Coursework from the IBM Data Science Professional Certificate on Coursera.
| # | Course | Topics |
|---|---|---|
| 01 | What is Data Science? | Concepts, methodology, careers |
| 02 | Tools for Data Science | Jupyter, RStudio, GitHub |
| 03 | Data Science Methodology | CRISP-DM, problem framing, evaluation |
| 04 | Python for Data Science, AI & Development | Types, data structures, NumPy, APIs |
| 05 | Python Project for Data Science | Stock data extraction, web scraping |
| 06 | Databases and SQL for Data Science | SQL queries, analysis, Db2 |
| 07 | Data Analysis with Python | Wrangling, EDA, model development |
| 08 | Data Visualization with Python | Matplotlib, Seaborn, Folium |
| 09 | Machine Learning with Python | Regression, classification, clustering, recommenders |
| 10 | Applied Data Science Capstone | SpaceX Falcon 9 landing prediction |
Courses 1-2 were conceptual with no notebook labs.
Python, Jupyter, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Folium, BeautifulSoup, SQL, REST APIs