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Updated in [August 13th, 2023]

Skills and Knowledge Acquired:
This course will provide students with the skills and knowledge necessary to be successful in Data Science. Students will become familiar with the Data Scientist's tool kit, which includes libraries and packages, data sets, machine learning models, kernels, and various open source, commercial, big data, and cloud-based tools. Students will gain hands-on experience working with Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. They will understand the features and limitations of each tool, and be able to execute code in Python, R, or Scala. At the end of the course, students will create a final project with a Jupyter Notebook, demonstrating their proficiency in preparing a notebook, writing Markdown, and sharing their work with their peers.


Contribution to Professional Growth:
This course provides a comprehensive introduction to the tools used in Data Science. It covers popular libraries and packages, data sets, machine learning models, kernels, and open source, commercial, big data, and cloud-based tools. Through hands-on experience, learners will develop the skills necessary to work with these tools, such as working with Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. At the end of the course, learners will create a final project with a Jupyter Notebook, demonstrating their proficiency in preparing a notebook, writing Markdown, and sharing their work with peers. This course provides a great opportunity for professional growth, as it equips learners with the skills and knowledge necessary to use the tools of Data Science.


Suitability for Further Education:
This course is suitable for preparing further education in Data Science as it provides a comprehensive overview of the popular tools used by Data Science professionals. It covers a wide range of topics, from libraries and packages to machine learning models and open source commercial big data and cloud-based tools. The course also provides plenty of hands-on experience to develop the necessary skills to work with these tools. Furthermore, the course culminates in a final project with a Jupyter Notebook, which allows students to demonstrate their proficiency in writing Markdown and sharing their work with their peers.