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