110+ Exercises - Python + SQL (sqlite3) - SQLite Databases
What you'll learn
- projects: e-learning platform, company database, forum application
- solve 110+ exercises with SQL and Python
- Python + SQL (sqlite3) + csv
- Python + SQL (sqlite3) + pandas
- User-defined SQL functions
- deal with real programming problems
- work with documentation and Stack Overflow
- guaranteed instructor support
- completion of all courses in the Python Developer learning path
- completion of all courses in the SQL Developer learning path
The "110+ Exercises - Python + SQL (sqlite3) - SQLite Databases" course is a comprehensive learning resource designed to equip learners with a strong understanding of integrating Python with SQLite, a self-contained, serverless, and zero-configuration database engine. This course aims to help learners enhance their data manipulation and analysis skills using these two powerful tools.
With over 110 exercises, the course starts with basics such as creating and connecting to SQLite databases using Python's built-in sqlite3 module. Learners then proceed to more complex topics, including writing SQL queries, updating and deleting records, and handling transactions in Python. The course also covers error handling and database security to ensure the robustness and integrity of your applications.
Each exercise poses a unique problem, with a focus on practical, real-world scenarios. The exercises are designed to challenge learners and stimulate their problem-solving skills. Solutions to all exercises are provided, allowing learners to compare their approach and gain different perspectives on solving problems.
As the course advances, learners will also tackle more complex topics, such as optimizing queries, using advanced SQL constructs in Python, and understanding database models.
The "110+ Exercises - Python + SQL (sqlite3) - SQLite Databases" course is an excellent choice for Python developers, data analysts, or anyone interested in handling databases with Python and SQLite. It is also beneficial for data science aspirants, as it provides a solid foundation for data manipulation and exploration. A basic understanding of Python and SQL is recommended to make the most of this course.
SQLite - Lightweight Database Powerhouse!
SQLite is a lightweight, serverless, and self-contained relational database management system. It is widely used as an embedded database in various applications due to its simplicity, small footprint, and high performance. SQLite stores the entire database in a single file, making it easy to deploy and manage without requiring a separate server process.
Despite its compact size, SQLite supports a full range of SQL features and provides ACID (Atomicity, Consistency, Isolation, Durability) compliance. It offers a reliable and efficient way to store and retrieve structured data, making it suitable for small to medium-sized applications, mobile devices, and embedded systems.
With SQLite, developers can create tables, define relationships, and execute SQL queries to perform various operations such as inserting, updating, deleting, and querying data. It supports common data types, transactions, indexes, and triggers, enabling efficient data manipulation and retrieval.
SQLite integrates seamlessly with different programming languages, including Python, C, C++, and more, making it highly versatile and widely adopted. It is used in a wide range of applications, such as mobile apps, desktop software, web browsers, IoT devices, and data analysis tools.
In summary, SQLite provides a lightweight and efficient solution for managing relational databases without the need for a dedicated database server. It offers simplicity, flexibility, and reliability, making it a popular choice for applications that require local data storage and retrieval.
Who this course is for:
- Python developers or programmers who want to learn how to work with SQLite databases using the sqlite3 module in Python
- students or individuals with a basic understanding of Python who want to gain hands-on experience in managing and querying SQLite databases using Python
- data analysts or data scientists who want to add SQLite as a data storage and retrieval option to their data manipulation and analysis toolkit in Python
- professionals working with small to medium-sized datasets who need a lightweight and easy-to-use database solution like SQLite and want to leverage its features through Python
- self-learners or enthusiasts interested in databases and SQL, who want to practice creating, managing, and querying SQLite databases using Python
- developers who want to understand the fundamentals of database management and SQL syntax using SQLite and Python as a stepping stone to more complex database systems
Python Developer/AI Enthusiast/Data Scientist/Stockbroker
Enthusiast of new technologies, particularly in the areas of artificial intelligence, the Python language, big data and cloud solutions. Graduate of postgraduate studies at the Polish-Japanese Academy of Information Technology in the field of Computer Science and Big Data specialization. Master's degree graduate in Financial and Actuarial Mathematics at the Faculty of Mathematics and Computer Science at the University of Lodz. Former PhD student at the faculty of mathematics. Since 2015, a licensed Securities Broker with the right to provide investment advisory services (license number 3073). Lecturer at the GPW Foundation, conducting training for investors in the field of technical analysis, behavioral finance, and principles of managing a portfolio of financial instruments.
Founder at e-smartdata
Data Scientist, Securities Broker
Jestem miłośnikiem nowych technologii, szczególnie w obszarze sztucznej inteligencji, języka Python big data oraz rozwiązań chmurowych. Posiadam stopień absolwenta podyplomowych studiów na kierunku Informatyka, specjalizacja Big Data w Polsko-Japońskiej Akademii Technik Komputerowych oraz magistra z Matematyki Finansowej i Aktuarialnej na wydziale Matematyki i Informatyki Uniwersytetu Łódzkiego. Od 2015 roku posiadam licencję Maklera Papierów Wartościowych z uprawnieniami do czynności doradztwa inwestycyjnego (nr 3073). Jestem również wykładowcą w Fundacji GPW prowadzącym szkolenia dla inwestorów z zakresu analizy technicznej, finansów behawioralnych i zasad zarządzania portfelem instrumentów finansowych. Mam doświadczenie w prowadzeniu zajęć dydaktycznych na wyższej uczelni z przedmiotów związanych z rachunkiem prawdopodobieństwa i statystyką. Moje główne obszary zainteresowań to język Python, sztuczna inteligencja, web development oraz rynki finansowe.
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