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Business Analytics vs Data Science: Which One Is Right for You?

Pickl AI

With data science and analytics reshaping industries, understanding the distinction between Business Analytics and Data Science is crucial for anyone navigating a career in this field. According to the US Bureau of Labor Statistics, jobs requiring data science skills will grow by 27.9%

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Discover the Most Important Fundamentals of Data Engineering

Pickl AI

The global Big Data and Data Engineering Services market, valued at USD 51,761.6 This article explores the key fundamentals of Data Engineering, highlighting its significance and providing a roadmap for professionals seeking to excel in this vital field. ETL is vital for ensuring data quality and integrity.

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Data Warehouse vs. Data Lake

Precisely

Data warehouse vs. data lake, each has their own unique advantages and disadvantages; it’s helpful to understand their similarities and differences. In this article, we’ll focus on a data lake vs. data warehouse. It is often used as a foundation for enterprise data lakes.

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The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Pickl AI

Two prominent roles that play a crucial part in this data-driven landscape are Data Scientists and Data Engineers. Data Quality and Governance Ensuring data quality is a critical aspect of a Data Engineer’s role. Data Warehousing: Amazon Redshift, Google BigQuery, etc.

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Beginner’s Guide To GCP BigQuery (Part 1)

Mlearning.ai

In my 7 years of Data Science journey, I’ve been exposed to a number of different databases including but not limited to Oracle Database, MS SQL, MySQL, EDW, and Apache Hadoop. A lot of you who are already in the data science field must be familiar with BigQuery and its advantages.

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8 Best Programming Language for Data Science

Pickl AI

Programming for Data Science enables Data Scientists to analyze vast amounts of data and extract meaningful information. There are different programming languages and in this article, we will explore 8 programming languages that play a crucial role in the realm of Data Science.

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The Backbone of Data Engineering: 5 Key Architectural Patterns Explained

Mlearning.ai

Data engineering is a rapidly growing field that designs and develops systems that process and manage large amounts of data. There are various architectural design patterns in data engineering that are used to solve different data-related problems.