Industry Internship Experience: Trading Intelligence and Data Analysis

Related lectures (32)

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Covers the fundamentals of database systems, including data modeling, information processing, and the challenges of managing large volumes of data.

Covers data wrangling techniques using Apache Hive for efficient big data management.

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

Introduces the fundamentals of handling data, emphasizing the importance of Pandas and data modeling for effective analysis.

Covers best practices and guidelines for big data, including data lakes, architecture, challenges, and technologies like Hadoop and Hive.

Introduces data modeling concepts, SQL usage, and Pandas library applications for efficient data processing.

Covers Spark Data Frames, distributed collections of data organized into named columns, and the benefits of using them over RDDs.

Addresses challenges in data cleaning for analysis, proposing optimizations to reduce processing time.

Explores query-driven cleaning techniques for denial constraints in databases, focusing on relaxation strategies and cleaning efficiency.

Covers the analysis of KMnO4 concentrations using data from a CSV file.