Advanced Data Science with Python

Why should you attend?

This Advanced Data Science with Python course is meticulously designed to provide an in-depth understanding of data science fundamentals, leveraging the power of Python. Participants will start by grasping the rationale behind using Python for data science and explore its program structure. As they progress, they'll delve into the nuances of execution steps, including working with interactive shells, script files, and integrated development environments (IDEs). The journey continues through memory management and garbage collection processes to ensure efficient object creation and deletion. A deep dive into data types and operations will equip learners with knowledge on handling various data structures such as numbers, strings, lists, tuples, dictionaries, sets, and more. Students will also learn about converting between these core types. Advanced topics cover statements and syntax rules for control flow constructs like loops and conditional statements. File operations are thoroughly explored, including file I/O for various formats and database interactions. The course then transitions into defining functions, understanding scope, and utilizing arguments effectively. Object-oriented programming concepts are introduced with a focus on classes, objects, inheritance, constructors, and operator overloading. The Pandas library is covered extensively for series and DataFrame manipulation alongside indexing, selecting data, function applications, reindexing strategies. Finally, machine learning techniques form a crucial part of the curriculum with detailed explanations of algorithms' mathematical foundations, application scenarios, differences from other algorithms, result interpretation using Python code examples. Topics such as hypothesis testing, correlation analysis, outlier detection are included along with specific statistical methods and machine learning models.