• I am strengthening my education in Data Science through a 6-month intensive Data Science Bootcamp at CodeOp.  This bootcamp has a mission to cover fundamental concepts of Statistics and Machine Learning and Relational Databases:

  • • Hands-on training using libraries in Python: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn

    • Hands-on working with Git, GitHub, Jupyter Notebook, Visual Studio Code (VSC), Python programming, and working in small groups for the assignments to improve team working skills.

    • Working on one individual and one group project based on real data.

• Besides, I am learning more about Data Science concepts and fundamentals through different sources. DataCamp is a highly recommended source. I do a track called Data Scientist with Python:

  • • Learning Python for data science and gain the career-building skills needed to succeed as a Data Scientist, from Data Manipulation to Machine Learning.

    • Working with real-world datasets to learn the Statistical and Machine Learning techniques needed to perform Hypothesis Testing and build Predictive Models. 

    • An introduction to Supervised (and Unsupervised) Learning with Scikit-learn.

    • Apply all the skills to various projects and interactive exercises.

• I also have very good Data Science and Python courses from Udemy. 

• To update my Math skills, I enrolled and passed a course, Data Science Math Skills by Duke University, on Coursera.

• Here on my personal website, I share my projects to:

1-  Follow my own progress along with the courses.

2- Provide all my projects in one place for job applications.

3- Having a great source of all that I have done and will do in my journey.

4- Create a great information source for people like me, future Data Scientists!