Let’s walk into the Data Science world and see what it means and how does it connect with other terms that we often hear in the context – Artificial Intelligence & Machine Learning.
What is Data Science?
In a context of a particular domain, data science is gaining insights into data through statistics, computation and visualization. Below diagram represents this multi-focal field well:
For a real world problem, based on statistics, we make certain assumptions. Based on assumptions, we make a learning model using mathematics. With this model, we make software that can help validate and solve the problem. This leads to solving complex problems fast and more accurately.
How to use Data Science?
Clearly, it can be defined as a process. When followed, it would help move towards destination with proper next steps:
There are multiple iterative stages that solves specific queries. Based on answers to the queries, we might have to circle back and reassess the steps of the entire process.
Various stages involved?
Once we have a defined process, it’s easier to break it down into different functional groups. It would help us interpret how to visualize, connect them and know who can help us at each of those stage:
There is a strong correlation of business intelligence with data science here. Current advancements in algorithms and tools has helped us improve accuracy for each of the stages above.
Where does AI or ML fits in?
Data Science, Artificial Intelligence & Machine Learning are different but often used interchangeably. There are overlaps and a part covers all of them together:
On a high level, part of the data science provides processed data. AI or ML or DL helps to process the data to get the needed output.
Artificial Intelligence (AI)
It is a program that enables machine to mimic human behavior. As such, goal here is to understand human intelligence, learn to imitate and act accordingly. I came across a good AI exhibit by BCG:
Self driving cars and route change suggestion are few common AI solutions
Machine Learning (ML)
These are AI techniques that enables machines to learn from examples, without being explicitly programmed to do so. It incorporates mathematics and statistics to learn from itself and improve with experience.
Recommendation engines & Spam email classification are few common ML solutions
I will cover Machine Learning in much more detail in later posts.
Deep Learning (DL)
These are subset of ML that makes the computation of multi-layer neural network feasible. It does not require feature selection/engineering. They classify information similar to human brains. They often continue to improve as the size of your data increases.
Face detection and number recognition are few common DL solutions
Moving On …
Overall, Data Science is more about extracting insights to build a robust strategy for business solution and is different from AI or ML.
To read more around differences between the Data Science and other terms, Vincent Granville has shared in detail here.
With above, we have entered into the Data Science world. Going forward, I will concentrate more on Machine Learning aspect for now.
Keep exploring!