Data Science Dojo

JANUARY 12, 2023

Analytics Data lakes give various positions in your company, such as data scientists, data developers, and business analysts, access to data using the analytical tools and frameworks of their choice. You can perform analytics with Data Lakes without moving your data to a different analytics system. 4.

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Pickl AI

DECEMBER 25, 2024

Descriptive analytics is a fundamental method that summarizes past data using tools like Excel or SQL to generate reports. Techniques such as data cleansing, aggregation, and trend analysis play a critical role in ensuring data quality and relevance. Data Scientists require a robust technical foundation.

Data Science 52

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Pickl AI

MARCH 10, 2023

Data Science is the process in which collecting, analysing and interpreting large volumes of data helps solve complex business problems. A Data Scientist is responsible for analysing and interpreting the data, ensuring it provides valuable insights that help in decision-making.

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Pickl AI

JULY 25, 2023

Unfolding the difference between data engineer, data scientist, and data analyst. Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. Role of Data Scientists Data Scientists are the architects of data analysis.

Data Engineering 52

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Pickl AI

NOVEMBER 4, 2024

They are responsible for building and maintaining data architectures, which include databases, data warehouses, and data lakes. Their work ensures that data flows seamlessly through the organisation, making it easier for Data Scientists and Analysts to access and analyse information.

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Data Engineering Data Engineering Data Engineer Data Engineering 52

Pickl AI

JULY 18, 2023

Data Science helps businesses uncover valuable insights and make informed decisions. Programming for Data Science enables Data Scientists to analyze vast amounts of data and extract meaningful information. 8 Most Used Programming Languages for Data Science 1.

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Precisely

MARCH 9, 2023

Snowflake, for example, is a SaaS-based data warehouse application that is ideally for storing large volumes of data in the cloud, making it available for analytics. Apache Hadoop, for example, was initially created as a mechanism for distributed storage of large amounts of information.

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