Foundations of Data Science: Principles and Techniques

The course “Foundations of Data Science: Principles and Techniques” provides students with a comprehensive understanding of the fundamental principles and techniques that underpin the field of data science. This course serves as a solid introduction for individuals who are new to data science and wish to develop a strong foundation in the subject.

Through a combination of theoretical lectures and practical hands-on exercises, students will gain a deep understanding of the key concepts and methods used in data science. The course covers a wide range of topics, including data acquisition, data cleaning and preprocessing, exploratory data analysis, statistical inference, data visualization, and data storytelling.

Students will learn how to effectively manipulate and analyze data using popular programming languages such as Python and R. They will explore various data manipulation techniques, including data wrangling, feature engineering, and data transformation. Additionally, students will be introduced to statistical concepts and methodologies that are essential for making meaningful inferences from data.

The course also focuses on data visualization techniques to effectively communicate insights and patterns found in data. Students will learn how to create informative and visually appealing visualizations using popular libraries and tools such as Matplotlib, Seaborn, and Tableau.

By the end of the course, students will have gained a solid foundation in data science principles and techniques, enabling them to tackle real-world data problems with confidence. They will have the skills to acquire, clean, analyze, and visualize data, and will be well-prepared to dive deeper into advanced topics in data science.

Prerequisites: Basic knowledge of programming concepts and familiarity with a programming language such as Python or R is recommended. No prior knowledge of data science is required.