Course duration

• 3 days

Course scope

To emphasize the role of data analysis and manipulation skills as foundational knowledge for deeper exploration into advanced fields like machine learning, artificial intelligence, and other areas within deep technology.

By framing the curriculum in this context, learners understand that mastering data analysis and manipulation skills is not only essential for general data science but also serves as a crucial stepping stone towards more advanced pursuits in deep technology.

Course description

Module 1: Introduction to Jupyter Notebook and Python Basics

1. Explanation of how Jupyter Notebook and Python are foundational tools for data analysis and manipulation in deep tech fields.
2. Introduction to Python syntax and data types with examples relevant to data analysis in deep tech applications.

Module 2: Data Manipulation with NumPy

1. Emphasis on NumPy's role in handling large datasets and numerical computations, which are common in deep tech applications such as machine learning and scientific computing.

Module 3: Data Analysis with Pandas

1. Highlighting Pandas' significance in preparing and cleaning datasets for deep tech projects, including tasks like feature engineering and data preprocessing.

Module 4: Data Visualization with Matplotlib and Seaborn

1. Illustrating the importance of data visualization in exploring and understanding complex datasets, a crucial step in deep tech projects for gaining insights and making informed decisions.

Module 5: Exploratory Data Analysis (EDA)

1. Connecting EDA techniques to the initial stages of deep tech projects, where understanding the data's characteristics and patterns is essential for formulating hypotheses and designing experiments.

Module 6: Case Studies and Projects

1. Integrating deep tech case studies and projects that demonstrate how data analysis and manipulation skills are applied in practical scenarios within fields like machine learning, natural language processing, computer vision, etc.
2. Examples could include data preprocessing for machine learning models, analyzing experimental results, or exploring datasets for training deep neural networks.

Module 7: Conclusion and Next Steps

1. Discussing how the skills acquired in this training serve as a solid foundation for further exploration into deep tech fields.
2. Providing resources and guidance for learners interested in transitioning to advanced topics such as deep learning, reinforcement learning, or other areas within deep technology.

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