What are the difference between data science and machine learning?

In my opinion machine learning is a part of data science.
1, Data science is a science that deals with the collection of data and then studying that data and taking useful sections from the data collected. Then processing of the data to extract useful sections will be carried out by using available tools. Machine learning will be useful in this part of data science. So definitely there will be an overlap between these two but there are certain differences between the two.
2, Machine learning is a part of Artificial intelligence. This technology is growing these days. The machine uses its fast experience to learn on its own.
3. Data science is used to discover insights from the data that is available to it. Machine learning is used to make predictions and classify the results from the data collected.
Data science is a vast subject that is used to create a model solution for the problem that is taken up. Machine learning will be used for modelling purposes.
4, For processing the data, a data scientist requires knowledge of data tools like Python, R, and Scala. Machine learning engineers require some knowledge of computer science fundamentals and programming scales.
5. Data science can process any data. Raw data, unprocessed data as well as processed data can be used in data science. Machine learning require mainly structured data to work.
6, Data Scientists will handle the data, cleansing the data and finding out a pattern. Machine learning engineers manage difficult situations and realise a model.

Data Science is the complex study of large data of a company or an organisation and the specialist has to handle the same effectively for the utilisation of the same. This could be both structured and unstructured one.
Machine learning is the field of technology providing computers the capability to learn without the same being programmed. Inputs of Machine Learning are the aggregate of instructions and data.
The difference between the two are given below-
1) Data Science is a technology regarding the process to extract data from both structured and raw one.
Where as Machine learning is the field of study imparting computers the abilities of learning through the explicit programming.
2) Data Science is a branch dealing with data.
Where as Machine Learning deals with the techniques to learn more about the data.
3) Data Science would need entire analytics universe where as Machine Learning is the combination of both Machine and Data.
4) Data Science applies on algorithms statistics but at the same time, it takes care of data processing. Where as Machine Learning focuses exclusively on algorithms statistics.
5) Data Science Specialist would deal with the varying data and would evolve some rational strategies whereas Machine Learning Engineer would manage the tough situation arising out of his job so as to evolve a suitable programme for his job.

There are many differences between data science and machine learning which are as follows:-
1. It deals with understanding and finding hidden patterns or useful insights from the data, which helps to take good business decisions. Whereas, machine learning is a subfield of data science that enables the machine to learn from past data and experiences automatically.
2. Data science is a field of studying data and deals with how to extract meaning from it, whereas machine learning is a field in which understanding and building are utilised to data to improve performance or inform predictions.
3. Machine learning is a branch of artificial intelligence. It uses an algorithm to extract data and then predicts future trends.
4. Data Science require entire analytics universe where as Machine Learning is the combination of Machine and Data.
5. Data Science is applied on algorithms statistics as well as data processing. Whereas, machine Learning is applied using Algorithms to process the data and get trained for delivering future predictions without human intervention.

Data science is the study of organizing and handling data in various fields. Machine learning is the technique of building models using advanced tools. Machine learning has a slight relationship with data science in that it operates everything using data.

Not only in machine learning, but data science also contributes to almost all industries and business fields. Both data science and machine learning have their own business and career opportunities as well as scopes.

Most commonly, data scientists learn machine learning to perform their predictions and smart actions. Because machine learning needs no human intervention. There are only certain differences between machine learning and data science:

Skills and experience requirements for data science and machine learning:

1. Data science needs stronger skills in programming languages like SAS, R, Python, etc. whereas machine language needs strong expertise in computer science with some skill sets in data structure, architecture, and algorithm.

2. In data science, you should have knowledge of handling structured and unstructured data. In machine learning, you should be proficient with probability and statistics.

3. Some skill sets and knowledge requirements intersect in both data science and machine learning.

One can become a data analyst after completing a bachelor's degree or even without graduation. Before entering as a data analyst, complete the necessary foundational education. After that, gather and train yourself with the required technical skills by working on sample projects with real data.

There are simply 5 basic steps involved in data analysis. They are:

1. Questioning - Make questions yourself about the requirement.
2. Data collection - Finding the answer and collecting the required data.
3. Data cleaning - The data collected needs a cleaning-up process.
4. Data analyzing - Arrange and organize the data
5. Data interpretation - Visualize the interpreted data in a suitable statistical form like a chart or graph.

Machine learning can be difficult to learn for beginners but, anyone can learn it themself. Both machine learning and artificial intelligence require some coding knowledge and software programming to write algorithms and perform.

In fact, machine learning is an integrated combination of computer science and mathematics. Machine learning needs a lot of studies in every project. Hence, it is a little stressful career to choose and take over.