JetBrains AcademyBy the end of this course, you will be able to:
Understand the role of data engineering and how data is collected, stored, and used in analytics systems;
Use Python, Pandas, and SQL to read, clean, transform, and move data between systems;
Design and work with relational databases and write efficient SQL queries;
Work with NoSQL databases such as MongoDB and understand when to use them;
Build and schedule basic ETL pipelines using Apache Airflow;
Apply data quality, performance, and optimization techniques to maintain reliable data systems.
Master advanced engineering concepts through ambitious projects. Each project deepens your expertise and transforms you from an experienced engineer into an exceptional one.
In this project, you'll explore the rich collection of research articles on arXiv while gaining hands-on experience with SQL. You'll start by mastering basic SQL queries to extract and analyze article metadata, including authors, titles, and abstracts. As you progress, you'll learn to perform similarity searches using vector embeddings.
Graduate
Data preprocessing is one of the first steps in the machine learning workflow. The main idea is to transform raw data into a format that machine learning algorithms can easily understand. The predictive performance of a machine learning model highly depends on the input data quality. Thus, it's an absolute must to know how to improve the quality of your input data by removing the features with low predictive value, engineering new ones, and dealing with multicollinearity. With this project, you'll apply these concepts to NBA data to get a high-quality dataset ready to be fed to a linear model!
Graduate
