Difference Between Data Science and Machine Learning

Key Difference between Data Science and Machine Learning

• Data Science is a combination of algorithms, tools, and machine learning techniques that help you find common hidden patterns in raw data, Whereas Machine learning is a branch of computer science that deals with system programming to automatically learn and improve with experience.
• Data Science extracts insights from vast amounts of data through the use of various scientific methods, algorithms, and processes. On the other hand, Machine learning is a system that can learn from data through self-improvement and without logic being explicitly coded by the programmer.
• Data science can work with manual methods, though they are not very useful, while Machine learning algorithms are hard to implement manually.
• Data science is not a subset of Artificial Intelligence (AI), while Machine learning technology is a subset of Artificial Intelligence (AI).
• Data science technique helps you to create insights from data dealing with all real-world complexities, while the Machine learning method helps you to predict the outcome for new database values.

Here, I differentiate between data science and machine learning and will methodically review their respective pros and cons.

What is Data Science?

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

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 an 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 corporations to make actionable insights. Machine learning is closely related to data mining and Bayesian predictive modeling. The machine receives data as input and uses an algorithm to formulate answers.

Difference Between Data Science vs Machine Learning

Let me explain the major differences between data science and 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 is used to perform a specific task.
Data science technique helps you create insights from data dealing with all real-world complexities.	Machine learning method helps you predict the outcome of 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 the algorithms used.
Data science can work with manual methods as well, though they are not very useful.	Machine learning algorithms are 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 SSDs are used, which helps you overcome I/O bottleneck problems.	In Machine Learning, GPUs are used for intensive vector operations.

Roles and Responsibilities of a Data Scientist

Having worked in the field, I can tell you that there are some important skills required to become a data scientist.

• Knowledge about unstructured data management
• Hands-on experience in SQL database coding
• Able to understand multiple analytical functions
• Data mining is 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 some important skills I have identified as necessary to become a data scientist.

• 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 have knowledge of deep learning technology
• Implement appropriate machine learning algorithms and tools

Challenges of Data Science Technology

As I have learned, here are some vital skills you need to master to become a data scientist.

• The wide variety of information and data 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 are 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

In my experience, these are the primary challenges of machine learning methods:

• 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

From my experience, these are the applications of Data Science.

• Internet Search: Google search uses data science technology to search for 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 and Speech Recognition: Speech-recognizing systems like Siri, Google Assistant, and Alexa run on the technique of data science. Moreover, Facebook recognizes your friends when you upload a photo with them.
• Gaming World: EA Sports, Sony, and 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, and Shopzilla work on the data science mechanism. Here, data is fetched from the relevant websites using APIs.

Applications of Machine Learning

Based on my knowledge, here are the applications of machine learning:

• Automation: Machine learning, which works entirely autonomously in any field without the need for any human intervention; for example, robots perform 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, which has massive face recognition. The government uses Artificial intelligence to prevent Jaywalker.
• Healthcare Industry: Healthcare was one of the first industries to use machine learning for image detection.

How to Choose Between Data Science and Machine Learning

With this model, I have trained machines to automate tasks that would be exhaustive or impossible for humans. Moreover, machine learning can make decisions with barely any need for human intervention.

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