Data science and machine learning are two words that are, quite often, used interchangeably. People usually regard them as buzzwords, sometimes without even knowing what they actually mean.
It is essential to address that though they are not entirely the same concepts, data science and machine learning are related to each other.
To distinguish between them and establish a relationship, you need to have a good idea of what they actually mean and how they work.
What is Data Science?
Data science is a field that studies data. It involves deriving meaningful insights for humongous amounts of data generated across the globe on a daily basis.
In order to do so, it uses a variety of algorithms, scientific methods, and processes. Data science helps you discover various patterns from collected raw data.
According to the 2018 Data Security Confidence Index from Gemalto, 65 percent of businesses reported that they could not analyze all the data that they had stored.
This is where data science comes into the picture in helping companies analyze and store important data. Data science is a widely popular term and has been so-called because it uses data analysis, big data, and mathematical statistics to extract insights from withering structured or unstructured data.
This helps businesses in solving crucial problems by coming up with efficient solutions. Needless to say, it is an interdisciplinary field.
What is meant by Machine Learning?
According to Forbes, between the years 2013 and 2017, the patents for Machine Learning grew at a rate of 34% Compound Annual Growth Rate (CAGR).
Machine learning applies artificial intelligence or AI to provide computers with the ability to learn and improve without having to be programmed explicitly.
It is definitely one of the most interesting technologies one could come across and mainly focuses on the development of system programs in such a way that they can access necessary data and use it to learn and improve themselves with no necessity of human intervention.
The ability to learn by themselves makes computers more similar to humans. It begins with data or observations, including instructions or direct experience.
The latter steps involve looking out for and analyzing patterns to make better decisions in case such a situation arises again.
Relationship between Machine Learning and Data Science
If you are wondering how machine learning and data science are related to each other and not interchangeable, it must be noted that though the distinction between these concepts is not too clear, understanding these terms will give you a clear and deeper understanding of the relationship between machine learning and data science.
It will also help you understand how they are closely connected.
While machine learning uses a variety of algorithms to parse and learn from data in order to make accurate decisions, data science is a broad, interdisciplinary field that interprets huge amounts of data and is used for a number of applications.
It involves the collection of data, management of data, analysis, and interpretation to present it visually.
Data science correlates all other terms such as deep learning, machine learning, and artificial intelligence to present meaningful insights for given data, including exploratory data analytics and predictive analytics, to make accurate predictions when large datasets are given.
This makes it clear that data science uses fields such as machine learning, visualization, and statistics, to name a few.
Data science and machine learning are very crucial for businesses to make accurate decisions on the strategic level with the help of these key skills.
In simpler terms, one must understand that artificial intelligence works on the basis of machine learning. It then collects data that is used as a part of data science.
This will give you a fairly better idea of the relationship between these two fields. Machine learning is a critical part of data science. It effectively uses statistics and algorithms in order to analyze and work on data extracted from multiple sources.
When asked about the difference between Artificial Intelligence, Machine Learning, Deep learning and Data Science, Mr. Omkar Raikar, CTO of Business Toys Pvt Ltd says –
“AI is enabling machines to think by using Machine Learning, which provides us statistical & mathematical tools to explore and analyze data and deep learning techniques, a subset of Machine learning, which is an attempt to mimic human brain using Multi-Neural network architecture. Now this entire study of processes involving one to solve data problems using Machine learning and deep learning is Data Science”.
With this, one can say that data science is a field that merges algorithms derived from machine learning to develop practical solutions while making use of domain expertise, mathematics, and statistics.
Therefore, it stands as a one-stop term for merging machine learning and artificial intelligence into one function.
It is crucial to keep in mind that machine learning and artificial intelligence are a part of data science where the purpose is achieved on a different, advanced level.
Data science is a very broad term that cannot solely focus on algorithms that are considered complex, and this is why machine learning is a necessity for this field.
Machine learning can be used for various projects, such as supervised clustering or regression, minimizing human error.
However, it cannot be denied that when it comes to searching for available patterns and bringing a solid structure to big data, data science is revolutionizing the needs of businesses across the globe.
It is necessary to understand that regardless of whether it is data science or machine learning, they cannot work in isolation.
Both these fields require to be integrated in order to achieve maximum results. A set plan might give you the results you need, but without the integration of these concepts, there is no quick way to learn.
Data science helps you focus on what problems you need to solve, and machine learning helps you in building real-world applications that facilitate you in solving the problems you just recognized.
Both these concepts, when integrated, work towards:
- Solving real-world problems
- Help understand the trade-offs between the usage of multiple concepts
- Understanding how different concepts work together
- Achieving a single goal that is a priority
Without knowing how data science and machine learning work in the real world, it becomes a difficult task to clearly understand the fundamental concept of how they have a relationship with each other.
This means that you must have an overall view of what is the problem you are trying to solve, in order to understand how these things actually work, which would otherwise be limited.
It is advisable that you begin with fundamentals when trying to understand the right concepts, not just academically, but also practically.
Data science and machine learning will help you to know the problem, and more often than not, you will not have a manual on how to solve them.
The relationship between the two concepts of machine learning and data science is a clear one and does not require much justification if you are clear enough with what each of them actually stands for.
Getting an idea about the fundamentals of each of these concepts will help you along the way.
Technology has now become part and parcel of our day to day life and is taking over the world.
With advancements in the fields of data science, deep learning, machine learning, and artificial intelligence, it is safe to say that everything we use today, including the internet, smartphones, or social media sites, your every activity is monitored.
This data is a valuable asset to most companies in enhancing their businesses for the better. If you’re looking to get into or transition to the data science domain, a number of great opportunities await you.