Data Science vs Machine Learning is one of the most debated topics that often confuse tech enthusiasts. We hear this question being asked a lot, “What is the difference between data science and machine learning? A lot of the skills that are used for machine learning overlap a with data science, with a few differences but each has got its individual applications and are not the same. Take a look at this Data Science vs Machine Learning Google Trends Graph for the last 5 years
The confusion between data science and machine learning originates from the fact that the two trends go hand in hand and have a noteworthy hold in the tech and business world. Both the buzzwords data science and machine learning have garnered a similar level of interest though machine learning was a little higher recently. Data Science vs Machine Learning, the line is often blurry between the two concepts that newbies often mix up elements of data science and machine learning and some people even use these buzzwords interchangeably. Often when you hear data scientists shoot a dozen machine learning algorithms when discussing their data science projects or go into the details of TensorFlow people get confused that both data science and machine learning are the same. Data Science and Machine Learning are often used in conjunction with similar use-cases that beginners in the industry are forced to think they are the same. Neither of the hypothesis is true. So, let’s clear up this confusion once and for all.
Data Science vs Machine Learning – What is the difference?
Before we go on to understand the differences between data science and machine learning , it is very important to understand what is data science and what is machine learning.
What is Data Science?
Imagine you are going to join a new data science company, with new colleagues, and you would be leaving your old colleagues from the previous organization. Making new friends from all those new colleagues can be quite a daunting task. How would you know which colleagues, from the new organization, are more likely to become your friends? A clever professional (yes, that’s you), would make a note of all the characteristics you know about your friends in the current organization. For example:
|Name||Favorite Hobby||Likes Watching Reality TV Shows?||Favorite Programming Language|
|Jon Snow||Photography||Too Much||R|
|Arya Stark||Hiking||A lot||Python|
|Sansa Stark||Hiking||Yes, very much||Python|
|Bran Stark||Hiking||A little bit||Python|
From this data, you can see that all your friends like watching reality TV shows even if a little. All of them, except one, have Hiking as their favorite hobby, and most of them love programming in Python. So now you can use this information to find out which of the colleagues in your new organization are most likely to become your friends. If their favorite hobby is Hiking, and they like watching reality TV shows, they are most likely to become your friends. Still, any colleague who loves watching reality TV shows is most likely to become your friend, even if their favorite hobby is dancing or singing. The favorite programming language does not matter much, however, someone whose favorite hobby is Hiking has Python as their favorite programming language, and is a reality show freak is definitely going to make a very good friend of yours.
Now, what you have done is Data Science right there. It’s collecting data, organizing it in a way so that it can be easily analyzed for making decisions. We performed various data science tasks when trying to get new friends for you :
- Define the Business Problem – We identified the problem to be finding new friends from hundreds or thousands of colleagues would be exhausting.
- Collect the Data: In our example, we already had the data so we only had to make a note of it by jotting it down in a tabular format. If we didn’t know the favorite hobby for all our friends, we would have to go to each of them and ask them. This is a part of the data collection process in data science.
- Data Cleansing and Processing: After collecting the data, we need to see if there is anything that is not needed so it can be deleted. In our example, the names of the friends are not important as they do not exhibit any characteristics and are of no use in deciding who can be a good friend. So, the names can be removed.
- Data Analysis and Exploration – Having performed data cleansing, the next step in the data science process is to look at the data pattern and identify trends if any. For instance, in our example, all friends liked watching reality TV shows. This was easy because we were looking at the data of only 5 friends. Imagine, if you had 5 million data records to explore, it would not be possible to identify any new patterns or trends in the data. This is where machine learning algorithms come to the rescue.
- Data Visualization: The ultimate step of the data science process is to visualize the results through easy to understand digestible images like tables, pie charts, histograms, graphs, etc to inform the decision making of management at various levels.
I hope this example has shed some bright light on what data science is all about. It can be considered as a 21st-century extension of mathematics that people have been doing for ages. Whether it’s a small 5 columnar table or 5 million records, the goal of data science is to produce insights and predict future trends. Data Science or data-driven science is an umbrella term that combines multiple disciplines (multi-disciplinary in nature) such as data analytics, machine learning, statistics, software engineering, domain expertise, data mining, and other related disciplines. Data Science defines what business questions should be asked rather than finding specific answers to them.
What is Machine Learning?
Machine learning needs no introduction, as it is something which we all are quite aware of. We come across hundreds of machine learning applications in a day. Do you use Alexa? iPhone’s Siri? Netflix? Amazon Echo? Google ?. If you answered no to Google, we know you’re lying to us. Just imagine this, you have a machine learning interview scheduled tomorrow and you want everything to go as per the schedule to avoid any last-minute hustle-bustle. So, you tell –
- Hey Siri, Set an alarm for 6 AM tomorrow
- Book an Ola for 8 AM to the interview venue.
- Estimate the Ola Ride fare
- Ask Maps to estimate the time to reach the interview venue and expected congestion.
- Ask Alexa to Switch Off the Light so you can have a sound sleep before the interview.
You just have to talk to the digital assistants and the machine learning algorithms behind the scenes will get to work and get the tasks done for you. Machine learning is a subset of Artificial Intelligence with roots as far back as the 1700s with Thomas Bayes, the statistician behind the famous Naïve Bayes machine learning algorithm and the Bayes Theorem. Machine learning lets computers learn on their own from previous experiences and improve over time without any explicit programming or human intervention. The best way you can relate to this definition is through Facebook’s machine learning algorithms which learn about you, your interests, and your likes, and dislikes. The machine learning algorithms then show you content or advertorials that you are most likely going to engage with.
Machine learning is a field of prediction that involves perfecting a decision model under predictive analytics by matching the probability of occurrence of an event to what actually happened at a predicted time. A one-liner definition for machine learning goes something like this –
“Given an instance X with specific features, predict Y about it.”
Predictions can relate to anything about the future (“predict whether a customer will repay the loans” or qualities (“predict whether a given image has a dog in it”).
Key Differences in Data Science and ML
- Data Science vs Machine Learning: Machine Learning is Scalable Compared to Data Science
Machine learning is a scalable activity meaning if a machine learning expert has implemented an algorithm, anybody can make use of it. Many companies cannot afford to recruit the topmost talent in the industry, however, they can make use of their work available in cloud APIs, open-source libraries, and research papers. On the contrary, data science is a non-scalable activity or rather is less scalable because it requires an in-depth understanding of an organization’s business, requirements, and assets. Even if a data scientist’s approach to a specific business problem is available to the public, some features and characteristics of the problem will differ on a situational basis based on the business. The approach cannot be replicated as is.
An organization can easily scale its machine learning capabilities by hiring more machine learning experts while that might not be easy to do in the case of data scientists. Hiring a new data scientist on the team also requires a period of training and learning where the employee needs to know the ins and outs of the business and its processes.
- Machine Learning is a single step in the Data Science Process
Data Science is a complete process that covers a full spectrum of data processing including data extraction and transformation, data cleansing, data exploration, data analysis, machine learning, and data visualization. Data Science is not ideally the subset of machine learning but uses machine learning to make predictions for solving real-world business problems. This makes machine learning an integral component of data science that uses other steps of data science to build the best-suited machine learning algorithm for predictive analysis. This makes a data scientist job role far more multi-disciplinary than that of a machine learning engineer. Using machine learning is just one aspect of a data scientist’s job.
|Data Science||Machine Learning|
|It is not a subset of AI but is based on strict analytical evidence.Data Science works by collecting, cleaning, and processing data to glean meaningful business insights. It deals with big data (structured, semi-structured, and unstructured data).Data science defines new business problems that can be solved using machine learning.The multi-disciplinary nature of data science makes its scope vast.Data science can exist without machine learning but it is not efficient.Popular data science tools include Hadoop, Spark, Python, SAS, R, BigML, Tableau, MATLAB.||A subset of Artificial Intelligence.Machine learning uses data to self-learn without having to be programmed explicitly.It deals with statistical models to improve with experience.It comes into picture only in the data modeling stage of the data science process.The problem is clear and experts use various machine learning tools and techniques to find the best possible solution for the business problem.Machine learning cannot exist without data science because it requires clean data ( which is input from the data science process)to train and test a model.Popular machine learning tools include SciKit Learn, Azure Machine Learning Studio, Amazon Lex, and Watson|
Data Science and Machine Learning are close companions and have a lot to do with each other, but are not the same. Each discipline stands discrete in its own applications and functionalities. IBM predicts that the demand for data scientists is expected to increase by 28% by the end of 2020. Data Science and Machine Learning are rewarding career choices especially when professionals are getting pay cuts and laid off. Companies are on the verge of welcoming skilled data science and machine learning talent with good paychecks and amazing work-life balance. A perfect combination of real-world experience with the right skillset can help you land a top gig either as a machine learning engineer or a data scientist. Take the first step to prepare for these data science job roles by enrolling for an accredited machine learning course or a data science course. At Springboard, we offer mentor-led project-based data science and machine learning programs to help you take your career a step ahead and master the data science and machine learning skills top tech companies want.