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From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you’ll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization.
Provides a background in data science fundamentals before moving on to working with relational databases and unstructured data and preparing your data for analysis
Details different data visualization techniques that can be used to showcase and summarize your data
Explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques
Includes coverage of big data processing tools like MapReduce, Hadoop, Dremel, Storm, and Spark
