Objectives

1. Master Statistical and Machine Learning Techniques: Cover a range of statistical methods and machine learning algorithms applicable to engineering problems, including regression analysis, classification, clustering, and time series analysis.
2. Apply Data Science Techniques to Engineering Challenges: Illustrate how data science techniques can be applied to solve real-world engineering problems, such as predictive maintenance, process optimization, fault detection, and quality control.
3. Develop Proficiency in Programming Languages: Build proficiency in programming languages commonly used in data science and engineering, such as Python and R, focusing on data manipulation, analysis, and visualization libraries.
4. Utilize Tools and Frameworks: Introduce students to popular data science tools and frameworks, such as pandas, NumPy, scikit-learn, TensorFlow, and PyTorch, enabling them to efficiently analyse large datasets and build machine learning models.
5. Emphasize Data Visualization: Emphasize the importance of data visualization techniques for communicating insights effectively, covering tools like Matplotlib, Seaborn, and Plotly to create informative and visually appealing plots and charts.