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Related lectures (15)

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Data Science Visualization with Pandas

Covers data manipulation and exploration using Python with a focus on visualization techniques.

Critical Data Studies: Reproducibility and Renku

Explores the significance of reproducibility in data science and introduces Renku, a platform for managing data-driven projects.

Data Science Essentials: Python, Numpy, Pandas, and Scikit-learn

Covers the essentials of Data Science using Python, Numpy, Pandas, and Scikit-learn, including DNA sequence analysis and classification.

Analytics on Data at Rest and Data in Motion

Explores combining data at rest with data in motion, emphasizing the Lambda architecture complexities and quality assessment of streams and batches.

Introduction to Data Science

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Decision Tree Classification

Covers decision tree classification using KNIME Analytics Platform for data preprocessing and model creation.

Data Wrangling with Hadoop

Covers data wrangling techniques using Hadoop, focusing on row versus column-oriented databases, popular storage formats, and HBase-Hive integration.

Elements of Collaborative Data Science

Introduces collaborative data science tools like Jupyter notebooks, Docker, and Git, emphasizing data versioning and containerization.

Renku: Collaborative Data Science

Introduces Renku, a platform for collaborative data science enabling reproducibility and promoting code and data reusability.

General Introduction to Big Data

Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.