We will explore the wonderfully concise and expressive use of Python’s advanced module features and apply it in probability, statistical analysis, training models, and various other applications.

Students will explore mathematical operations with array data structures, optimization, probability density function, interpolation, visualization, and other high-performance benefits of core scientific packages such as NumPy, SciPy, scikit-learn, and Matplotlib.

Once you gain a deep understanding of the practical aspects of Python for data wrangling involved in ML and AI applications, you will be a more effective problem solver when dealing with engineering and scientific challenges in your own career.

Learning Outcomes
At the conclusion of the course, the student should be able to

• Develop complex functions and scripts to perform complicated calculations to solve engineering, financial, mathematical and scientific problems and visualize the results of these calculations.
• Install, configure Python and essential Python development tools and write programs to perform data analysis, statistical analysis, learning and AI techniques.
• Manage and manipulate data, perform data type conversions, merge datasets, deal with missing values, and extract, delete, or transform subsets of data based on logical criteria.
• Attain deeper understanding of the mathematical toolkit provided by powerful core packages and acquire hands-on experience.

Skills Needed:

Basic Programming Knowledge as can be acquired in Python Programming for Beginners (CMPR.X415) and a knowledge of Fundamentals of Statistics