Data Scientist Career Track with Python

The 30th of December I finished the “Data Scientist Career Track with Python” on

It was a great journey and it lasted 226 h (tracked with the Pomodoro Technique).

I was too lazy to remove the word "Working"

The Career Track is composed of 20 courses, I also enrolled other two, the first on SQL, the second on PostgreSQL

This career track cost 180$, actually with 180$ I have one year access to all the courses, so I can enroll new courses (and after February I will do it) until August 2019.

It was very interesting and I discovered a new discipline that really engaged me.

What I really liked about the Career Track it was the courses modularity, moreover, every 5 minutes of theory, explained through a video, followed at least three exercises.

A preliminary knowledge about Python was not necessary, although I had some basic about C thanks to Arduino.

Will I recommend it?

Yes, if you are interested about the subject, but after the Career Track, you must start some meaty project to put into practice what you learned and avoid the risk to forget what you have learned.

If there is a negative side to this “Career Track”, it is the time needed. On the website, I read that all the Career Track last 67h, I don’t know how they calculated this time.
On average 3,35 hours for a course but based on my personal experience I think the evaluation is not true.

Efforts and time needed to master the subjects explained are higher.
Now It’s time to put into practice all the things I have learned!

On January I have to study for the National Engineer Exam, work on some projects and create a personal portfolio on Git Hub .

I have also promised to Diego that I will write some posts on his blog where I will explain the statistic concept of “Test hypothesis” and errors related to these tests.

Also because as you can see from the first and the following graph, all the time dedicated during these months on Python was for the study on Datacamp (226h of 290h tot.)

Happy new year! 🙂

Python Pomodoro Technique Logs Analyzer

I am a big fan of the Pomodoro Technique.
Developed by an Italian, I wrote about it on my Italian blog (wow it was in the 2013 time is running fast).
The technique splits the time in 25 minutes slots.
After every 25 minutes, you take 5 minutes break.
Is a great way to avoid distractions, be focused and manage your energy.
Obviously if you don’t know why are you doing something, what really “motivate” you, It will not work.
I use a smartphone app that not only alerts me when the 25 minutes are passed, but I can also label the kind of activity done during the time slots.
This way is easier to analyze how I manage my work time and how I waste it.
Awareness on how time flow is essential.
By the time I gathered a lot of logs and became quite hard to efficiently analyze all in a quick way, so I developed a short script on python trying to solve the problem.
The script in thi, example is used for analyze how much time I have dedicated to python since I started to study it
Basically the code read the Clockwork Pomodoro Activity Log *.CSV, clean the data and extract all the rows that contains the word “Python” from the Activity Column, then it makes the sum of it and plot the data.
The future goal is to apply the Markov’s inequality or the central limit theorem to estimate what can I reach in the following months, based on passed results.

First Post, Where It All Started

The first time I heard about Machine Learning was during my Master Degree in Civil Engineer when I enrolled the course “Theory of road infrastructure”.
Here the prof. De Blasiis talked about Neural Networks applied to road accident analysis and the subject was completely mind-blowing.

After that, I started reading about Machine Learning, but only in 2016 I started learning how to code.

I started studying MATLAB with the Machine Learning course on Coursera, then after my master degree in Civil Engineer, I realized that I needed to learn Python and so I decided to buy a year subscription on

This blog will be a travel journey about this exciting experience in the world of Data Science.

I will describe my script/project but also my idea about Data and events where I will attend.

I hope to update the blog regularly, although it is not going to be easy.

Ps This blog  Is also a gym to improve my bad  English.