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This article was published as a part of the Data Science Blogathon. Introduction With a huge increment in data velocity, value, and veracity, the volume of data is growing exponentially with time. This outgrows the storage limit and enhances the demand for storing the data across a network of machines.
However, despite being a lucrative career option, Data Scientists face several challenges occasionally. The following blog will discuss the familiar Data Science challenges professionals face daily. It contains data clustering, classification, anomaly detection and time-series forecasting.
Unify Data Sources Collect data from multiple systems into one cohesive dataset. To confirm seamless integration, you can use tools like ApacheHadoop, Microsoft Power BI, or Snowflake to process structured data and Elasticsearch or AWS for unstructured data.
Now that you know why it is important to manage unstructured data correctly and what problems it can cause, let's examine a typical project workflow for managing unstructured data. They enable flexible data storage and retrieval for diverse use cases, making them highly scalable for big data applications.
Here are some project ideas suitable for students interested in big data analytics with Python: 1. Kaggle datasets) and use Python’s Pandas library to perform datacleaning, data wrangling, and exploratory data analysis (EDA). Analyzing Large Datasets: Choose a large dataset from public sources (e.g.,
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