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A Sneak Peek at SQL Bootcamp for Data Science A Sneak Peek at SQL Bootcamp for Data Science
For years now, ODSC has served as a resource for data professionals to connect, learn from each other, and contribute back to the community... A Sneak Peek at SQL Bootcamp for Data Science

For years now, ODSC has served as a resource for data professionals to connect, learn from each other, and contribute back to the community as our field moves forward at a lightning pace. Their commitment to data science has extended into the current pandemic, resulting in their first virtual conference accessible to data scientists around the world. I was eager to lead a session introducing SQL for data science, as I’m eager now to expand on that session with their new AI+ training platform.

SQL is everywhere in the data world. No matter your domain experience, field of interest, or tool of choice, knowing how to write effective SQL queries will save you time, energy, and increase your productivity and understanding of your data. The better your depth of SQL knowledge, the more easily you can tackle problems in Excel, create features for machine learning models, or create an efficient ETL process in data engineering. SQL is truly the skill to have when working with any kind of data.

Following a series of courses designed to teach you machine learning, deep learning, and data storytelling, SQL Bootcamp for Data Science aims to give you the confidence you need to ensure you can retrieve, transform, and augment the data you need to perform any data analytics data science, or data engineering task. This course empowers you to explore and quickly derive value from data you encounter anywhere — a new job, new data sources, a job application assessment, or designing a data warehouse. You will complete this session confident in your ability to efficiently write queries for any kind of relational (SQL) database.

Over the course of this 4-hour session, you will learn the following skills:

  • Understanding data contained in database tables and the relationships between them
  • Writing queries to aggregate, filter, and sort data in a table
  • Joining data from multiple tables in a relational database
  • Performing mathematical operations on one or more columns of data
  • Identifying the need for subqueries used to transform your data in multiple steps
  • Choosing the appropriate subquery to complete a task
  • Translating business questions into the best choice of SQL query based on available data

And that’s not all — you’ll complete this course with a comprehensive set of resources to further practice the skills you learned and continue taking your SQL knowledge to the next level.

If you’re new to the data world, have primarily worked in proprietary tools (Excel, SPSS, SAS, STATA), and are ready to dive into the rich and complex world of databases, then this session is for you. I invite you to register for my course taking place on September 30th, with a 20% discount available until September 25th. I look forward to working with you!


About the instructor: Mona Khalil is a Data Scientist at Greenhouse Software in New York City, where they contribute to data-informed decision making across the company and machine learning solutions to improve the hiring process for Greenhouse customers. They’ve previously worked in government, creating analytics and machine learning solutions to improve the lives of New Yorkers, and continue to be involved in civic projects through a number of volunteer and non-profit organizations. They’ve also been a statistics and data science educator with DataCamp, Emeritus, and in university settings. They hold a graduate degree in Developmental Psychology, and are passionate about contributing to the ethical use of data science methodology in the public and private sector.

ODSC Community

ODSC Community

The Open Data Science community is passionate and diverse, and we always welcome contributions from data science professionals! All of the articles under this profile are from our community, with individual authors mentioned in the text itself.

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