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Beyond data: Cloud analytics mastery for business brilliance

Dataconomy

Here are some of the key types of cloud analytics: Descriptive analytics: This type focuses on summarizing historical data to provide insights into what has happened in the past. It helps organizations understand trends, patterns, and anomalies in their data. Ensure that data is clean, consistent, and up-to-date.

Analytics 203
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Data Science Career Paths: Analyst, Scientist, Engineer – What’s Right for You?

How to Learn Machine Learning

Data Cleaning and Preparation The tasks of cleaning and preparing the data take place before the analysis. This includes duplicate removal, missing value treatment, variable transformation, and normalization of data. Some of the famous tools and libraries are Python’s scikit-learn, TensorFlow, PyTorch, and R.

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A Guide to Choose the Best Data Science Bootcamp

Data Science Dojo

Data science bootcamps are intensive short-term educational programs designed to equip individuals with the skills needed to enter or advance in the field of data science. They cover a wide range of topics, ranging from Python, R, and statistics to machine learning and data visualization.

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Where Does Fivetran Fit into The Modern Data Stack?

phData

Over the past few decades, the corporate data landscape has changed significantly. The shift from on-premise databases and spreadsheets to the modern era of cloud data warehouses and AI/ LLMs has transformed what businesses can do with data. This is where Fivetran and the Modern Data Stack come in.

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Optimizing Matillion Workflows: A Guide to Visual Design and Best Practices

phData

With a background in Data Visualization and BI tools, Ive always approached things with a visual mindset. A Matillion pipeline is a collection of jobs that extract, load, and transform (ETL/ELT) data from various sources into a target system, such as a cloud data warehouse like Snowflake.

AI 52
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The Modern Data Stack Explained: What The Future Holds

Alation

Data ingestion/integration services. Reverse ETL tools. Data orchestration tools. These tools are used to manage big data, which is defined as data that is too large or complex to be processed by traditional means. How Did the Modern Data Stack Get Started? A Note on the Shift from ETL to ELT.

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How Does Snowflake Ensure High Availability and Disaster Recovery for Data?

phData

Using cloud data services can be nerve-wracking for some companies. Yes, it’s cheaper, faster, and more efficient than keeping your data on-premises, but you’re at the provider’s mercy regarding your available data.