A Fast Road for Engineers to Learn Data Science and AI: An HOV Lane for Engineers and Engineering Students
Here, I explain to engineers how to learn data science and AI in a very fast way. This is an HOV lane only for engineers!!!

Why Only Engineers?
My suggested fast road to learning data analytics is for engineers because most of the engineers (electrical, mechanical, aerospace, petroleum and …) already are familiar with most of the statistical and algebraic basics of the data science and AI. Instead, they can focus on tools and methods. And don’t worry, still, you learn mathematics and logic behind algorithms and techniques using my HOV lane.

How Fast is this HOV lane?
Normally, it takes 14–20 months for someone to start data science and AI from zero to a professional level. My HOV line reduces the time to 9 months. It is based on spending 10 hours per week for learning and exercising.
Let’s Start …
Step 0, Learn Python
Stop searching “What language should I learn for data science?” on the internet. I am telling you. Python. End of the story. If you don’t know Python (like me when I started) start with this Coursera Specialization.
https://www.coursera.org/specializations/python
For data science and AI, you just need to complete the two first courses: Getting Started with Python and Python Data Structures B)
Step 1, Learn Pandas
Now, you need to learn a python library that gives you the ability to load and manipulate data. So, learn Pandas. I am suggesting to watch this video series from Data School. This series has about 20–25 videos that teach you the most important Pandas skills.
https://www.youtube.com/watch?v=yzIMircGU5I
Step 2, Learn Machine Learning
After finishing this video series, I recommend you to take this Coursera Specialization:
Personally, I recommend taking courses 1, 2 and 3 from this specialization.
Step 3, STOP and Only Exercise for a Few Weeks
By reaching this step, you should have been exhausted from learning. STOP learning for a few weeks and start participating in a few competition by Kaggle (a competition platform for machine learning and AI).
Probably the best competition for starting is Titanic Competition (A Machine Learning Classification Problem).
Also, I recommend participating in a Machine Learning Regression competition too.
Also, I think you need to read a few books that give you some business idea about data science applications in the real world. My number one recommendation is this wonderful book by Provost and Fawcett.

Step 4, Go Advanced and Learn Deep Learning
So far, you have learned data analytics and machine learning (ML). Those are interesting topics in artificial intelligence (AI) but not as exciting as deep learning (DL). To learn DL, I highly recommend taking this Deep Learning Specialization by Andrew Ng (one of the best instructors in the field of DL):
https://www.coursera.org/specializations/deep-learning
In my opinion, this Coursera Specialization should be followed by the following wonderful book.

In this book, Chollet shows you how to use Keras (the easiest and the best deep learning platform in my opinion) to build DL networks.
Like machine learning, I recommend you to participate in a few Kaggle competitions related to deep learning (especially computer vision or convolutional DL) and learn from professional Kagglers.
Step 5, Super Advanced Topics …
If you want to go further, the next topic is Reinforcement Learning (RL). Many data scientists or AI professionals don’t go further than DL. But if you want to do super exciting things like Google DeepMind, the next stop is RL. They are not many good resources for this topic, but one of the good references is the following book.

Your HOV Lane Experience?
This is my HOV lane and is based on my experience. Different people might have their own secret HOV lanes. Please tell me about your experience and help me to expand this post.
