Introduction to Data Science and Machine Learning

An introductory journey into Data Science and Machine Learning, blending theory, coding, and real-world applications.

What you'll learn
- Grasp foundational math, statistics, and data science principles that underlie modern machine learning.
- Create compelling data visualizations and use storytelling to communicate insights effectively.
- Explore Machine Learning Algorithms: Learn key algorithms including regression, neural networks, and unsupervised methods like clustering.
- Utilize programming tools to address real-world problems using Python and its popular libraries.
- Utilize data-driven techniques to tackle real-world issues in fields such as engineering, business, and journalism.

Requirements
- Having a solid understanding of basic algebra and logical reasoning is essential for recognizing data patterns and algorithms.
- Interest in Data and Problem Solving: A curious mindset and a willingness to explore how data can address real-world challenges.

Description
This course provides a thorough introduction to the intersection of data science and machine learning, balancing theory, numerical methods (coding), and real-world applications. It is designed for students and beginners who want to build a strong foundation in the concepts, statistics, and mathematics that support modern data science and machine learning algorithms.

No prior experience is required; this course starts with the fundamentals, making it an excellent choice for beginners ready to embark on their learning journey.

The course covers essential topics, including:

- The basics of data science

- Data visualisation and storytelling

- Linear and non-linear regression methods

- Explore the world of classification techniques with powerful tools like decision trees, random forests, and neural networks to unlock insights from your data.

- Dive into unsupervised learning, where you can discover hidden patterns and groupings in your data using innovative clustering methods like spectral clustering.

By the end of this course, students will be able to:

- Apply quantitative modelling and data analysis techniques to solve real-world problems.

- Effectively communicate findings through data visualisation.

- Demonstrate proficiency in statistical data analysis techniques used in applied engineering.

- Utilise data science principles to tackle engineering challenges.

- Employ modern programming languages and computational tools to analyse big data.

- Understand key concepts and gain in-depth knowledge of classical machine learning algorithms.

- Implement classic machine learning algorithms to create intelligent systems.

Who this course is for:
- Undergraduate and Graduate students in engineering, computer science, or related fields looking to grasp data-driven methods. Beginners in programming or data science interested in data analysis—no prior expertise needed. Aspiring data scientists and ML engineers seeking a solid foundation in key concepts. Professionals in engineering and applied sciences wanting to apply data science to real-world problems.
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