Advance your Data Science career abroad.

You’ll spend the first week of the course focusing on learning Python, including the process of importing data into Python, understanding data structures, manipulating data, statistics, applying aggregations and transformations, data plotting and visualization, and managing anomalies and missing data.

These broadly-applicable principles will be covered throughout your course. You’ll learn about different data sources and ethical considerations in the industry at the beginning of the course, along with deeper dives into understanding the relevance of data sets, appropriate management practices, identifying appropriate visualization strategies and post-deployment checks on machine learning modules.

A key aspect of the data science toolkit is the ability to identify the most appropriate way to visualize data for the sake of telling its most compelling story. The first week will include classes that dive into the principles of identifying appropriate visualizations, graphs and other infographic methods, as well as strategies for framing data sets’ narratives that are most relevant for differing data types. You’ll learn to use the Seaborn module to create visualizations in Python.

The second week of the course focuses on machine learning, where you will develop a sound conceptual understanding of regression techniques broadly, and linear regression as a specific example. In addition, you will be able to implement logistic regression in Python with scikit-learn. This section will form the foundation for learning more advanced principles later in the course.

In the second week of the course, you will learn dimension reduction techniques broadly, and principal components analysis (PCA) as a specific example. You will also learn clustering techniques, K-means and hierarchical clustering as part of the unsupervised learning toolkit. This section will form the foundation for learning more advanced principles later in the course.

In weeks 3-6, while doing your internship, you’ll continue to learn more advanced techniques in the data science pipeline. Learn to create decision trees and tree ensembles, as well as how to identify when to use them. Understand the mathematics behind support vector machines, how they are trained, how to choose their hyper-parameters and when to use them. Do an introduction to Natural Language Processing, where text inputs are manipulated to develop predictive models. Learn about time series models and neural networks, which lead to the ability to build regression models in Python.