Data Science as name suggest it is an interdisciplinary field that uses scientific methods, process and algorithms to extract knowledge and information from data in various forms. Where as, Machine Learning, as name suggest it allow system to automatically learn and improve from previous experience without being programmed for it.
Confused? No Problem. Below we have differentiate between data science and machine learning in a easy language.
But, before we go into the differences between these two concepts, it is important that we define the two concepts as a basis for a clear understanding of the two terms.
Difference Between Data Science And Artificial Intelligence (Machine Learning)
TABLE OF CONTENTS
Let us look at the head to head comparison between the two:
- The data that it creates are the ones gotten from real-world complexities. There are several tasks that we are looking at here such as extracting data; understanding requirement; and several others.
- The majority of the input data is generated is generated using human consumable data. It is analyzed by the human being in form of tabular data and in some instances, it can come in the form of images
- There are unstructured data that will come into the process; the data science contains components that will handle this form of data effectively. Lots of components on the move can be synchronized independent jobs
- Data science consists in the main of strong SQL; ETL and data profiling; domain expertise and NoSQL systems Standard visualization
- It comes with the benefits of a horizontally scalable system that can be used to handle massive data. A high Ram and SSDs that are used to surmount I/O bottleneck
- This is the extension of the statistics that is capable of dealing with a huge amount of data through the aid of computer science technologies.
It is used to create insights out of the data. It deals with all real-world complexities. This includes several tasks like extraction of data; understanding the requirement and several others.
The majority of the input data is gotten through human consumable data. It is useful in carrying out the explanation of data that is to be analyzed by humans in tabular data from or via images.
Components for handling unstructured raw data coming. The components that are typically scheduled by an orchestration layer to sync with independent jobs
Preferred Skill Test
- Domain expertise
- ETL and data profiling
- Strong SQL
- NoSQL systems
- Standard reporting/ visualization
- It uses mathematical models to get new data points from historical data to accurately classify such data.
- The input data here is transformed specifically for algorithms that are used. Some of the examples that we are looking at here include world embedding or feature scaling
- ML will deal with the major complexities which have to do with the algorithm as well as the mathematical concept behind it. The ensemble models have more than one ML model and each affects the final output.
- ML on its part consists of Python/R programming; a strong mathematics understanding and Data wrangling with SQL Model specific visualization
- For effective vector operations, GPUs are preferred. In the near future to come, we are going to have more powerful models in the range of TPUs link.
- Now, aside from the head to head; let us look at it from another perspective so as to make us appreciate better the line of differences between the two.
- Machine learning is a major area under the broad spectrum of data science; however, it is not the only area because there are others that are included. It can be seen that though the two are often confused with each other, each of them remained separate entities of their own; the latter is the bigger of the two. We shall be taking a look at five differences that separate the two with the objective of establishing the fact both thoughts looked similar, they are far from being the same. We shall use five bases to compare these two significant entities looking at it from another point of view. You have to read more on the basis of that considering through the following points:
On its part, it will accurately classify/predict the very outcome for any new data point. This is achieved through learning patterns gotten from historical data. To achieve this, mathematical models are used.
The input data for machine learning will be turned in algorithms that will then be used. Some of the examples of what we are looking at here include Word embedding, Feature scaling, adding polynomial features, etc.
The major complexity here is with the algorithms as well as the mathematical concepts that are behind them.
The Ensemble models have more than one ML model; which individually affects the final output
Preferred Skills Test
- Strong Maths understanding
- Python/R programming
- Data wrangling with SQL
- Model-specific visualisation
- Hardware Specification
It can be seen from the above analysis that there are differences between data science and machine learning. Though to the uninformed, the two will look as some of the same; but then the differences are very clear from what we have shared above.