Python for Data Science

Understanding Python for Data Science

Enthought’s Python for Data Science training course is designed to accelerate the development of skill and confidence in using Python’s core data science tools — including the standard Python language, the fast array programming package NumPy, and the Pandas data analysis package, as well as tools for database access (DBAPI2, SQLAlchemy), machine learning (scikit-learn), and visual exploration (Matplotlib, Seaborn).

What: A guided walkthrough and Q&A about Enthought’s technical training course “Python for Data Science and Machine Learning” with VP of Training Solutions, Dr. Michael Connell

Who Should Watch: individuals, team leaders, and learning & development coordinators who are looking to better understand the options to increase professional capabilities in Python for data science and machine learning applications

VIEW the Python for Data Science webinar here

In this webinar, we give you the key information and insight you need to evaluate whether Enthought’s Python for Data Science course is the right solution to advance your professional data science skills in Python, including:

  • Who will benefit most from the course
  • A guided tour through the course topics
  • What skills you’ll take away from the course, how the instructional design supports that
  • What the experience is like, and why it is different from other training alternatives (with a sneak peek at actual course materials)
  • What previous course attendees say about the course

VIEW the Python for Data Science webinar here


michael_connell-enthought-vp-trainingPresenter: Dr. Michael Connell, VP, Enthought Training Solutions

Ed.D, Education, Harvard University
M.S., Electrical Engineering and Computer Science, MIT


 

Considering Moving to Python for Data Science?

Then Enthought’s Python for Data Science training course is definitely for you! This class has been particularly appealing to people who have been using other tools like R or SAS (or even Excel) for their data science work and want to start applying their analytic skills using the Python toolset.  And it’s no wonder — Python has been identified as the most popular coding language for five years in a row for good reason.

One reason for Python’s broad popularity across a range of disciplines is its efficiency and ease-of-use. Many people consider Python more fun to work in than other languages (and we agree!). Another reason for its popularity among data analysts and data scientists in particular is Python’s extensive (and growing) open source library of powerful tools for preparing, visualizing, analyzing, and modeling data.

Python is also an extraordinarily comprehensive toolset – it supports everything from interactive analysis to automation to software engineering to web app development within a single language and plays very well with other languages like C/C++ or FORTRAN so you can continue leveraging your existing code libraries written in those other languages.

Many organizations are moving to Python so they can consolidate all of their technical work streams under a single comprehensive toolset. In the first part of this class we’ll give you the fundamentals you need to switch from another language to Python and then we cover the core tools that will enable you to do in Python what you were doing with other tools, only faster!

Share this article:

Related Content

Retuning the Heavens: Machine Learning and Ancient Astronomy

What can we learn about machine learning from ancient astronomy? When thinking about Machine Learning it is easy to be model-centric and get caught up…

Read More

Extracting Target Labels from Deep Learning Classification Models

In the blog post Configuring a Neural Network Output Layer we highlighted how to correctly set up an output layer for deep learning models. Here,…

Read More

Exploring Python Objects

Introduction When we teach our foundational Python class, one of the things we do is make sure that our students know how to explore Python…

Read More

Choosing the Right Number of Clusters

Introduction When I first started my machine learning journey, K-means clustering was one of the first algorithms I was introduced to – and it is…

Read More

Prospecting for Data on the Web

Introduction At Enthought we teach a lot of scientists and engineers about using Python and the ecosystem of scientific Python packages for processing, analyzing, and…

Read More

Configuring a Neural Network Output Layer

Introduction If you have used TensorFlow before, you know how easy it is to create a simple neural network model using the Keras API. Just…

Read More

No Zero Padding with strftime()

One of the best features of Python is that it is platform independent. You can write code on Linux, Windows, and MacOS and it works…

Read More

Got Data?

Introduction So, you have data and want to get started with machine learning. You’ve heard that machine learning will help you make sense of that…

Read More

Sorting Out .sort() and sorted()

Sorting Out .sort() and sorted() Sometimes sorting a Python list can make it mysteriously disappear.  This happens even to experienced Python programmers who use .sort()…

Read More

A Beginner’s Guide to Deep Learning

Deep learning. By this point, we’ve all heard of it. It’s the magic silver bullet that can fix any complex problem. It’s the special ingredient…

Read More

Join Our Mailing List!

Sign up below to receive email updates including the latest news, insights, and case studies from our team.