Difference Between Data Science and Machine Learning

What is the Difference Between Data Science and Machine Learning?

Data Science is a combination of algorithms, tools, and machine learning technique which helps you to find common hidden patterns from the given raw data. Whereas Machine learning is a branch of computer science, that deals with system programming to automatically learn and improve with experience.

In this tutorial of difference between Data Science and Machine Learning, Let us first learn:

What is Data Science?

Data Science is the area of study which involves extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes. It helps you to discover hidden patterns from the raw data.

Data Science is an interdisciplinary field that allows you to extract knowledge from structured or unstructured data. This technology enables you to translate a business problem into a research project and then translate it back into a practical solution. The term Data Science has emerged because of the evolution of mathematical statistics, data analysis, and big data.

In this Data Science vs Machine Learning tutorial, you will learn:

• What is Data Science?
• What is Machine Learning?
• Roles and Responsibilities of a Data Scientist
• Role and Responsibilities of Machine Learning Engineers
• Data Science Vs Machine Learning
• Challenges of Data Science Technology
• Challenges of Machine Learning
• Applications of Data Science
• Applications of Machine Learning
• Data Science or Machine Learning – Which is Better?

What is Machine Learning?

Machine Learning is a system that can learn from data through self-improvement and without logic being explicitly coded by the programmer. The breakthrough comes with the idea that a machine can singularly learn from the example (i.e., data) to produce accurate results.

Machine learning combines data with statistical tools to predict an output. This output is then used by corporate to makes actionable insights. Machine learning is closely related to data mining and Bayesian predictive modeling. The Machine receives data as input, uses an algorithm to formulate answers.

Check the following key differences between Machine Learning vs Data Science.

Roles and Responsibilities of a Data Scientist

Here, are an important skill required to become Data Scientist

• Knowledge about unstructured data management
• Hands-on experience in SQL database coding
• Able to understand multiple analytical functions
• Data mining used for Processing, cleansing, and verifying the integrity of data used for analysis
• Obtain data and recognize the strength
• Work with professional DevOps consultants to help customers operationalize models

Role and Responsibilities of Machine Learning Engineers

Here, are an important skill required to become Machine learning Engineers

• Knowledge of data evolution and statistical modelling
• Understanding and application of algorithms
• Natural language processing
• Data architecture design
• Text representation techniques
• In-depth knowledge of programming skills
• Knowledge of probability and statistics
• Design machine learning systems and knowledge of deep learning technology
• Implement appropriate machine learning algorithms and tools

Data Science Vs Machine Learning

Here are the major differences between Data Science vs Machine learning:

Data Science vs Machine Learning

Data science	Machine Learning
Data science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge from many structural and unstructured data.	Machine learning is the scientific study of algorithms and statistical models. This method uses to perform a specific task.
Data science technique helps you to create insights from data dealing with all real-world complexities.	Machine learning method helps you to predict and the outcome for new databases from historical data with the help of mathematical models.
Nearly all of the input data is generated in a human-readable format, which is read or analyzed by humans.	Input data for Machine learning will be transformed, especially for algorithms used.
Data science can work with manual methods as well, though they are not very useful.	Machine learning algorithms hard to implement manually.
Data science is a complete process.	Machine learning is a single step in the entire data science process.
Data science is not a subset of Artificial Intelligence (AI).	Machine learning technology is a subset of Artificial Intelligence (AI).
In Data Science, high RAM and SSD used, which helps you to overcome I/O bottleneck problems.	In Machine Learning, GPUs are used for intensive vector operations.

Challenges of Data Science Technology

Here, are important challenges of Data Science Technology

• The wide variety of information & data is needed for accurate analysis
• Not adequate data science talent pool available
• Management does not provide financial support for a data science team.
• Unavailability of/difficult access to data
• Data Science results not effectively used by business decision-makers
• Explaining data science to others is difficult
• Privacy issues
• Lack of significant domain expert
• If an organization is very small, it can’t have a data science team.

Challenges of Machine Learning

Here, are primary challenges of Machine learning method:

• It lacks data or diversity in the dataset.
• Machine can’t learn if there is no data available. Besides, a dataset with a lack of diversity gives the Machine a hard time.
• A machine needs to have heterogeneity to learn meaningful insight.
• It is unlikely that an algorithm can extract information when there are no or few variations.
• It is recommended to have at least 20 observations per group to help the Machine learn.
• This constraint may lead to poor evaluation and prediction.

Applications of Data Science

Here, are the application of Data Science

Internet Search:

Google search uses data science technology to search a specific result within a fraction of a second

Recommendation Systems:

To create a recommendation system. For example, “suggested friends” on Facebook or suggested videos” on YouTube, everything is done with the help of Data Science.

Image & Speech Recognition:

Speech recognizes systems like Siri, Google Assistant, Alexa runs on the technique of data science. Moreover, Facebook recognizes your friend when you upload a photo with them.

Gaming World:

EA Sports, Sony, Nintendo, are using data science technology. This enhances your gaming experience. Games are now developed using machine learning techniques. It can update itself when you move to higher levels.

Online Price Comparison:

PriceRunner, Junglee, Shopzilla work on the data science mechanism. Here, data is fetched from the relevant websites using APIs.

Applications of Machine Learning

Here, are Application of Machine learning:

Automation:

Machine learning, which works entirely autonomously in any field without the need for any human intervention. For example, robots performing the essential process steps in manufacturing plants.

Finance Industry:

Machine learning is growing in popularity in the finance industry. Banks are mainly using ML to find patterns inside the data but also to prevent fraud.

Government Organization:

The government makes use of ML to manage public safety and utilities. Take the example of China with massive face recognition. The government uses Artificial intelligence to prevent jaywalker.

Healthcare Industry:

Healthcare was one of the first industry to use machine learning with image detection.

Data Science or Machine Learning – Which is Better?

The machine learning method is ideal for analyzing, understanding, and identifying a pattern in the data. You can use this model to train a machine to automate tasks that would be exhaustive or impossible for a human being. Moreover, machine learning can take decisions with minimal human intervention.

On the other hand, data science can help you to detect fraud using advanced machine learning algorithms. It also helps you to prevent any significant monetary losses. It helps you to perform sentiment analysis to gauge customer brand loyalty.