Data Journey

My journey

• August(08/08)
  • Learning the basics of SQL and Python on Mimo.
  • Choosing which university will provide me with the best job opportunities so I can start working as soon as possible.
  • Career plan presentation on Prezi and discussing it with family.
  • Solving problems on Brilliant, there’s a data science course.
  • Starting the IBM Data Science course on Coursera- €38.99 per month - week 22/08 to 28/08
  • Starting to better understand the importance of business mindset and storytelling to be a better professional.
  • Starting to learn SQL and resuming Python learning on the Mimo app.
• September:
  • Handling the basics of Jupyter Notebook
  • Doing some exercises on Mimo.
• October:
  • Starting a degree in Data Science and Artificial Intelligence with two subjects: Programming Fundamentals and Professional Skills.
  • In professional skills, I learn a bit more about storytelling by making presentations.
  • In Programming Fundamentals, I do some base conversions and start studying Python again.
  • On the Datacamp platform, I start a basic Python course.
  • On Coursera, I’m doing Data Science Methodology.
• November:
  • I’ve been studying the third block on Coursera, data science methodology.
  • A bit of Python basics on Datacamp.
  • I’ve been able to validate professional skills and programming fundamentals I, so I don’t have any subjects enrolled this semester.
• December:
  • End the DataCamp Python Basics course
  • Start an intermediate Python course
  • Finish DataCamp free access €300 to continue, maybe next semester.
  • Try to find some dream job in a big company to adapt the skills and be prepared for the interview.
  • Continue intermediate Python course
• January:
  • Finish the intermediate Python course on DataCamp.
  • Finish the Python Project or Data Science on Coursera
  • Start Databases and SQL for Data Science with Python on Coursera
• February:
  • Finish Databases and SQL for Data Science with Python on Coursera
  • Start the Semester in the UAX
  • Start Data Analysis with Python on Coursera
• March:
  • Still in the Data Analysis with Python Course
  • Making some exercises with SQL databases for the University, connecting some tables and making conclusions
• April:
  • Finish the Data Analysis with Python Course
  • Start the Data Visualization Course
• May:
  • Continue with Data Visualization Course
  • Studying for university exams
• June:
  • Take 4 exams and passed 2
• July:
  • Finish all the exams and passed 3 of 4
  • Failed Numeric Methods, because the exams were too hard for me
• August
  • Finish Data Visualization Course
  • Start Machine Learning
• September
  • Pause the Coursera course
  • Start Kaggle machine learning course
  • Start an experience course - week of a data scientist (not finished)
• October
  • Start the classes in the university
  • Continuing the intermediate python in Datacamp
  • Trying to understand how a project is made
• November
  • Start a project with DataCamp about marketing with python
  • Stop the DataCamp project, have to pay the fee
  • Did some exercises in SQL, Python and Spark
  • Start doing scrapping with BeautifulSoup
• December
  • Did some projects with web scrapping
  • Start learnig about Docker
  • Start to visualize some works that I can do for companies.
• January
  • Drop out from college
  • Try to find some project to do by myself
  • Start working with a coworking space project
• February:
  • Continue to work on the coworking project
  • Change all the project to work in my new Ubunutu
• March:
  • Start to get more knowledge in statistiscs and probability on work.
  • Getting more familiar with connecting databases with visualization tools.
• April:
  • Continue to work with data visualization in the Cowroking project.
• May:
  • Start work with PowrBi in Windows with the Coworking project.
  • Start Nodd3r course.
• June:
  • Making python exercices in the Nodd3r course.
  • Making SQL exercices in the Nodd3r course.