Data Science Tools IBM Full Course, Data Science Tools and Techniques

The course starts at 0:00 with an introduction, then at 04:04 it covers categories of data science tools. At 11:42 and 19:41 the instructor discusses open‑source tools in two parts, followed by commercial tools at 24:46 and cloud‑based tools at 31:27. The next section, beginning at 39:29, explores the languages of data science, with introductions to Python at 42:00, R at 46:14, SQL at 50:23, and other languages at 54:27. At 1:00:43 the focus shifts to libraries, then to APIs at 1:06:05, datasets at 1:10:45, and sharing enterprise data at 1:16:25. Machine‑learning models are introduced at 1:20:26, followed by the Model Asset Exchange at 1:27:41. The course then moves to Jupyter Notebook at 1:31:48, covering getting started at 1:35:13, kernels at 1:39:13, architecture at 1:41:18, and additional Anaconda and cloud environments at 1:43:22 and 1:48:54. R and RStudio are introduced at 1:53:04, with plotting at 1:56:12, and an overview of Git and GitHub at 1:59:47, including repositories, getting started, and working with branches. Finally, the course covers Watson Studio, starting at 2:22:18, with Jupyter Notebooks in Watson Studio at 2:29:25 and 2:34:50, and linking GitHub to Watson Studio at 2:37:24.