting insights from data using statistical analysis, visualization, and other techniques

Focuses on creating algorithms that allow machines to learn from data without being explicitly programmed

Uses a wide range of tools including SQL, Python, R, SAS, and Tableau

Uses tools such as TensorFlow, PyTorch scikit-learn for building models

Deals with structured as well as unstructured data such as text and images

Deals mainly with structured numerical or categorical data

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Data Science vs. Machine Learning: Tools

While both data science and machine learning share some similarities in terms of the skills required to work in these fields, they do differ significantly in terms of their goals, tools used, and types of data processed.

Here's a comparison table that outlines some key differences between these two fields:

Data Science

Machine Learning

Involves working with large datasets to extract insights that can help inform business decisions or solve complex problems

Involves building predictive models using large datasets to enable machines to make decisions or take actions

Requires skills such as statistical analysis, programming languages like Python or R, database management using SQL

Requires skills such as advanced statistical concepts like regression analysis or decision trees; deep learning libraries like Tensorflow

Tools used include SQL databases for storage; Python/R for scripting/analysis; Tableau for visualization

Tools used include scikit-learn or TensorFlow for model development; Jupyter notebooks for prototyping

Handles both structured and unstructured data such as text, images, and audio

Deals mainly with structured numerical or categorical data

Data Science vs. Machine Learning: Career Path

Both data science and machine learning offer exciting career paths with a range of opportunities to choose from.

Here are some traditional job roles in data science and machine learning:

Data Science

Machine Learning

Data Analyst

Machine Learning Engineer

Business Intelligence Analyst

AI Researcher

Data Scientist or Engineer

Computer Vision Engineer

Project Manager

NLP Engineer

Salaries in these fields vary greatly depending on the role, experience, and location. According to Glassdoor, the average salary for a data scientist in India is ₹9 lakhs per annum while the average salary for a machine learning engineer is around ₹10 lakhs per annum.

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Difference Between Data Science and Machine Learning

Data Science

Machine Learning

Data Science is a field about processes and systems to extract data from structured and semi-structured data.

Machine Learning is a field of study that gives computers the capability to learn without being explicitly programmed.

Need the entire analytics universe.

Combination of Machine and Data Science.

Branch that deals with data.

Machines utilize data science techniques to learn about the data.

Data in Data Science maybe or maybe not evolved from a machine or mechanical process.

It uses various techniques like regression and supervised clustering.

Data Science as a broader term not only focuses on algorithms statistics but also takes care of the data processing.

But it is only focused on algorithm statistics.

It is a broad term for multiple disciplines.

It fits within data science.

Many operations of data science that is, data gathering, data cleaning, data manipulation, etc.

It is three types: Unsupervised learning, Reinforcement learning, Supervised learning.

Example: Netflix uses Data Science technology.

Example: Facebook uses Machine Learning technology.

Education and Upskilling Courses

To get started in either field of data science or machine learning, there are many academic degrees available that can give you an advantage.

Here's a breakdown of some popular undergraduate and graduate degrees:

Bachelor's Degrees


B.Tech/B.E. Computer Science/IT/Data Science

₹6 - 10 lakhs

B.Sc. Mathematics/Statistics/Physics with Computer Science/Programming electives

₹1.5 - 3 lakhs


Master's Degrees


M.Sc./M.S. Data Science/Machine Learning/AI/Statistics

₹2 - 8 lakhs

M.Tech Computer Science with Data Science/Machine Learning specialisation

₹3 - 6 lakhs

There are also certification courses available that can help you upskill in data science or machine learning, such as Google Cloud Certified Professional Data Engineer, AWS Certified Machine Learning, and Microsoft Certified: Azure AI Fundamentals.

 In addition to academic degrees and certifications, there are many online courses available for upskilling in both fields.

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Industry Growth Prospects

The market demand for data scientists and machine learning engineers is growing rapidly.

According to a report by Gartner, the global business value derived from AI is expected to reach $1.2 trillion by 2023.

In India alone, the analytics market is expected to be worth $30 billion by 2025, with nearly 70% of the country's top companies planning to increase their hiring for these roles.

With WIZR's assistance, you can access certification programmes and courses that enable you to upskill without disrupting your current career path.


1. Which is more difficult, data science or machine learning?

Both fields require significant expertise, but they focus on different elements of data analysis. Data science focuses on finding insights within large datasets and creating visualisations, while machine learning involves building models that can make predictions or decisions based on inputted data.

2. Is it mandatory to have a degree to pursue a career in data science or machine learning?

While some employers may require bachelor's degrees in computer science, statistics, or related fields, there are several certification programmes available that allow individuals to develop the necessary skills and break into these industries without a formal degree.

3. How much can I earn as a data scientist in India?

According to Glassdoor estimates, the average salary for a data scientist in India is ₹9 lakhs per year, but this figure can vary based on experience level and employer.

4. Which specialisation should I choose if I want to build my career in machine learning?

Popular specialisations within machine learning include natural language processing (NLP), computer vision, and deep learning. Depending on your interests and goals, any of these areas could be an excellent choice.

5. How does WIZR help young professionals upskill with certification programmes and courses?

WIZR provides accessible financing options for individuals seeking career-focused upskilling and certification programmes tailored to their specific needs.

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