Data Journey

My journey

2022

• 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 terms of professional skills, I learned more about storytelling by making presentations.
  • I do some base conversions in Programming Fundamentals 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, 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

2023

• 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 the Python Course
  • Start the Data Visualization Course
• May:
  • Continue with the Data Visualization Course
  • Studying for university exams
• June:
  • Take 4 exams and pass 2
• July:
  • Finish all the exams and pass 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 at 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 scraping
  • Start learning about Docker
  • Start to visualize some work that I can do for companies.

2024

• January
  • Drop out of college
  • Try to find some project to do by myself
  • Start working on a coworking space project
• February:
  • Continue to work on the coworking project
  • Change all the projects to work in my new Ubuntu
• March:
  • Start to get more knowledge in statistics and probability at 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.
• July
  • Continue to do some SQL exercises
• August
  • Continue to do some SQL exercises
• Septemeber
  • Finish the SQL module
  • Start the MongoDB module
• October
  • Made a Streamlite app for the FilmAffinity project and made a LinkedIn post
  • Finish MongoDB module
  • Made a LinkedIn post about Sentiment Analysis with TextBlob library
  • Start Numpy module
• November
  • Continue with the Numpy module
• December
  • Finish the Numpy Module
  • Made some adjustments and improvements to the coworking space project
  • Start the Pandas module

2025

• January
  • Finish The Pandas module
  • Start with the Machine Learning module
• Fabruary
  • Finish The Machine Learning module
  • Use the Coworking Project as final project and implementing machine learning into this project
  • Start to callaborate with a ONG (Generar-ECO)
• March

Strengths (Internal, Positive)	Weaknesses (Internal, Negative)
Technical Skills (Python, SQL, MongoDB, PowerBI, ML)	Falta de experiencia en empresas
Experiencia con datos reales (Películas, Coworking)	Necesidad de mejorar los proyectos
Mentalidad Autodidacta	Sin títulos en educación formal
Capacidad de Adaptación

Opportunities (External, Positive)	Threats (External, Negative)
Crecimiento del sector de Data Science	Competencia en el mercado
Proyectos personales como Carta de Presentación	Requisitos estrictos en algunas ofertas
Interés en I.A