ODSC - Open Data Science

JANUARY 18, 2024

Data engineering is a rapidly growing field, and there is a high demand for skilled data engineers. If you are a data scientist, you may be wondering if you can transition into data engineering. In this blog post, we will discuss how you can become a data engineer if you are a data scientist.

Data Engineering 52

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ODSC - Open Data Science

FEBRUARY 2, 2023

Scale is worth knowing if you’re looking to branch into data engineering and working with big data more as it’s helpful for scaling applications. This includes popular tools like Apache Airflow for scheduling/monitoring workflows, while those working with big data pipelines opt for Apache Spark.

Data Science 98

Data Science Data Scientist Computer Science Computer Science 98

IBM Journey to AI blog

SEPTEMBER 19, 2023

By analyzing datasets, data scientists can better understand their potential use in an algorithm or machine learning model. The data science lifecycle Data science is iterative, meaning data scientists form hypotheses and experiment to see if a desired outcome can be achieved using available data.

Data Science 84

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IBM Journey to AI blog

DECEMBER 16, 2022

Let’s look at five benefits of an enterprise data catalog and how they make Alex’s workflow more efficient and her data-driven analysis more informed and relevant. A data catalog replaces tedious request and data-wrangling processes with a fast and seamless user experience to manage and access data products.

Data Quality 52

Data Quality Data Governance Data Wrangling Data Scientist 52

phData

FEBRUARY 14, 2023

A traditional approach requires massive efforts and a long lead time in sourcing from various data providers, data pipelining, and integrating into data marts. Also today’s volume, variety, and velocity of data, only intensify the data-sharing issues.

Data Governance 52

Data Governance Data Silos Predictive Analytics Data Wrangling 52

Pickl AI

MAY 15, 2024

Data Analyst to Data Scientist: Level-up Your Data Science Career The ever-evolving field of Data Science is witnessing an explosion of data volume and complexity. Ensuring data quality and implementing robust data pipelines for cleaning and standardization becomes paramount.

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