Ever since the term data science started gaining currency among the tech savvy, it has been thrown around recklessly. So misused is the term data science that other people have been led to believe that it is similar to machine learning and Artificial intelligence when it truth it is anything but.
Here at Runrex, we get asked the question of what the difference between machine learning and data science is a lot. We therefore thought it wise to highlight the difference between the two terms which are closely interconnected but have different distinct purposes and functionalities. So what is the difference between data science and machine learning?
Difference between Data Science and Machine Learning
Data science is the use of complex and advanced analytical tools and algorithms to process, analyze and draw meaningful insights out of a huge volume of unstructured data. Unstructured data means a set of data generated from different forms and lacks uniformity.
Data Science entails trying to understand the hidden patterns behind the raw data collected from various sources. Data science does exploratory analysis of a set of unstructured data to draw insights and patterns from that set of data.
Data science involves the process of data extraction, data cleansing, data analysis, data visualization, and actionable Insights generation. A data scientist will therefore be tasked with trying to make the most out of the data set in hand and draw as many insights as possible from the data.
The insights drawn from the raw data are telling. They harbor unnoticed patterns which explain a lot about things like customer behavior, predictive analysis, operational shortcomings and supply chain cycles. These are all used by companies to better improve their products and service delivery.
Machine learning on the other hand, is a branch of data science which draws from statistics and algorithms to work on the generated data and extracted from a number of sources. In a layman’s language, machine learning is the ability of system to learn and process data sets without the help of humans.
To achieve autonomous processing of data, it requires complex algorithms and techniques such as supervised clustering and regression. Machine works on the principle of large numbers where a system is fed with a set of similar variables on a frequent basis until it is able to predict the outcome if it is fed with similar variables in the future.
So, now that we have seen the interconnection between the two, what’s the difference between data science and machine learning? What makes them two totally different entities? Here is a deeper look at the differences between machine learning and data science:
Data science produces insights while machine learning produces predictions
This by far is the most distinguishing difference between data science and machine learning. The fundamental goal of machine learning, is to be able to predict the possibilities of certain events happening in the future while data science is tasked with drawing insights and patterns from a set of data.
To be able to predict events of the future, a system is trained by being exposed to a set of variables and outcomes over a period of time until the system can almost predict the outcome if it is exposed to almost similar variables.
Machine learning is therefore used in areas such as self-driving cars where the cars are able to tell when to stop, when to accelerate and even when to overtake based on the data that they collect from their sensors and data from their memory.
Data science on the other hand is a wider field. It combines statistics, software engineering and domain expertise to analyze a set of data and draw meaningful insights from the set of data. This involves the use of special programming tools and analysis software.
One can therefore be a data scientist who is a qualified machine learning expert because machine learning is part and parcel of data science. A machine learning expert however, isn’t necessarily a data scientist because the latter is wider and entails a lot more than just training systems to be able to predict the future and so on.
How machine learning and data science can be used together
Machine learning and data science, can successfully be used together to build more effective systems capable of processing large chunks of data and predicting future possibilities. A good example is the case of self-driving cars:
Machine learning will be used to recognize traffic signs from the sensors and cameras fitted on the car and construct an algorithm to predict the signs which require the car to stop or slow down while data science will give the car the knowledge it needs to know which streets have minimal traffic congestion at certain periods of the day.
Machine learning, data science and artificial intelligence working together can result in very complicated and smart systems which will revolutionize the way we live and work in this world.
Talk to Runrex about machine learning and data science
Want more information and insights about the relationship between data science and machine learning? Give us a call here at Runrex and we will help you understand the workings of the two.
Besides offering affordable data science to clients, Runrex offers the best training and professional consultation services on data science and we are looking forward to helping you today. Give Runrex a call today.