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Machine learning practitioners are often working with data at the beginning and during the full stack of things, so they see a lot of workflow/pipeline development, datawrangling, and datapreparation.
As a Python user, I find the {pySpark} library super handy for leveraging Spark’s capacity to speed up data processing in machine learning projects. But here is a problem: While pySpark syntax is straightforward and very easy to follow, it can be readily confused with other common libraries for datawrangling.
One is a scripting language such as Python, and the other is a Query language like SQL (Structured Query Language) for SQL Databases. Python is a High-level, Procedural, and object-oriented language; it is also a vast language itself, and covering the whole of Python is one the worst mistakes we can make in the data science journey.
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 DataPreparation and DataWrangling.
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.
Databricks: Powered by Apache Spark, Databricks is a unified data processing and analytics platform, facilitates datapreparation, can be used for integration with LLMs, and performance optimization for complex prompt engineering tasks. PythonPython’s prominence is expected.
Amazon SageMaker Canvas is a low-code no-code (LCNC) ML platform that guides users through every stage of the ML journey, from initial datapreparation to final model deployment. Without writing a single line of code, users can explore datasets, transform data, build models, and generate predictions. The script.py The script.py
Allen Downey, PhD, Principal Data Scientist at PyMCLabs Allen is the author of several booksincluding Think Python, Think Bayes, and Probably Overthinking Itand a blog about data science and Bayesian statistics. in Ecology, he brings a unique perspective to statistics, spatial analysis, and real-world data applications.
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