Ever wondered if data science and machine learning the same? Well, Data science, machine learning, artificial intelligence, or deep learning are very confusing buzzwords. It is good to know the differences between them. In this post, I will answer, “Are data science and machine learning the same?”
It was a question that I asked myself in my first semester of my Data Science master, in which multiple classes started throwing those fancy terms.
To give you the short answer, No, Machine Learning and Data Science are not the same. Data science is a study that involves collecting raw data, cleaning raw data, analyzing the clean data, and extracting valuable information from that data. Whereas, Machine learning, on the other hand, is a branch of artificial intelligence and a subfield of data science.
Nevertheless, Data Science and Machine Learning closely relate to each other, but they have not the same meaning. Furthermore, the functionalities and goals of the two fields are different. To understand the difference between the two, let us first briefly introduce these two modern technologies.
What is data science?
Data science revolves around a deep study of data. In summary, it revolves around extracting valuable information and insights from data with the help of various tools, statistical models, and machine learning algorithms. You need the following skills to become a data scientist.
- Sound programming knowledge using languages like Python, R, Scala, etc.
- Ability to transform, combine
- SQL database coding knowledge and experience.
- The understanding of Machine learning algorithms.
- Fair understanding of statistics and math
- Skills to use tools like Hadoop to handle Big Data.
Skill not just limited to the above
What is machine learning?
Machine learning is a modern growing technology that is a part of artificial intelligence and the subfield of data science. This technology revolves around enabling machines to learn from past data and do given tasks automatically. A machine learning specialist will have these skills in general.
- Understanding and implementing ML Algorithms.
- Good programming skills using Python or R.
- Exceptional knowledge of concepts in Math and statistics.
- Data evaluation and data modeling knowledge and understanding.
skills not just limited to the above
What is the difference between data science and machine learning?
The first difference between data science and machine learning is, as we have already discussed, that data science involves extracting useful information from raw data whereas Machine Learning is a sub-field of data science that enables machines to automatically learn from data.
Again a machine learning specialist will probably know how to do data science stuff. However, the focus of a machine learning specialist would be to improve the models. To enhance the models that a data scientist would create and take them to production. So an ML specialist would worry less about how to clean and wrangle the data and more about what test he should run to optimize the performance of the models.
An ML specialist will make a decision based on model performances. Indeed, an ML specialist could decide to run a Random Forest model instead of a Neural Net because the RF is faster to train/test/validate and has the same scoring as the NN. The ML engineer will run more tests for hyperparameter tuning than a data scientist would do. If Data Science was medicine, You can think of an ML as a specialist (like a cardiologist or surgeon) and a Data Scientist as a Generalist.
In contrast, a Data Scientist will still do modeling, hyperparameter tuning, and all the like. However, he will probably have to do the cleaning, collecting, wrangling, the feature engineering, before doing the modeling. And even after the modeling, we have the reporting/presentation that the Data Scientist has to do. So, we have a higher spectrum of completely different tasks that a Data Scientist has to do, machine learning being one of them.
So are data science and machine learning the same? Here’s the summary
| Data Science | Machine Learning |
| Helps to extract insights from the data. | Helps to predict and classify the result for new data points. |
| Data science is a broad term that consists of various steps to establish a model for a given problem and then launch the model. | Machine learning is used during the data modeling phase of data science. |
| Data scientists need to acquire and develop different skills including Machine Learning, Data Querying, Data Storage, Data Wrangling, Data Reporting, Statistics, etc.. | Machine learning specialists need to have a deeper knowledge of ML algorithms, statistics, programming, and other relevant skills. |
| Data science utilizes raw data. | Machine learning relies on refined and structured data. |
| Data scientists spend a lot of time handling the raw data, refining it, and understanding its different patterns. | ML specialists spend a lot of time and energies in solving problems during the implementation of algorithms in the modeling phase. |
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