When: March 30 - April 3, 10 AM - 4:30 PM
Where: Courtyard by Marriott, 2929 Westpark Dr, Houston, TX, 77005
General Admission: $1999
Get started with data science using Python with a hands-on, in-person course with expert instructor Ted Petrou.
Before the course
- Installation - You will be given detailed instructions on how to install Python onto your machine and set up an environment to run all the code during class.
- Jupyter Notebooks - We will be using the excellent Jupyter Notebook to run most of the code during class. It provides an interactive coding environment to quickly execute code, get feedback, and make notes.
- Assignment - You will be given an assignment on how to use Jupyter Notebooks and write Markdown that is expected to be completed before the start of the course.
- Private Slack Channel - Upon registration, you be given access to Ted's private Slack channel where you can directly communicate him and ask him questions about the course.
- All Access Pass ($199 value) - You will be granted the All Access Pass which provides you lifetime access to all current and future books and non-live courses. Currently, this includes over 1,000 pages of material, 500 exercises, and 20 hours of video.
- pandas DataFrames - The pandas library is a popular and powerful library to analyze data. We will learn about the DataFrame, the main container of data with lots of functionality to analyze data. To use the DataFrame effectively, you must be aware of its component - the index, columns, and values. We will learn commands that are used often when first reading in data into a pandas DataFrame.
- Selecting subsets of data - One of the most common and basic data analysis tasks is to select a certain subset of the data. This could be particular rows, columns, or both rows and columns. There are unfortunately many ways to select subsets of data with pandas. We will cover the best and most efficient ways to do so.
- Series operations - A single column of a pandas DataFrame may be extracted as a Series. This object is very similar to a DataFrame, with the vast majority of its attributes and methods overlapping. The simplest analysis we can perform involves operating on this single column of data. We learn how to call methods that aggregate, and return a single value, as well as those that do not aggregate and return more than a single value.
- String and Datetime methods - Columns containing strings or datetimes are processed very differently than numeric columns. We learn about specific accessors that provide us with special methods just for these types of data.
- Entire DataFrame operations - After understanding how to operate on a single column of data, we move to operations that involve multiple columns in a DataFrame. Operating on multiple columns opens up the possibility of changing the direction of the operation.
- Grouping - All the operations used above were applied to all values of a Series or DataFrame. You will learn how to split your data into groups based on the unique values of one or more columns. This 'grouping' allows you to run different calculations for independent groups within your data.
- Pivot tables - Grouping data often results in a summary that is difficult for humans to read. You will learn how to create pivot tables to present your data in a format that is easier to interpret.
- Regular expressions -You will learn how to find patterns within text by learning regular expressions. This will allow you to extract information from text data.
- Tidy data - Real-world data is messy and not immediately available for aggregation, visualization or machine learning. Identifying messy data and transforming it into tidy data provides a structure to data for making further analysis easier.
- Visualization - Data visualization is one of the most effective ways to present the findings of an analysis. You will learn how to produce informative visualizations with pandas.
- Create your own data analysis - As a capstone project, you will use all of the skills gained to produce your own data analysis on a real-word dataset.
After the course
- Certificate of completion - Upon conclusion of the course, you will be given a direct path towards mastering the fundamentals of data science using Python. If you go on to complete all of the tasks on this path, you will receive a certificate of completion.
- Lifetime access to material - You will always have access to the material and any updates to it after the course has completed. Ted upgrades and adds to his material on a regular basis.
- Lifetime Slack access - You will have lifetime access to Slack allowing you to interact with Ted and all of the previous students.
The Intro to Data Science Bootcamp targets those who have little data science experience, but do understand the fundamentals of programming in Python. If you do not feel comfortable with basic Python, then this course is not for you. Consider taking the Intro to Python Bootcamp first which will give you all the skills needed to prepare for this course.
Class time is divided between live coding sessions delivered by Ted and hands-on practice exercises and projects that you complete. During the live coding, you are provided an outline of the topics that Ted will cover as a Jupyter Notebook allowing you to code right along with Ted, explore how the commands work, and ask questions.
During student exercises, you will be writing Python code to solve data science problems. Ted is always available to provide help directly at your seat and is constantly seeking out students who may be in need of extra support.
About the Instructor
This course is taught by Ted Petrou, an expert at Python, data exploration and machine learning. Ted is the author of the highly rated text Pandas Cookbook. Ted has taught hundreds of students Python and data science during in-person classroom settings. He sees first hand exactly where students struggle and continually upgrades his material to minimize these struggles by providing a simple and direct path forward.
Ted is one of the foremost authorities on using the pandas library to do data analysis with his blog posts totaling well over 1 million views. He is also a prolific contributor on Stack Overflow having answered over 400 questions. He is an enthusiastic instructor and dedicates his time to helping students at their desk during exercises to ensure understanding.
Ted holds a master's degree in statistics from Rice University and is the author of Exercise Python and Master Machine Learning with Python.
Part of the intrigue of an in-person class is the social interaction and camaraderie formed between yourself and the other students. This fellowship is mainly absent when taking an online class or watching a recorded video. Many friendships and potential career opportunities are formed between the students that attend Ted's classes.
And if you are looking for proof, just take a look at the immense joy radiating from the faces of the students in the picture above.
Refunds are available up to 30 days prior to the start of the event