All the work done by me as part of IBM's CongnitiveClass "Applied Data Science with Python" Learning Path.

Certificates

• Python 101 for Data Science https://courses.cognitiveclass.ai/certificates/user/688744/course/course-v1:Cognitiveclass+PY0101EN+v2
• Data Analysis with Python https://courses.cognitiveclass.ai/certificates/user/688744/course/course-v1:CognitiveClass+DA0101EN+2017
• Data Visualization with Python https://courses.cognitiveclass.ai/certificates/adce9136dbd14ef09429dea420d6de4b

Python packages used

1 - Scientific Computing Libraries:

• Pandas (data structures & tools)
• Numpy (Arrays & matrices)
• Scipy (integrals, solving differential equations, optimization)

2 - Visualization Libraries:

• Matplotlib (plots & graphs)
• Seaborn (heat plots, time series, violin plots)

3 - Algorithmic Libraries:

• Scikit-lean (Machine learning: regression, classifcation...)
• Statsmodels (data exploration, statistical models & statistical tests)

Course 1 - Python for Data Science

Jupyter notebooks labs Here

Course 2 - Data Analysis with Python

Jupyter notebooks labs Here

Course 3 - Data Visualization with Python

Jupyter notebooks labs Here