What is the difference between data science, data analysis and machine learning?
Data science, machine learning and data analysis are the three main areas of activity that have gained immense popularity in recent years. For professionals in these areas it is their finest hour. The demand for them in the labor market is high. It is predicted that by 2020 there will be many open vacancies in these areas.
So what do these names mean? What is the difference between these areas of activity? To answer these and other questions, we compared data science, machine learning, and data analysis.
What is data science?
Although this subject has many definitions, we will use the most common, which will be clear to everyone. Data science is a concept that is used to work with big data. This concept includes aspects of data preparation, data cleansing and data analysis.
Under normal circumstances, a person involved in data science collects data from various sources and uses various techniques to extract meaningful information from these data sets. Among the methods frequently used in this regard are predicative analytics, sentiment analysis, and even machine learning.
People involved in data science view this data from a business perspective. They try to make forecasts as accurately as possible, since decisions can be made on their basis.
Skills required to do data science
Do you want to be a professional data scientist? There are several key areas of specialization that you will need to focus on. These are programming, analytics and subject area (highly specialized knowledge).
You will need to acquire the following knowledge and skills:
- Practical experience in programming in Python.
- Good knowledge of SQL database programming.
- The ability to work with unstructured data from various sources, such as social media platforms.
- Knowledge of machine learning.
- Understanding of analytical functions.
Let’s start with the main thing. What is machine learning?
Machine learning can be described as the process of using algorithms to thoroughly examine data and extract meaningful information from it. Machine learning can also use predefined datasets to predict future trends. For years, machine learning software has used statistical and predictive analysis to identify patterns and reveal hidden, but relevant, knowledge in them.
A great example of the implementation of machine learning in life is the Facebook algorithm. This algorithm was created to study your behavior in this social network. The knowledge he then uses to form your tape. Amazon will examine your browser behavior to recommend products that you probably want to buy. The same goes for Netflix. He will recommend movies based on your browsing habits.
What is needed to become an expert in machine learning?
If we consider strictly, machine learning can be considered a branch of both computer science and statistics. If you plan to opt for this career, you should:
- Gain experience with computer systems.
- Master the practical skills of programming.
- Understand probabilities and statistics.
- Examine data modeling.
What is the difference between data science and machine learning?
Data science is a wide field of activity that includes many disciplines. Machine learning falls under the concept of data science, because it employs several techniques commonly used in this field.
But the science of data may or may not be a derivative of machine learning. It includes many disciplines, as opposed to machine learning, which concentrates on one subject.
Data analysis to arrive at some conclusion leads to descriptive statistics and data visualization. He is very associated with statistics. The analyst must be able to work with numbers. In most cases, data analysis is considered as the basic version of data science.
If you are engaged in data analysis, you should be well able to explain various reasons why the data is exactly as it is. You should be able to present the data in such a way that it is understandable to everyone, not just the experts.
What skills are needed to work in data analysis?
You should be well versed in the following areas of knowledge:
- Mathematics and Statistics
- Data processing
As you can see, all three areas of activity are closely related to each other. However, there are differences between them, which we told you in our article. Hopefully now you can better distinguish between data science, machine learning, and data analysis.
So the conclusion of this article is Data science , Machine learning and Data analysis are 3 different fields. Each one special in its own way. Its important to have knowledge of all these technologies but need to specialize in one of the field. All of them are highly paid and have a secure future in coming days. You can have good career with 7 figure salary in your pocket.
These technologies are also in high demand as a freelancer you can work in the comfort of your home and earn a good money.
Let us know about your thoughts regarding this article in comment sections.
This site uses Akismet to reduce spam. Learn how your comment data is processed.
Recent Posts: TechnoBlogy