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January Live Online Intro to Data Science with Python

Dates: January 4 - 27, 2021

Class Times: Mondays and Wednesdays 8 - 10 p.m. EDT

General Admission: $399

Black Friday Special - Get 50% Off - Limited Time Offer!

Class Details

Get started learning data science using Python with a hands-on, online 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 with him and ask questions about the course.

Week 1

  • Basic types - Every 'value' in Python is an object and all objects have a type. We begin by learning how to create 'basic' types such as booleans, integers, and floats.
  • Operators - The simplest actions we can perform with these basic types make use of operators. Most operators are one or two non-alphanumeric symbols that use two values (arguments) to produce a new value. For example, the addition operator, +, is used to add two numbers. The result of an operator is always a new value (5 + 7 returns 12). You will learn about arithmetic, comparison, and boolean operators.
  • Variable names - We need to save the result of an operation in order to reuse it. In Python, we use assignment statements to assign this result to a variable name.
  • Strings - Strings are a more complex type of object, consisting of a sequence of characters. Strings are the first types of objects where we use methods to harness their power. We also learn about how to select subsets of strings using slice notation. 
  • Lists - Lists are sequences that may contain any number of other objects of any type. They are an important data structure used frequently in most Python programs and are mutable (able to be changed).

Week 2

  • Control flow - Normal flow of Python programs occurs by executing the very next line of code under the current line beginning at the top. We can change this normal flow with if/else statements and for/while loops. You will learn from practical examples, such as simulating a game of the casino game Craps.
  • Functions - Functions allow us to reuse an entire block of code by labeling it with a name. They help avoid repeating the same code over and over. You will learn how to use several built-in functions as well as how to create user-defined functions.  
  • Tuples, Sets, and Dictionaries - Python has several flexible built-in data structures that give us tremendous power to complete many tasks without the need to build them ourselves. Tuples, sets, and dictionaries are all different containers of data that we will explore.
  • Tic-Tac-Toe - As a capstone for the course, we will build a complete two-player Tic-Tac-Toe game using all of the previously covered topics. 

Week 3

  • 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. 

Week 4

  • Grouping - 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.

Target Student

The Intro to Data Science with Python class targets those who have little to no programming experience and would like a slow and thorough introduction to the most important fundamentals of data science using the Python programming language.

Interactive Class

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.

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 ensure student understanding.

Ted is the author of multiple Python libraries, including dexplot, bar_chart_race, jupyter_to_medium, and others.

Ted holds a master's degree in statistics from Rice University and is the author of Exercise Python, Master Data Analysis with Python, and Master Machine Learning with Python.