SQL Crash Course: A First Step Towards Big Data
SQL is one of the most in demand tech skills of today.
SQL (Structured Query Language) is a universal database query language widely used by large organisations to update and retrieve information from a database and is the common standard for all relational databases that most apps are built on.
Excel spreadsheets are useful for data analysis and visualisation, but can be slow, cumbersome and downright impossible to use with large datasets. Relational databases such as MySQL or powerful data warehouses such as Oracle can address many of Excel’s limitations, by streamlining and simplifying data transformations over Big Data. However, one needs to know SQL to fully harness these technologies.
About the Course
This 2-day course provides an intensive introduction to the world of databases and analysis in SQL. Students will be provided a virtual machine containing the popular MySQL database environment on their own laptops, and run queries over datasets containing millions of records. After this class, students will understand the fundamentals of standard SQL, and will be comfortable writing queries using MySQL Workbench (one of the world's most popular database tools).
§ Learn introductory database concepts
§ Understand the fundamentals and the intuition behind SQL
§ Learn how to write standard SQL queries to explore and manipulate data –
§ including filtering, aggregation and merging of very large datasets
§ Develop a solid foundation to self-learn SQL beyond the course
Who should attend?
§ Anyone who is interested in data
§ Anyone who wants to catch up the trend of the era
§ Anyone who wants to have potential career escalation with SQL skills.
Prerequisites & Preparation
§ Interest in databases and SQL
§ A laptop running Windows/Mac OS X with 4GB memory or greater
§ Installation instructions will be provided upon registration
§ No programming knowledge required.
Instructor: Yuan Ruo, Data Scientist
Yuan received his MSc in Computing Science from Imperial College London, where he completed his thesis in machine learning (inverse reinforcement learning) and robotics. He also received his BEng in Engineering with Business Finance from University College London and the London School of Economics.
He has 2½ years experience working as a data scientist and runs SQL queries over massive datasets every week.