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Data Wrangling with Python

Mlearning.ai

The goal of data cleaning, the data cleaning process, selecting the best programming language and libraries, and the overall methodology and findings will all be covered in this post. Data wrangling requires that you first clean the data. Getting Started First, we need to import the necessary libraries.

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Introduction to SQL for Data Science

Pickl AI

The easiest skill that a Data Science aspirant might develop is SQL. Management and storage of Data in businesses require the use of a Database Management System. This blog would an introduction to SQL for Data Science which would cover important aspects of SQL, its need in Data Science, and features and applications of SQL.

SQL 52
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How Dataiku and Snowflake Strengthen the Modern Data Stack

phData

Snowflake excels in efficient data storage and governance, while Dataiku provides the tooling to operationalize advanced analytics and machine learning models. Together they create a powerful, flexible, and scalable foundation for modern data applications.

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State of Machine Learning Survey Results Part One

ODSC - Open Data Science

In a series of articles, we’d like to share the results so you too can learn more about what the data science community is doing in machine learning. In the first blog, we’re going to discuss the technical side of things, such as what languages and platforms people are using. What areas of machine learning are you interested in?

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The Top Ten Certifications For Data Analysts

Pickl AI

A Data Analyst certification builds credibility, validates expertise, and opens doors to advanced career opportunities. This blog explores top certifications, factors to consider when choosing one, and future trends, helping aspiring and experienced analysts navigate their professional growth effectively.

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Moving from Traditional to Active Data Governance

Alation

Rather than locking the data away from those who need it, this approach instead welcomes more users to the data — but adds guardrails to guide use. Deprecation warnings, SQL AutoSuggest, and quality flags are examples of “guardrail features.” Provide as much information as possible to make the data easier to trust.

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Data science vs data analytics: Unpacking the differences

IBM Journey to AI blog

And you should have experience working with big data platforms such as Hadoop or Apache Spark. Additionally, data science requires experience in SQL database coding and an ability to work with unstructured data of various types, such as video, audio, pictures and text.