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Summary: A comprehensive BigData syllabus encompasses foundational concepts, essential technologies, data collection and storage methods, processing and analysis techniques, and visualisation strategies. Fundamentals of BigData Understanding the fundamentals of BigData is crucial for anyone entering this field.
To pursue a data science career, you need a deep understanding and expansive knowledge of machine learning and AI. To pursue a data science career, you need a deep understanding and expansive knowledge of machine learning and AI. And you should have experience working with bigdata platforms such as Hadoop or Apache Spark.
Skills like effective verbal and written communication will help back up the numbers, while data visualization (specific frameworks in the next section) can help you tell a complete story. DataWrangling: Data Quality, ETL, Databases, BigData The modern data analyst is expected to be able to source and retrieve their own data for analysis.
Data Analysts need deeper knowledge on SQL to understand relational databases like Oracle, Microsoft SQL and MySQL. Moreover, SQL is an important tool for conducting Data Preparation and DataWrangling. For example, Data Analysts who need to use BigData tools for conducting data analysis need to have expertise in SQL.
These courses introduce you to Python, Statistics, and Machine Learning , all essential to Data Science. Starting with these basics enables a smoother transition to more specialised topics, such as Data Visualisation, BigData Analysis , and Artificial Intelligence. Prestigious Background : Offered by Harvard University.
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