- 1 Introduction
- 2 Data Science vs Machine Learning
- 2.1 Definition
- 2.2 Difference
- 2.3 Purpose
- 2.4 Common Skills
- 2.5 Specialized Skills
- 2.6 Good to have skills
- 2.7 Practical Applications
- 3 In The End…
Data Science has been termed as sexiest job of 21st century where as Machine Learning, AI is supposed to steal our jobs !! The two things sounds contradicting, yet if you see the job openings for data scientist and machine learning engineer you will find similarities in job profile. Beginners who wants to make career shift are often left confused between the two fields. In this post we will do a thorough comparison of data science vs machine learning on various levels to give you a more better understanding.
So let us get started.
Data Science vs Machine Learning
There is no single universal definition for either of them and people have their own take on definitions of the two. So for sake of consistency let us see the following excerpts from Wikipedia.
Data science is a “concept to unify statistics, data analysis, machine learning and their related methods” in order to “understand and analyze actual phenomena” with data.
Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence.
- As you can see above that data science is not a single discipline but a collection of various fields like data analytics, machine learning, statistics, visualizations that are used to understand data and solve problems using statistical or machine learning models.
- On other hand, machine learning is a field on it’s own which uses various algorithms to create ML models that can perform tasks without explicit programming. In fact, as we saw above, machine learning is one of the skills used in data science. This is the reason we find so much similarities between the two.
Even though data science and machine learning looks similar, there are different purpose and vision behind the emergence of the two fields. Let us see.
- Machine Learning is a field of study which is pushing forward the vision of artificial intelligence where machine become intelligent enough to do activities without being explicitly programmed.
- Data Science is the field of study to solve business, industry and even society problem with the help of data.
Both data scientists and machine learning engineers have many skills in common. Let us see them one by one.
A good knowledge of statistics is required to understand various aspects of data for e.g. data range, data distribution, central tendency, correlation between data etc. This is crucial to understand the data you are playing with and lay foundation for rest of your decision makings.
Whether you love it or hate it, machine learning and data science algorithms are built on top of mathematics . You should have at least some mathematical knowledge of probability, calculus, linear algebra to say the least for better understand of how and why these algorithm works.
Statistical and Machine Learning Modeling
Both machine learning and data science encompasses –
- Regression and classification algorithms of supervised learning for creating predictive models.
- Unsupervised learning algorithms for finding hidden patterns, association, outliers in the data.
Also Read- Supervised vs Unsupervised Learning – No More Confusion !!
You require to have knowledge of at least one programming language so that you can use it to process and transform data and create practical machine learning models. The two most popular choices are Python and R because they have rich collection of ready to use libraries for data science and machine learning.
But you can use any other programming language like C++, Java, Matlab, Julia etc.
This is a very important skill, where you should know how to convert data into graphs to visualize data more clearly. This is not only important to understand the data yourself but also to present your findings to other stakeholders in a more visual format.
Apart from common visual graphs like bar chart, histograms, scattered plots, pie chart it is useful to have knowledge of some advance visualization also.
Though there are many essential skills that are common, but there certain skills which are more specialized for each of data science and machine learning.
- A good hold on writing sql queries is needed to perform data analysis and exploration.
- A strong domain knowledge is required to understand the business data and problem which you are trying to address.
- A good verbal and visual story telling skill is needed to communicate to stakeholders usually business owners.
- Apart from supervises and unsupervised learning, it also has a specialized branch of reinforcement learning which has nothing to do with data science.
- Deep learning is specialized sub branch of machine learning which is seeing exciting developments like GANs, neural style transfers etc.
- Requires more knowledge of computer fundamentals and principles like graph theory, information retrieval, pattern recognition etc to build a strong ML understanding.
Good to have skills
Till now we saw some of the essentials skills that are required for data science and machine learning. But in addition to it, extra skills mentioned below are good to have which can be beneficial in both data science and machine learning area as per current trends.
- Natural Language Processing
- Computer Vision
Data Science is used to solve business and industry problems. Typical use case of data science can be –
- To predict the probability of customers leaving your service so that business can do something to retain them.
- To predict the sales forecast of business so that they can plan accordingly.
- To identify different customer segments based on their behavior and target each segment with different promos.
- To help create recommendation systems.
- Risks analysis so that business can take timely action to mitigate the risk.
To appreciate the vision of machine learning, let us keep aside the common problems solved by machine learning and data science and see some exclusive use cases of ML. This will really give you feeling of science fiction –
- Self driving cars is a dream which is now becoming reality due to companies like Tesla and Uber.
- Voice based personal assistants like Alexa, Google Home are nothing but a box of machine learning device at it’s core.
- AIs that can compete with human and beat them in games. For example IBM’s Watson and Deepmind’s AlphaGo both defeated champion humans in game of Jeopardy and Go respectively.
- Robots that can show capabilities and actions like humans and four legged animals. This is happening right now and Boston Dynamics is the leading company in this space.
- We then have artist AIs which can write it’s own rhyming poem, produce paintings, write movie scripts.
- You can read some more examples at the below link.
Also Read Machine Learning Examples – A motivation for beginners
In The End…
I hope this comparison of data science vs machine learning would now give you a more clear picture regarding the two. In my opinion the line between the two is very much blur because recruiting companies are demanding similar skills for both data scientist and machine learning engineer role. But in spite of the similarities the vision and goal for both of them are different.
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