When I wrote my blog post Becoming a Data Scientist — Curriculum via Metromap, little did I know that it will receive a rousing feedback. So a big THANK YOU first of all! Over years a lot of people reached out to me with very kind words and how they use it as a guide in their data scientist journey. Also, many who sought permission to use the Metromap picture in their presentations as well as a few universities that also reached out to use it as part of their syllabus. Writing that post made me realize two things,

• Taking a step back, presenting a complex topic using a big-picture metaphor in a consumable and aesthetic fashion has value + use.
• Scared the pants off me to write my next post.

Now here I’m after almost 4 years to write on a topic that is very close to my heart and yet again see a lot of confusing fluff floating around — Artificial Intelligence (A.I). I’m pretty sure many of you including me would say yes to the following,

1. Everyone in your LinkedIn connections list has AI in their title.
2. Are getting flooded with articles that talk about A.I transforming industries and doomsday articles that go hand in hand.
3. See articles that are not only confusing and misleading but also don’t tend to be comprehensive.
4. Hear from someone is working on “AI for X”, where X can be anywhere from treating cancer to ordering lunch.

Pardon me for overgeneralizing, but I also see of folks who very loosely use the word A.I and have absolutely no clue/idea about what they are talking about. If you try to avoid them and try to seek the answer for “What is AI?”, you are bound to get flooded with conflicting views and very obfuscated terms and definitions. Just because someone is using a deep learning library/package, that doesn’t mean their system is intelligent. There is more to it. So here is my yet another modest attempt to convey via a picture — “Demystifying AI”.

If I have to pick a great starting definition for AI, I would vote for John McCarthy’s. He probably gave the most profound and yet a simple definition of A.I,

“science and engineering of making intelligent machines, especially intelligent computer programs”

AI is a fascinating area and I personally feel it will not do justice to explain it without looking at it from multiple dimensions. I have provided my point of view on AI in the following dimensions,

1. Guardrails for AI (starting with, “just because you can doesn’t mean you should”)
2. Core & essential building blocks
3. Types of data AI systems work on
4. Primary characteristics of an AI system
5. Different types of AI (yawn!)
6. Types of approaches to train / teach AI systems
7. Top Algorithms
8. Most common AI workloads/tasks
9. Common examples of AI systems at work
10. Dev Ops for AI — how are AI systems built?
11. Popular Platform, API’s, Libraries & Frameworks
12. Some of the absolute concepts and topics you need to take time in knowing
13. What’s next for AI?

My goal with this visual is to provide you all with an ability to look at the big picture of AI and yet look at it from various dimensions. I have consciously not gone into great depth and detail, but stuck to a fairly high-level to convey the concepts clearly. I could easily take each of these dimensions and blow it up in multiple levels of detail. I may try to do that in the future or might try to write a book. Feel free to leave your comments and constructive feedback.

The post Demystifying Artificial Intelligence. What is Artificial Intelligence & explaining it from different dimensions. appeared first on Thinking in Systems.

Given how critical visualization is for data science, ironically I was not able to find (except for a few), pragmatic and yet visual representation of what it takes to become a data scientist. So here is my modest attempt at creating a curriculum, a learning plan that one can use in this becoming a data scientist journey. I took inspiration from the metro maps and used it to depict the learning path. I organized the overall plan progressively into the following areas / domains,

1. Fundamentals
2. Statistics
3. Programming
4. Machine Learning
5. Text Mining / Natural Language Processing
6. Data Visualization
7. Big Data
8. Data Ingestion
9. Data Munging
10. Toolbox

Each area  / domain is represented as a “metro line”, with the stations depicting the topics you must learn / master / understand in a progressive fashion. The idea is you pick a line, catch a train and go thru all the stations (topics) till you reach the final destination (or) switch to the next line. I have progressively marked each station (line) 1 thru 10 to indicate the order in which you travel. You can use this as an individual learning plan to identify the areas you most want to develop and the acquire skills. By no means this is the end; but a solid start. Feel free to leave your comments and constructive feedback.

PS: I did not want to impose the use of any commercial tools in this plan. I have based this plan on tools/libraries available as open source for the most part. If you have access to a commercial software such as IBM SPSS or SAS Enterprise Miner, by all means go for it. The plan still holds good.

PS: I originally wanted to create an interactive visualization using D3.js or InfoVis. But wanted to get this out quickly. Maybe I will do an interactive map in the next iteration.

The post Becoming a Data Scientist – Curriculum via Metromap appeared first on Thinking in Systems.