Take Your Data Science Skills to the Next Level with Anaconda Academy
Interactive Data Science Training Designed by Python Experts
Discover the art of using Python for data modeling, visualization, machine learning, and more
Hone your data science skills to prepare you for the daily challenges you face
Extract insights from your data to amplify your organizational impact
Our Training Offerings
Anaconda Training is ideal for groups of data science practitioners looking to hone their skills and deliver actionable insights to their organizations. The courses outlined below are intended to complement your journey with Anaconda Enterprise or Anaconda Distribution.
The Data Science Learning Path
Designed by the Python experts at Anaconda, our Data Science Learning Path is the compass you need to navigate your data science journey.
The Anaconda Ecosystem
The Anaconda Ecosystem (coming soon)
Anaconda Packaging (coming soon)
This course covers how to write your own functions in Python to solve problems that are dictated by your data. You'll come out of this course being able to write your very own custom functions, complete with multiple parameters and multiple return values, along with default arguments and variable-length arguments.
This course will introduce you to working with iterators and list comprehensions, which are handy tools for data scientists working in Python. You'll end the course by working through a case study in which you'll apply all of the techniques you learned both in this course as well as in Part I.
Data Import and Export
This course demonstrates the many ways to import data into Python: from flat files such as .txts and .csvs; from files native to other software such as Excel spreadsheets, Stata, SAS, and MATLAB files; and from relational databases such as SQLite & PostgreSQL.
This course extends the knowledge gained in Part 1 by showing you how to import data from the web and how to pull data from Application Programming Interfaces (APIs).
In this course, you'll learn the basics of using Structured Query Language (SQL) with Python.
Data Manipulations & Analysis
This course will teach you how to use the industry-standard pandas library to import, build, and manipulate DataFrames.
This course will show you how to leverage pandas' extremely powerful data manipulation engine to get the most out of your data. You will learn how to tidy, rearrange, and restructure your data by pivoting or melting and stacking or unstacking DataFrames.
This course will teach you the act of combining, or merging, DataFrames, an essential part of any working Data Scientist's toolbox. You'll hone your pandas skills by learning how to organize, reshape, and aggregate multiple data sets to answer your specific questions.
This course will equip you with all the skills you need to clean your data in Python, from learning how to diagnose your data for problems to dealing with missing values and outliers.
This course extends Intermediate Python for Data Science to provide a stronger foundation in data visualization in Python. Topics covered include customizing graphics, plotting two-dimensional arrays, statistical graphics, and working with time series and image data.
In this course, you will learn the fundamentals of Bokeh, an interactive data visualization library for Python (and other languages!) that targets modern web browsers for presentation. You will create versatile, data-driven graphics, and connect the full power of the entire Python data science stack to rich, interactive visualizations.
Drawing conclusions from your data hinges on the principles of statistical inference. In this course, you will start building the foundation you need to think statistically and speak the language of your data.
In Part II, you will dive into data sets, expanding and honing your hacker stats toolbox to perform the two key tasks in statistical inference, parameter estimation and hypothesis testing.
In this course, you'll learn how to use scikit-learn—one of the most popular and user-friendly machine learning libraries for Python—to perform supervised learning, an essential component of machine learning. Using real-world datasets, you will learn how to build predictive models, how to tune their parameters, and how to tell how well they will perform on unseen data.
This course covers the fundamentals of unsupervised learning using scikit-learn and SciPy. You will learn how to cluster, transform, visualize, and extract insights from unlabeled datasets.
This course focuses on a case study related to school district budgeting, based on a machine learning competition on DrivenData.
Data Science at Scale
PySpark (coming soon)
Parallel Computing with Dask (coming soon)
Interested in enrolling in Anaconda Training?
We can also deliver onsite Anaconda Training classes at your organization or our facility in Austin, Texas. Classes can follow the Data Science Learning Path, or can be customized to fit the specific needs of your organization. Contact us to learn more.