What is the Difference between Data Science and Machine Learning?

Data science

Data Science has evolved to become a powerful tool that Data Scientists use for the betterment of organisations. Data Science has become an inevitable tool used by most businesses in today’s world, irrespective of their size. The procedure involves several steps like detecting unique data information, analysing them, and understanding how they can be used to improve companies. On the other hand, machine learning is a procedure that involves data analysis and works towards operating with minimal to no human intervention.

What is Data Science?

Data Science can be considered to be the field of study that focuses on gathering and transforming raw, unique data information into critical business matters. The data extracted from big data is later utilised by the Data Scientists who use it to make the organisational features better for the future. It is mainly used as a method that helps organisations improve their76 revenue, business opportunities and meanwhile reduce costs on an overall picture.

Data Scientists mainly focus on brainstorming and analysing new data patterns. With the help of data interpretation, predictive analysis, visualisation, and data manipulation, they invent new strategies that work for their organisation. Experts in this field have to go through rigorous Data Science and analytic courses to be able to handle big data and information. Whether a company is big or a small one, more than 76% of businesses rely on this method for their betterment. Huge companies like Netflix, Amazon, airlines, internet search prefer the Data Science method to ensure better performance. 

What is Machine Learning?

Machine learning is the field of study that helps computers to learn without being programmed explicitly. Its sole purpose is to analyse data that automates analytical model building. It focuses on identifying patterns and making decisions regarding data analysis without any human intervention.

As a separate field of study, machine learning was born with the idea that computers can learn without being explicitly programmed. Researchers and scientists experimented with its potential in the field of data interpretation. It is a method that helps computers to recognise and learn new, raw data patterns with the help of past reliable and repeatable data results. Machine learning is not a new technique. It has been there for quite a few decades now. However, the idea and application of how computers can now apply big mathematical calculations to big data so quickly automatically is definitely a recent development in the field.

What are the differences between these two?

Since both these methods are primarily based on data analysis, many people seem to often make the mistake of using them as two synonymous data analysis processes. If you are wondering what the points are that creates the distinction between these two, keep reading!

  • Data Science can be regarded to be the approach that has scientific connotations to it. It is used to extract meaning and war insights from big sized data. Experts in this field generally make predictions in order to make decisions. Machine learning, on the contrary, is the process that researchers use to help computers learn from data. 
  • Data Science is the bigger field consisting of many different tools and methods, including machine learning. Machine learning, therefore, is a sub-tool of Data Science. 
  • Scientists who have a specialisation in statistics are preferred as Data Scientists. In comparison, computer science experts are sufficient to make great experts in machine learning.
  • Data visualisation is often made as one of the most important steps of Data Science. In contrast, data evaluation and modelling are the major factors considered in machine learning.
  • Data Science is the broader tool in the data analysis procedure. It requires specialisation in certain programming languages like python, R to run smoothly. Machine learning, on the other hand, can be operated with simple language processing by experts. It does not require any such specialisation.
  • Data Science experts are required to have a deep applied knowledge of big data tools like Apache Hive, Pig, and Hadoop. Machine learning experts need to have special knowledge in architectural data design, text representation techniques, and statistical modelling.
  • Data Science is a powerful procedure that requires highly skilled scientists who know extracting and analysing quality data to run smoothly. Machine learning is a less complicated process that does not require any such specialisations.
  • Those who practise Data Science on a regular basis need to have practical knowledge of data utilisation. A mixture of computer science, mathematical expertise, and market management is preferred in this field. Machine learning, being the subdivision of this huge field, requires expertise in computer science.
  • Data Science, as the broader practice, has not evolved from the machine learning process. Machine learning, on the other hand, as a subdivision of the broader field, uses techniques like supervised clustering and regression.
  • Non-technical skills like communication skills, intuition about data information, and impeccable business skills are preferred in Data Science. Although these skills are more or less appreciated in Machine learning, they are not mandatory for an expert to have.

Limitations of Data Science and Machine Learning:

Machine Learning and Data Science are two big methods that bring about huge changes in the technical world. These two have evolved to become two powerful approaches that actually make a difference in the real-life potential of organisations. However, their power is limited, and they are unable to do some major things in their field.

  1. Data Privacy is one of the biggest concerns all big organisations have these days. With the growing rate of cybercriminals and hackers, data privacy has become one of the most crucial aspects to look after. However, while extracting raw data from big sized data information, the chance of it being hacked increases. The possibility of this extracted data being used against the organisation, its community and people, is always present. This extracted data can also be used further by people with ill intentions inside the organisation. 
  2. The tools and methods used in this field are extremely complex and costly. The cost of it can come across as overwhelming and inessential to comparatively smaller companies. All experts in this field need to have a particular amount of training and learning to conduct these processes successfully. Data Science course fees, the training, as well as the entire procedure become extremely costly, which is why many talented potential experts fail to pursue their respective careers in this field.
  3. Data Science is a process that depends on data interpretation and data visualisation. Experts in this field usually make predictions in order to facilitate their decision-making process. This prediction often does not seem accurate, and this arbitrary thought affects the entire procedure.

Conclusion: 

Data Science, despite several limitations, has immense potential and has opened up several job positions in the market like data analyst, research analyst, business analyst, data engineer, etc. It has been a revolutionary development in the modern world that has immense potential and requires immense expertise in return.  If you are an aspiring Data Scientist or someone who is willing to get into the field of machine learning, go through the differences and perks of both fields in order to be able to evaluate the pros and cons of the space before becoming a part of it. So remember to carefully choose the right Data Science course that best suits your career prospects before jumping into anything.

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