Data structure and Algorihms for Data Acquisition and Curation

Description

• Introduction to Data Acquisition and Curation:
o Overview of data acquisition and curation processes
o Importance of data integrity and quality control
• Data Structures for Data Acquisition:
o Arrays, linked lists, and dynamic arrays
o Stacks, queues, and their applications
o Trees and graphs for hierarchical and network data representation
• Algorithms for Data Acquisition:
o Searching and sorting algorithms for efficient data retrieval
o Hashing techniques for fast data access
o Graph algorithms for analyzing networked data
• Data Curation and Storage Techniques:
o Database management systems and their role in data curation
o Relational databases and SQL for data manipulation
o NoSQL databases and their applications
• Data Cleaning and Preprocessing:
o Handling missing data and outliers
o Data normalization and transformation
o Exploratory data analysis techniques
• Data Integration and Transformation:
o Merging and combining datasets
o Data aggregation and summarization
o Feature engineering and dimensionality reduction
• Data Acquisition and Curation Tools:
o Introduction to data acquisition frameworks and APIs
o Data cleaning and preprocessing libraries

Compétences visées

The course "Data Structures and Algorithms for Data Acquisition and Curation" is designed to
provide students with a comprehensive understanding of the fundamental concepts,
techniques, and tools required for effective data acquisition and curation. This course focuses
on the key algorithms and data structures used in the context of acquiring and curating
diverse datasets, ensuring data integrity, optimizing data storage and retrieval, and facilitating
efficient data processing.

Bibliographie

Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to
Algorithms. MIT Press.
• Goodrich, M. T., Tamassia, R., & Goldwasser, M. H. (2014). Data Structures and
Algorithms in Python. Wiley.
• Ramakrishnan, R., & Gehrke, J. (2003). Database Management Systems. McGraw-Hill.
• Loshin, D. (2019). Data Preparation for Data Mining. Morgan Kaufmann.
• McKinney, W. (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy,
and IPython. O'Reilly Media.
• Healy, K. (2018). Data Visualization: A Practical Introduction. Princeton University Press.

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