How Different Are Data Science And Machine Learning In Practice?

Data science is a discipline that aims to draw meaningful conclusions from data using a scientific approach. Machine learning, in turn, is a set of techniques used by data scientists to enable computers to learn from data. In a nutshell, data science and machine learning are a way of combining science, statistics and computers together.


Machine learning is an artificial intelligence area with economic, social, ethical, and technical implications. The science of computer algorithms allows programs to improve automatically as they gain experience. One way to achieve artificial intelligence is through machine learning. Machine learning involves working with small and large datasets, analyzing and comparing data to find common patterns and explore nuances.

What Is Data Science?

By definition, data science is the process of extracting information from data collected from a variety of different sources. Today, televisions, refrigerators, cars, lighting systems, etc., can generate data and thus provide valuable information. Data science uses various techniques to analyze and interpret large amounts of data, such as predictive modeling and machine learning algorithms.

What Is Machine Learning?

Machine learning is a complex field with many different dimensions. Sometimes even technical experts find it hard to imagine the entire world of machine learning and its place in business. However, many are now interested in ML and delve deep into the subject.

For them, it is also essential to understand the structure of machine learning. As a field of artificial intelligence and computer science, machine learning uses data and algorithms to learn and evolve from experience without being directly programmed.


What Is The Difference Between Data Science And Machine Learning?

Since data science is a broad concept covering many fields, machine learning belongs to data science. Machine learning uses different techniques such as regression and supervised clustering. However, in data science, “data” may or may not come from a machine or a mechanical process.

Data science is more advanced than machine learning. Data in data science is not necessarily the result of a mechanical process. Data can be processed manually and usually has little to do with learning.


On the other hand, machine learning is a field of artificial intelligence, a subfield of computer science and data science.

Data science is the process of extracting valuable information from data. It is a broad discipline that encompasses skills such as statistics, mathematics, programming, computer science and business, as well as techniques and theories such as predictive analytics, data mining and visualization. 

What Is The Purpose Of Data Science?

The main goal of data science is to capture and interpret data effectively and present it in simple, non-technical language for end-users and decision-makers.

The second goal is to produce useful information and transform it into data-driven products.

What Is The Difference Between Data Science, Computer Science And Statistics?

Data science is the application of automated methods (computing) to analyze large amounts of data (statistics) and extract knowledge from it (business).

Data science is the study and analysis of all available structured and unstructured data to gain understanding and knowledge and design actions that lead to better results.

An Example Of Data Science For Problem Solving And Value Creation

It all starts with a business problem to solve. The process of using data science to solve a problem is as follows:

Business Problem –

Customers cancel their banking packages every 2-3 months after signing a contract.

Data Analysis

Data collected and analyzed concluded that customers are leaving their service packages as their debt increases.


Based on the data, the management decided to take a proactive approach toward customers in the same group with similar characteristics.


The company introduced a financial counseling program and developed applications to provide specific financial solutions to customers, which reduced over billing with increased turnover and profits.


Where Is Machine Learning Used In Data Science?

The use of machine learning in data science can start in the data science development process or life-cycle. The different phases of the data science life cycle are:

Business Requirements

in this phase, we try to understand the requirements of the business problem to which we want to apply the system. Suppose we want to develop a recommendation system to increase sales.

Data Collection

We collect the data needed to solve the problem in this phase. We can use user ratings, reviews, purchase history, etc., for different products for the recommendation system.

Data Processing

in this phase, the raw data obtained in the previous stage is transformed into a suitable format for easy use in the following steps.

Data Mining

This phase involves understanding the patterns in the data and trying to draw valid conclusions from them.


Modeling the data is the stage where machine learning algorithms are applied. Therefore, this phase includes the entire machine learning process. The machine learning process provides data ingestion, data cleaning, model building, model training, model testing and model performance improvement.

Application And Optimization

It is the final stage where the model is applied to the actual project and verified its performance.

How Do We Choose Between Data Science And Machine Learning?

No other choice. Data science and machine learning go hand in hand. Machines can’t learn without data, and data science is best implemented through machine learning, as explained above. Future data scientists will need to have a basic understanding of machine learning to model and interpret the vast amount of data that accumulates every day.


Data science, machine learning and artificial intelligence are changing the world. That’s why data science education can be an intelligent choice.

Soon, machines will replace functions performed by humans, and those who know how to work with these technologies will undoubtedly play an important role.

One of the best ways to keep up with these changes and learn how to operate machines is to become a data science expert.

Looking For A Data Science Consultancy?

Data science is an interdisciplinary field that uses computing power and big data to extract knowledge. Machine learning is currently the most popular data processing technology. Machine learning allows computers to learn on their own from the large amounts of data available.
The use of these technologies is widespread but not unlimited. Data science can be compelling, but it only works when people and data are highly specialized. To find out more, take a look at our data science courses and consultation programs.


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