Data Analytics Vs Machine Learning

Both Data analytics and machine learning have gained lots of recognition all over the world. Today many organizations rely on both of them to large extent due to the value that these disciplines provide to an organization. But what are the main differences between both disciplines? Is there any relationship between both disciplines? So, to answers these questions today we are going to share with you some of the key differences between both fields and how they are related to each other.

Basic Difference

Machine learning refers to the process of studying and creating algorithms that come with the ability to automatically learn and improve themselves from experience and by gathering user data. The main aim of this branch of AI is to develop programs or algorithms that can access or gather data and use it to improve themselves. Machine learning programs use predictive and statistical analysis for discovering patterns and finding insights to improve themselves. 

Data analytics revolves around cleaning, processing, and transforming data for finding insights. It allows an organization to take raw data and extract information from it. The main purpose of data analytics is to help top-level management in developing business strategies for improving their organization's performance and sales revenue. To learn how to gather insights from raw data feel free to join the Data Analytics Online Course in Qatar.

Use of Data

Machine learning programs use data for learning and training themselves in order to improve themselves. Whereas data analytics uses data to generate insights that can be used for developing strategies and improving the sales revenue of an organization.

Skill Requirements

To develop machine learning algorithms, you must have adequate knowledge about deep learning, natural language processing, computer science fundamentals and programming, neural networks, computer vision, etc. Whereas in order to become proficient in data analytics you must have adequate knowledge about statistical analysis, SQL, R programming, Python, Tableau, Excel, etc.

Ease of Learning

Learning to develop machine learning algorithms is not easy and requires one to have proficient in deep learning, computer science, mathematics, neural networks, etc. Becoming proficient in data analytics is also not very easy. However, in comparison to learning to develop machine learning algorithms becoming proficient in performing data analytics is much easier. To master all the skills that are essential to become a good data analyst feel free to join the Data Analytics Online Training in Kuwait.

Remuneration

On average machine learning, experts can earn approx. ₹ 6,83,000 per annum depending upon years of experience and skillset. Whereas on average a data analyst can earn approx. ₹ 4,28,000 per annum depending upon years of experience and skillset.

Conclusion

As you can see there are many differences between machine learning and data analytics. Machine learning uses data for training algorithms and improving them. While data analytics uses data for discovering insights and patterns. But despite all these differences, both machine learning and data analytics are also dependent on each other to some extent. Thus it’s wrong to say that there is no relation between both disciplines.