Associate Data Scientist in Python (previously Data Scientist with Python)

Learn to combine data from multiple tables by joining data together using pandas.

4 hr

Find the true Scala experts by exploring its development history in Git and GitHub.

2 hr

Learn how to create, customize, and share data visualizations using Matplotlib.

4 hr

Learn how to create informative and attractive visualizations in Python using the Seaborn library.

4 hr

Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.

3 hr

Continue to build your modern Data Science skills by learning about iterators and list comprehensions.

4 hr

Use Seaborn's sophisticated visualization tools to make beautiful, informative visualizations with ease.

4 hr

Explore a dataset from Kaggle containing a century's worth of Nobel Laureates. Who won? Who got snubbed?

2 hr

Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.

3 hr

Improve your Python data importing skills and learn to work with web and API data.

2 hr

Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights!

4 hr

Learn how to work with dates and times in Python.

4 hr

Learn to use best practices to write maintainable, reusable, complex functions with good documentation.

4 hr

Explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior using pandas.

4 hr

Build the foundation you need to think statistically and to speak the language of your data.

3 hr

Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.

4 hr

Reanalyse the data behind one of the most important discoveries of modern medicine: handwashing.

2 hr

Build a machine learning model to predict if a credit card application will get approved.

2 hr

Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.

4 hr

In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.

5 hr

In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.

4 hr