About This Course

This fast-paced class is intended for practicing data scientists, data analysts, and business intelligence experts interested in using Python for their day-to-day work. The primary focus is on learning to use Python tools for data science, data analysis, and machine learning efficiently and effectively.


Course Overview

Participants in this course will take away:

  • Hands-on experience setting up an integrated analysis environment for doing data science with Python.
  • An understanding of how to use the Python standard library to write programs, access the various data science tools, and document and automate analytic processes.
  • Orientation to some of the most powerful and popular Python libraries for data science including Pandas (data preparation, analysis, and modeling; time series analysis), scikit-learn (machine learning), Matplotlib and Seaborn (data visualization).
  • Working knowledge of the Python tools ideally suited for data science tasks, including:
    1. Accessing data (e.g., text files, databases)
    2. Cleansing and normalizing data
    3. Exploring data (e.g., simple statistics, correlation matrices, visualization)
    4. Modeling data (e.g., machine learning)

Contact Us

Questions or need help registering? Call us at 512.536.1057 or fill out the form:

Course Syllabus & Topics

Course Prerequisites

The course assumes a working knowledge of key data science topics (statistics, machine learning, and general data analytic methods). Programming experience in some language (such as R, MATLAB, SAS, Mathematica, Java, C, C++, VB, or FORTRAN) is expected. In particular, participants need to be comfortable with general programming concepts like variables, loops, and functions. Experience with Python is helpful (but not required).

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Setting Up Your Integrated Analysis Environment & Tools Overview

  • IPython Shell
  • Custom environment settings
  • Jupyter Notebooks
  • Script editor
  • Packages: NumPy, SciPy, scikit-learn, Pandas, Matplotlib, Seaborn, etc.

Once you complete this module, you will understand some of the unique benefits of using Python for data science / what features make Python particularly well-suited for data science, you will be able to set up a fully functioning Python-based analysis environment, and you will know what each tool is used for in the data science workflow.

Python Essentials

  • Data types and objects
  • Loading packages, namespaces
  • Reading and writing data
  • Simple plotting
  • Control flow
  • Debugging
  • Code profiling

Once you complete this module, you will be able to use the Python standard library plus Canopy tools to write, run, debug, and profile programs that control your data science processes (which draw on the scientific packages).

Acquiring Data with Python

  • Loading from CSV files
  • Accessing SQL databases

Cleansing Data with Python

  • Stripping out extraneous information
  • Normalizing data
  • Formatting data

Once you complete this module, you will know how to load data from common types of data sources, including structured text files and SQL databases. and you will know some of the common tools used in Python to cleanse and prepare your data for analysis.

NumPy Essentials

  • The NumPy array
  • N-dimensional array operations and manipulations
  • Memory mapped files

Data Visualization

  • 2D plotting with Matplotlib
  • Advanced data visualization with Seaborn

Once you complete this module, you will understand how to use NumPy arrays for efficient numerical processing and how to use NumPy methods such as slicing to write code that is both compact and easy to read and understand. You will know how to use Matplotlib, Seaborn, and NumPy together to explore and visualize your data.

Searching for Gold in a Pile of Pyrite

  • Data manipulation with Pandas
  • Statistical analysis with Pandas
  • Time series analysis with Pandas

At the end of this module, you will know how to access some of the core tools used for statistical analysis and data exploration in Python.

Predicting the Future Can Be Good for Business

  • Input: 2D, samples, and features
  • Estimator, predictor, transformer interfaces
  • Pre-processing data
  • Regression
  • Classification
  • Model selection

At the end of this module you will have a working understanding of what machine learning tools are available in scikit-learn and how to use them.

Open Class Schedule

Onsite corporate classes are also available. Discounts are available for 3 or more attendees and academics currently at a degree-granting institution. Contact us with the form to the right to learn more.

There are no classes scheduled at this time. To request one, please contact us using the form to the right.

Contact Us

Questions or need help registering? Call us at 512.536.1057 or fill out the form:


What’s the difference between Enthought’s Python for Data Science, Python for Machine Learning and the Machine Learning Mastery Workshop?

Python for Machine Learning and Python for Data Science are both five-day classes designed to introduce Python, NumPy, Pandas, Matplotlib, seaborn and scikit-learn.

Where they differ:

Python for Data Science is five days and includes database access and is focused on machine learning algorithms. It is better suited for people who already know machine learning and want to learn Python. One previous attendee called it “the most concise data science primer you can find.”

Python for Machine Learning is also five days, includes image processing and is focused on feature engineering. It is better suited for people new to machine learning.

The Machine Learning Mastery Workshop is a three-day long workshop-style course for Python programmers already proficient with NumPy and Pandas, which means that it largely consists of guided, hands-on practice applying machine learning algorithms to real data. As opposed to a primer, the Mastery Workshop is more of a “deep-dive.”

What are the prerequisites for this course?

The course assumes a working knowledge of key data science topics (statistics, machine learning, and general data analytic methods). Programming experience in some language (such as R, MATLAB, SAS, Mathematica, Java, C, C++, VB, or FORTRAN) is expected. In particular, participants need to be comfortable with general programming concepts like variables, loops, and functions. Experience with Python is helpful (but not required).

I am worried that your training is only useful to people who are committed to using Enthought software products. How much of your training is usable without Enthought software?

100%. Our training teaches students how to write software with Python and solve problems using its scientific packages, not how to use proprietary software. Everything you will learn uses free and open source software. We provide Enthought Canopy (our integrated analysis environment and Python distribution) to training participants to ensure they have all of the tools and Python packages they need to complete the training and that the tools are as easy as possible to install. While participants sometimes do use other editors, package managers, and Python distributions, we strongly recommend participants use Canopy during the training. With Canopy we can ensure that you can easily install everything you need for the course out of the box and we can provide technical support (which we unfortunately cannot provide for other tool sets).

I use / will be using Anaconda Python. Will I still benefit from this course?

Absolutely. Our training materials work with any Python distribution (such as Anaconda), as long as you also have all of the necessary packages, a text or code editor, package manager, interactive IPython shell, and Jupyter notebooks installed.

Is a class completion certificate provided?

Yes, a class completion certificate is provided for the Python for Data Science class.

Have a question that isn’t answered here? Contact us or call 512.536.1057.


Working in data science for several years (but having no experience with Python), this course has made the transition to Python seamless and easy. The course has all the content you need to get ramped up on using Python for any existing analytics work you may have.

Data Science Consultant

High marks for you, Mike. We were all impressed by your teaching style and knowledge of python.

Python for Data Science Attendee

Everyone in the class is very impressed by our instructor. He knows Python inside out. He encourages questions and answer our questions thoroughly. He gave us a lot to think about, such as, things going on behind the Python syntax, pointers on how to write fast Python. He really made this class worthwhile, way above learning from examples that is what I usually do.


You could tell from the demos, examples and exercises that this course was designed and taught by someone who has first hand experience of using the tools on real world and real life data.


The class was delivered effectively,whilst achieving the right balance between demo and exercises. The instuctor is very good and shares his knowledge at a good pace.

Mide S.

A very insightful course which delivered by a true expert. I have left the course with hundreds of ideas upon which I can now act.

Neal M.

Excellent, important, succinct way to build a knowledge base in Python, critical to helping me develop the skill to successfully implement my ideas in new ways. and the skill to do so successfully.

Tyler C.

Alex was very clear and concise, as well as extremely knowledgable on the topics covered The course structure was ideal for learning a language

Python for Data Science Attendee

Great course, python is demystified in less than a week. I'm ready to apply it to my projects.

Javier P.

This is a great overview of useful concepts that are available for data analysis and covering a broad base of tools and machine learning packages.

Timothy M.

Learning from a professor who knows so much about both the internals of Python as a developer himself and about math and numbers was exceptional. Instructors knowledge and passion for the material was exceptional.


Enthought provided really high-quality training on data science aspects of Python. I came out of the training with fluency in my ability to run regression models using various algorithms.

Dhaval B.

The course instructor was a Python core developer so knew the language inside out. He was able to answer detailed questions we had on the mechanics of Python, or at least know exactly where we could find the right information.

S. Harrigan

I was very impressed with the instructor. Great mastery of the subject matter and a good communicator. He was very enthusiastic about the subject, and cared that we were 'getting it.'

Senior Engineer

Jonathan and Alex are extremely helpful and understand python very well! Thank you!

Andrew C.

Excellent class, best introduction into Python and Data Analysis that I have seen.

Wolf M.

Excellently taught course. Coming from a background of R and SAS, I finally understand Python beyond the basics and have necessary tools to really harness its power. This wouldn't have been possible without the instructor's deep knowledge, and the patience and willingness to share it!


Mark is an excellent instructor who conveyed his enthusiasm for the subject and dealt adeptly with the various levels of experience in the class.

Steven W.

Great course to assess the usefulness of Python for data science. Gives you a lot of pointers and saves you time. There are things you can not learn so quickly on your own: data science for Python is one of them. Learned a lot.

Python for Data Science Attendee

The Lecturer, Dr. Dickinson, was excellent - His demonstration of his practical and historical knowledge of the python language combined with mathematics was invaluable. He provided tools and a road map on how to go about working with the language

Balvinder V.

Jonathan Rocher is an exceptional teacher (having spent fourteen years in higher education, I have some basis for comparison). Teachers who can explain complex ideas with as much clarity as he does aren't too common; teachers who are so closely attuned to when their students are following and when they are falling behind are rare birds indeed. Jonathan has an uncanny instinct for reading confusion in his students' faces. Together with his interactive, conversational teaching style—he's not afraid to stop the talkative from talking, and calling on the quiet to fold them into the conversation—this made for a class pitched and paced exactly for the specific group in the room. He did a remarkable job of keeping the more experienced students stretched with asides and additional challenges/exercises without losing the rest of us. I walked away with a deep understanding of python language fundamentals that had been mysterious and perplexing to me before.

Ben K.

As a Python beginner without a computer programming background, I found that after this 5 day course I was able to work effectively in Python. This class was incredibly helpful in providing a foundation in a variety of areas for Data Science. Enthought not only has a great product but fantastic training classes as well.

Holly N.

Dr. Rocher is a great teacher, who can explain complex topics (especially stats) in a very simple manner.

Python for Data Science Attendee

Really enjoyed the class. I had a varied level of knowledge of the different things that were covered. Those I knew well, I had holes filled in. Those I didn't know well, I learned quickly.

Vikram R.

The best part of the class was John Tyree. He was extremely knowledgeable about the mathematical techniques and scientific considerations of using the software rather than just the software itself. His advice and insight was very helpful.

Python for Data Science Attendee

I've taken two Enthought Python classes now. They are very effective and cover wide ranges of topics. If you are really committed and review materials you learned once more at night, the material you learned actually become very practical for your day-to-day work. Highly recommended.

H. Park

John Tyree's accessability/approachablity, his scientific literacy & ability to translate challenging concepts into "plain English."

Amy P.

Pietro is fantastic, and the course was well designed. It's clear that a lot of thought and prep was put into it. The pace of the class was what attracted me. It's hard to find a class that assumes that you know how to code, but that you don't know Python. I was looking for something to give me enough of a base in the language that when I do try to hack something together from Stack Exchange (as I had done some in the past), I had more of a logical understanding for how vars are treated in the language and why things might go awry. So this course was great. Pietro knows a ton, and it was clear that he had a deep knowledge of the language. He was great at bridging the gap-- explaining the most basic things as well as the detailed, specialised stuff. Exposure to the broad range of topics was nice as well.

Laura H.

The course and program were great. Right pace, right breadth of material to cover. Also, the instructor was very knowledgable and really did a good job of walking the fine line between providing enough support/guidance to us and letting us get ourselves into trouble before throwing us the life raft.

Aubra A.

I particularly liked the overview of Pandas, Visualisation and SciKit Learn (days 3-5). However, Corran was highly competent at explaining all topics which made the whole course highly useful and excellent!

Mark S.

As a novice to data science this course provided an excellent introduction to the tools and concepts available through Python for performing both rudimentary and complex analysis.

Rueben P.

This is probably the most concise Data Science primer course you can find; 100% worth it.

Python for Data Science Attendee

Learning the simplicity of machine learning techniques in python with sci-kit learn. The ease and ability to use pandas to slice and analyze data quickly as well as the overall structure of the python language.

Python for Data Science Attendee

Excellent Data Science Course for Beginners and Practitioners!

Computational Chemist

This is a great class for anybody, new to Data Science or experienced. The instructor Dr Dickinson. is a Math/Python genius so he will answer all questions and guide you through all the Python tricks and traps/gotchas. All questions are answered. Overall I left the course with enough knowledge/direction and materials to dive deep into Data Science on my own. Definitely worth the time and money. Just prepare to spend full day class training for a week

Samuel K.