Master the basics of data analysis in Python. Expand your data science skill set by learning scientific computing with numpy.
Level up your Python data science skills by creating visualizations using matplotlib and manipulating data frames with Pandas.
Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0.
This course will equip you with all the skills you need to clean your data in Python.
Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.
Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.
In this course, you'll learn how to use the industry-standard pandas library to import, build, and manipulate DataFrames.
Learn how to build and tune predictive models and evaluate how well they will perform on unseen data.
Continue to build your modern Data Science skills by learning about iterators and list comprehensions.
Build the foundation you need to think statistically and to speak the language of your data.
Further improve your Python data importing skills and learn to work with more web and API data.
Learn more complex data visualization techniques using Matplotlib and Seaborn.
In this course, you'll learn the basics of relational databases and how to interact with them.
You will learn how to tidy, rearrange, and restructure your data using versatile pandas DataFrames.
This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.
This course is all about the act of combining, or merging, DataFrames, an essential part of any working Data Scientist's toolbox.
Learn how to create versatile and interactive data visualizations using Bokeh.
Learn how to build a model to automatically classify items in a school budget.
Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.
Learn to visualize multivariate datasets using lattice plotting.
Learn how to make predictions about the future using time series forecasting in R.