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
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
Presenter: 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!
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