DiegoTurri.com

SQL

Projects that include this skill

Bike sharing Data Analysis for data-driven business decisions

Goal: Convert casual users of the service into paying members Source: primary data, 12 datasets containing data for 2022 Context: You are a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. The marketing director believes the company’s future success depends on maximizing the number of annual memberships.…

Medical center front view on white background. Hospital building vector design.

Database Design of a Hospital Chain

Brief Description Given a text from Subject matter experts, I extracted insights to understand the requirements of the database. I created the E-R schema, restructured it, and then created the corresponding relational model. I made the SQL instructions to create tables and relationships between them. I used PostgreSQL as a database and linked it to…

Posts that include this skill

I’m officially a Google Certified Data Analyst!

I’m excited to share that I recently earned the Google Data Analyst Certification. This is a significant achievement for me, and I’m proud of the hard work and dedication that went into earning it. What is it? The “Google Data Analytics Certificate” is a professional certificate that is designed to prepare learners for entry-level data…

Definition

SQL, or Structured Query Language, is a programming language used to communicate with and manage data in relational databases. It is one of the most important tools for data scientists, as it allows them to efficiently query and analyze large and complex datasets.

SQL is a declarative language, which means that data scientists tell SQL what they want to do with the data, rather than how to do it. This makes SQL relatively easy to learn, even for those with no prior programming experience.

SQL can be used for a variety of tasks in data science, including:

  • Data cleaning: SQL can be used to clean and prepare data for analysis. This can involve tasks such as removing duplicate records, correcting errors, and converting data to a consistent format.
  • Data aggregation: SQL can be used to aggregate data into smaller, more manageable datasets. This can involve tasks such as grouping data by category, calculating summary statistics, and filtering data based on certain criteria.
  • Data analysis: SQL can be used to perform complex data analysis tasks. This can involve tasks such as identifying relationships between variables, developing predictive models, and detecting anomalies in the data.
  • Data visualization: SQL can be used to extract data from databases and prepare it for visualization in tools such as Tableau and Power BI.

SQL is an essential tool for data scientists, as it allows them to efficiently query and analyze large and complex datasets.By learning SQL, data scientists can expand their skillset and become more valuable to their organizations.

Here are some examples of how SQL is used in data science:

  • A data scientist uses SQL to query a database of customer purchase history to identify customers who have purchased a particular product in the past month.
  • A researcher uses SQL to query a database of gene expression data to identify genes that are associated with a particular disease.
  • A financial analyst uses SQL to query a database of stock prices to identify stocks that are likely to outperform the market in the next year.

Overall, SQL is a powerful and versatile tool for data science. It is used by a wide range of users to perform a variety of tasks, from data cleaning and data aggregation to data analysis and data visualization.