About this Course:
The course is a highly practical introduction to data-science using Python as a language of choice for its wide variety of libraries and tools designed for the subject.
Students will learn to use widely-used libraries: pandas for data preparation and reporting, numpy for efficient array computations and scikit-learn for unsupervised learning (clustering and data compression) and supervised learning (linear regression, logistic regression, decision trees, and support vector classifiers). Students will also be introduced to the process of designing their own machine learning method through recommendation systems. The provided course materials also will serve as a useful reference (of practical examples) that supports post-course revision and practice. Students who gain competence in the materials (a function of effort) will be ready to enhance their work with elements of data science.
Students are required to have basic knowledge of programming before signing up for this course.
If you want to pick up basic programming skills, please register for our Python Development course instead.
What You Get
Students will learn how to perform the various parts of the "data science process":
- Preparing data: Cleaning, transforming, etc.
- Selecting a machine learning model (algorithm & settings)
- Training a model
- Evaluating a model
- Making predictions