“Data Science” and “Machine Learning” are some of the most searched terms in the technology world. From 1st-year Computer Science students to big Organizations like Netflix, Amazon, etc are running behind these two techniques. And they also got the reason. In the world of data space, the era of Big Data emerged when organizations are dealing with petabytes and exabytes of data. It became very tough for industries for the storage of data until 2010. Now when popular frameworks like Hadoop and others solved the problem of storage, the focus is on processing the data. And here Data Science and Machine Learning play a big role.
Data Science and Machine Learning are closely related to each other but have different functionalities and different goals. At a glance, Data Science is a field to study the approaches to find insights from the raw data. Whereas, Machine Learning is a technique used by the group of data scientists to enable the machines to learn automatically from the past data. To understand the difference in-depth, let’s first have a brief introduction to these two technologies.
Machine Learning used in Data Science
Data Acquisition: In this step, the data is acquired to solve the given problem. For the recommendation system, we can get the ratings provided by the user for different products, comments, purchase history, etc.
Business Requirements: In this step, we try to understand the requirement for the business problem for which we want to use it. Suppose we want to create a recommendation system, and the business requirement is to increase sales.
Data Processing: In this step, the raw data acquired from the previous step is transformed into a suitable format, so that it can be easily used by the further steps.
Modeling: The data modeling is a step where machine learning algorithms are used. So, this step includes the whole machine learning process. The machine learning process involves importing the data, data cleaning, building a model, training the model, testing the model, and improving the model’s efficiency.
Data Exploration: It is a step where we understand the patterns of the data, and try to find out the useful insights from the data.
Deployment & Optimization: This is the last step where the model is deployed on an actual project, and the performance of the model is checked.
Data Science |
Machine Learning |
| It is used for discovering insights from the data. | It is used for making predictions and classifying the result for new data points. |
| It deals with understanding and finding hidden patterns or useful insights from the data, which helps to take smarter business decisions. | It is a subfield of data science that enables the machine to learn from the past data and experiences automatically. |
| It is a broad term that includes various steps to create a model for a given problem and deploy the model. | It is used in the data modelling step of the data science as a complete process. |
| A data scientist needs to have skills to use big data tools like Hadoop, Hive and Pig, statistics, programming in Python, R, or Scala. | Machine Learning Engineer needs to have skills such as computer science fundamentals, programming skills in Python or R, statistics and probability concepts, etc. |
| It can work with raw, structured, and unstructured data. | It mostly requires structured data to work on. |
| Data scientists spent lots of time in handling the data, cleansing the data, and understanding its patterns. | ML engineers spend a lot of time for managing the complexities that occur during the implementation of algorithms and mathematical concepts behind that. |