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This doesn’t mean anything too complicated, but could range from basic Excel work to more advanced reporting to be used for data visualization later on. Computer Science and Computer Engineering Similar to knowing statistics and math, a data scientist should know the fundamentals of computer science as well.
Key Takeaways Big Data focuses on collecting, storing, and managing massive datasets. Data Science extracts insights and builds predictive models from processed data. Big Data technologies include Hadoop, Spark, and NoSQL databases. Data Science uses Python, R, and machine learning frameworks.
To pursue a data science career, you need a deep understanding and expansive knowledge of machine learning and AI. Your skill set should include the ability to write in the programming languages Python, SAS, R and Scala. And you should have experience working with big data 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, Big Data The modern data analyst is expected to be able to source and retrieve their own data for analysis.
Apache Spark A fast, in-memory data processing engine that provides support for various programming languages, including Python, Java, and Scala. DataLake vs. Data Warehouse Distinguishing between these two storage paradigms and understanding their use cases. What Skills Are Necessary for A Career in Big Data?
Data scientists typically have strong skills in areas such as Python, R, statistics, machine learning, and data analysis. Believe it or not, these skills are valuable in data engineering for datawrangling, model deployment, and understanding data pipelines.
Example template for an exploratory notebook | Source: Author How to organize code in Jupyter notebook For exploratory tasks, the code to produce SQL queries, pandas datawrangling, or create plots is not important for readers. If a reviewer wants more detail, they can always look at the Python module directly.
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