AI Vs ML Vs Deep Learning Vs Data Science

So, what really is the difference amongst AI, ML, DL & Data Science?

This fundamentally is a marketing question. A better way to circumvent this question, is to define a spectrum for data science approaches.

At one end you will have Statistics, the other end will be occupied by software engineering. See below.

Let me offer an explanation to the above diagram. When you are to the left, you will see a lot of core data approaches, working with spreadsheets, data cleaning & wrangling, plotting histograms & fitting distributions, etc. This is the area where you will need to use your Maths/Stat skills like Hypothesis testing, measures of dispersion, etc. This I call 'Statistical Data Science'.
                                                                                                         As you look towards right, you will find a lot of new age software driven data hungry methods, which are validation metric oriented. 

So, it's like the same kind of solution with varying degree of software-maths-statistics usage. Towards the left, the proportion of Maths/Statistics is more, and the right is tilted towards software engineering.

Let's look at the same thing with a Venn diagram:

Also, there are things like Robotics & AGI which get mixed with Data Science. There is a need to understand that Data Science is largely a statistical discipline and Robotics has more Electronics/Mechanical Engineering.

Also, I have talked about these things in a youtube video. Check it out below: