5 Most Useful Difference between Machine Learning Vs Data Science

Data science and machine learning are often misunderstood as the same things.
So clearing this misconception Machine learning is an area under data science like the other data technologies such as Spark, Hadoop, SQL, python etc.

Data science

Data science is said to be the field in technology that combines the statistics, data analysis, machine learning, and other related processes together to get to a conclusion of completely understanding and analyzing the actual site with data. The data science hereby in order to process the task takes help of many techniques and theoretical explanations drawn in the fields of Information science, computer science, mathematics, statistics etc.

Machine learning

Whereas Machine learning in simple language could be defined as ability of the system to collect the information from the data provided and learn from it to provide an improved experience to the user. This ability of the system depends on the application of artificial intelligence (AI), the system could access the provided data and itself learn from it. In technical words, it could be explained as the study of different statistics and improvised algorithms which are brought in use by the computer system to enhance the quality of work on a specific task. The algorithm of machine learning hereby builds a model of the given sample data with respect to the mathematical aspects of it and the model is said to be the training data, which it uses to make decisions and assumptions about a task for which the system is not precisely programmed to perform.

List of some day to day life application of machine learning:

There are many days to day life application of machine learning such as :

• Virtual personal assistant
• Predictions while communicating
• Social media services
• Email spamming and malware filters
• Search engine result refining
• Online fraud detection
• Product recommendations etc.

5 differences between Data science Vs machine learning:

1. On the basis of scope

In Data science the system hereby works upon the information provided by the user in the real-time and deals with the tasks by analyzing the needs and requirements as well as fetching data from the insights created to work upon.
Whereas in Machine Learning the system acquires knowledge by learning the traces of the pattern followed by the user from historical data using the models build up in the mathematical aspects to come up with accurate predictions of the outcome.

2. On the basis of the Data Input

In the field of data science, the data which is being inputted in the program is worked upon and is been presented in the form which is convenient for humans to study and analyze such as in the pictorial formats or tabular representations. Whereas in the Machine Learning area the input data would be processed such that the output of the data provided is algorithm friendly and could be better used by the system accordingly to provide better and accurate predictions of the outcome.

3. On the basis of complexity of system

Data sciences are the field where complexity mainly occurs with the module which handles the incoming raw data that remains unstructured while flowing in, and also the scheduling of the different components which are to be bought together or synchronized to perform a liberated task.
Whereas in the Machine learning the area of algorithms and the mathematical theories and concepts behind those calculations are most prone of having major complexities.

4. On the basis of the specifications of hardware

In the field of data science, the system must have the desired specifications such as the system must be scalable horizontally in order to manage massive data.

The system must be equipped with high Random access memory (RAM) and Solid State Drives (SSD) so that it could take over the I/O bottleneck problems which are said to be the problem where a system lacks it speed and performs the input/output at a slow pace.
Whereas in Machine language in order to perform the intensive vector operations it is more likely to prefer the use of GPUs (graphics processing unit).

5. On the basis of the preferred skill set

In Data science

• One must be an expert in understanding the domain of data provided.
• Must have expert knowledge of the three database functions namely Extract, transform, and load as well as in the process of examining the data for collection of stats and informative summaries (data profiling).• Having good command on SQL is necessary as it provides you with better insights.

In Machine learning

• A strong understanding of mathematics is required to analyze and build correct and accurate mathematical models and algorithms
• Knowledge of python as a programming language would provide a better framework.
• Having a skill set of data wrangling with SQL would help in mapping the data and forwarding it to be more valuable.

Data science vs Machine learning – The Conclusion

Extraction, transformation, load, and processing of the data provided is what happens in both of the fields, but the output generated in both fields differ in the way here utilization as in data sciences the output generated is human friendly and is automatically detected in forms of pictures of tables and in machine learning the data input is used by the system to study the trails and pattern of the historical inputs and lead to accurate predictions of the outcomes.

Author Bio

Umar Bajwa is a young Tech geek and content coordinator at AppModo loves to write about Mobile Apps, Technology, Life Style and Digital Marketing