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0.Importing Data.webm
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WEBM
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9 MB
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0.Welcome.webm
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WEBM
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39.8 MB
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01. Accessing an API with Python.webm
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WEBM
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32.3 MB
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01. An Intelligent Spider.webm
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WEBM
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19.9 MB
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01. Cleaning A Spreadsheet.webm
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WEBM
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35.4 MB
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01. Complex Relationships.webm
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WEBM
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18.7 MB
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01. Controlling Conversion.webm
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WEBM
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19.7 MB
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01. Everyone Loves Charlotte.webm
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WEBM
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27.9 MB
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01. Examples and Features.webm
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WEBM
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12.8 MB
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01. Functions.webm
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WEBM
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30.3 MB
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01. Indexing.webm
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WEBM
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49 MB
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01. Installation and Creating Your First Notebook.webm
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WEBM
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17.7 MB
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01. Installing scikit-learn using Anaconda.webm
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WEBM
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18 MB
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01. Introducing Tuples.webm
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WEBM
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7.8 MB
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01. Iterate over Dictionaries.webm
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WEBM
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6.3 MB
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01. Iteration.webm
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WEBM
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11 MB
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01. Lets Chat About Sequences.webm
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WEBM
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11.6 MB
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01. Making Better Decisions with Data Analysis.webm
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WEBM
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22.2 MB
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01. Moving Forward.webm
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WEBM
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4.9 MB
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01. New Way of Thinking.webm
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WEBM
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55.2 MB
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01. Numeric.webm
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WEBM
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27.1 MB
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01. Our Data Set - Flower Power.webm
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WEBM
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19.1 MB
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01. Packing.webm
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WEBM
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13.5 MB
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01. Problem Discussion.webm
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WEBM
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13.1 MB
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01. Project Breakdown.webm
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WEBM
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17.1 MB
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01. Recap of Functions.webm
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WEBM
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11.4 MB
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01. Sequence Operations.webm
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WEBM
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7.8 MB
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01. Summarizing Data Maximum, Minimum, Range.webm
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WEBM
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17.9 MB
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01. The Application.webm
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WEBM
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18.6 MB
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01. The Project.webm
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WEBM
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17.5 MB
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01. Understanding Metrics.webm
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WEBM
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28.7 MB
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01. Welcome to Matplotlib.webm
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WEBM
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13.2 MB
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01. Welcome.webm
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WEBM
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19.2 MB
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01. What Are Objects And Classes.webm
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WEBM
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30 MB
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01. What Is Cleaning Data.webm
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WEBM
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16.7 MB
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01. What Problems Does Netflix Have.webm
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WEBM
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29.5 MB
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01. What is Anaconda and why use it.webm
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WEBM
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21.8 MB
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01. What is Data Scraping.webm
|
WEBM
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31 MB
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01. What is Machine Learning.webm
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WEBM
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22.4 MB
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01. What is a dictionary.webm
|
WEBM
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15.6 MB
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01. Where is it Being Used.webm
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WEBM
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14.1 MB
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02. Add Items.webm
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WEBM
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10.1 MB
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02. Boolean Array Indexing.webm
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WEBM
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45.4 MB
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02. Characteristics of Big Data.webm
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WEBM
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10 MB
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02. Cleaning A Spreadsheet Part 2.webm
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WEBM
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21.3 MB
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02. Comparing and Combining Dice.webm
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WEBM
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17.7 MB
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02. Context.webm
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WEBM
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17.3 MB
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02. Creation.webm
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WEBM
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10.2 MB
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02. Data is Everywhere.webm
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WEBM
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25.8 MB
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02. Decision Process.webm
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WEBM
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20.4 MB
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02. Defining Terms.webm
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WEBM
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20.4 MB
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02. Defining a Function.webm
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WEBM
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3.9 MB
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02. Dictionary Syntax and KeyValue Pairs.webm
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WEBM
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12.2 MB
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02. Functions Recap and Cheat Sheet.md
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MD
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1.4 KB
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02. Gather Information.webm
|
WEBM
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12.6 MB
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02. Gathering Weather Data.webm
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WEBM
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8.3 MB
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02. Getting Setup.webm
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WEBM
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17.5 MB
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02. Getting Started with Charting.webm
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WEBM
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10.2 MB
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02. How Does Netflix Apply Big Data Tools to Solve these Problems.webm
|
WEBM
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17.4 MB
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02. Installing Anaconda.webm
|
WEBM
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17.7 MB
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02. Installing Scrapy.webm
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WEBM
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11.9 MB
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02. Iterating with Basic For Loops.webm
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WEBM
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8.7 MB
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02. Labels and Classifiers.webm
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WEBM
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10.9 MB
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02. Lets Make a Class!.webm
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WEBM
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7.6 MB
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02. Loading a Dataset.webm
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WEBM
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14.4 MB
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02. Math.webm
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WEBM
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13.9 MB
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02. Mutability.webm
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WEBM
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14.5 MB
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02. Packing with Dictionaries.webm
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WEBM
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6.2 MB
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02. Packing, a Practical Example.webm
|
WEBM
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5.4 MB
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02. Recap.webm
|
WEBM
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9 MB
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02. Returning Values.webm
|
WEBM
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24.8 MB
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02. Running Code in Cells.webm
|
WEBM
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12.4 MB
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02. Running Scripts.webm
|
WEBM
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14.2 MB
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02. Scatter Plot.webm
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WEBM
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17.2 MB
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02. Scraping APIs.webm
|
WEBM
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22.9 MB
|
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02. Slices.webm
|
WEBM
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7 MB
|
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02. Strings and Operators.webm
|
WEBM
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14 MB
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|
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02. Summarizing Data Mean, Median, Mode.webm
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WEBM
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9.3 MB
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02. Super-Duper!.webm
|
WEBM
|
16.8 MB
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02. Supervised and Unsupervised Learning.webm
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WEBM
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29.4 MB
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02. Types of Data.webm
|
WEBM
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22.4 MB
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02. Universal Functions.webm
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WEBM
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42.9 MB
|
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02. Web Page Anatomy.webm
|
WEBM
|
18.8 MB
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03. Accessing Keys and Values.webm
|
WEBM
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6.3 MB
|
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03. Addition.webm
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WEBM
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13.3 MB
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03. All About Returns.webm
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WEBM
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8.2 MB
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03. Analyzing Data Spread.webm
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WEBM
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9.5 MB
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03. Analyzing the Data.webm
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WEBM
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14.4 MB
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03. Bad Data Types.webm
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WEBM
|
17.5 MB
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03. Beautiful Soup.webm
|
WEBM
|
27.2 MB
|
|
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03. Branch and Loop.webm
|
WEBM
|
19.3 MB
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03. Calling a Function.webm
|
WEBM
|
3.1 MB
|
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03. Calling the API.webm
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WEBM
|
25.5 MB
|
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03. Cleaning A CSV.webm
|
WEBM
|
22.8 MB
|
|
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03. Crawling Spiders.webm
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WEBM
|
19.6 MB
|
|
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03. Creating a Spreadsheet.webm
|
WEBM
|
17.9 MB
|
|
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03. Display the List.webm
|
WEBM
|
13.9 MB
|
|
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03. Domain Data Storage.webm
|
WEBM
|
34.6 MB
|
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|
03. Emulating Built-ins.webm
|
WEBM
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24.2 MB
|
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03. Expecting Exceptions.webm
|
WEBM
|
18.6 MB
|
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|
03. Giving a Hand.webm
|
WEBM
|
19.7 MB
|
|
|
03. Graphs and Charts.webm
|
WEBM
|
25.3 MB
|
|
|
03. Histogram.webm
|
WEBM
|
18 MB
|
|
|
03. Introducing Arrays.webm
|
WEBM
|
38.5 MB
|
|
|
03. Iterating with Enumerate.webm
|
WEBM
|
7.1 MB
|
|
|
03. Len, Min, and Max.webm
|
WEBM
|
4.4 MB
|
|
|
03. Machine Learning Frameworks.webm
|
WEBM
|
18.9 MB
|
|
|
03. Making Predictions with a Classifier.webm
|
WEBM
|
11.3 MB
|
|
|
03. Methods.webm
|
WEBM
|
14 MB
|
|
|
03. Multiple Superclasses.webm
|
WEBM
|
31.3 MB
|
|
|
03. Problem Summary and Presentation.webm
|
WEBM
|
8.9 MB
|
|
|
03. Routines in Action.webm
|
WEBM
|
38.5 MB
|
|
|
03. Slicing.webm
|
WEBM
|
28.2 MB
|
|
|
03. Split and Join.webm
|
WEBM
|
13.4 MB
|
|
|
03. String Methods.webm
|
WEBM
|
13.4 MB
|
|
|
03. The Importance of Big Data.webm
|
WEBM
|
22.8 MB
|
|
|
03. The Legend of Charting.webm
|
WEBM
|
10.8 MB
|
|
|
03. The Python Shell.webm
|
WEBM
|
20.5 MB
|
|
|
03. Tuples vs. Lists.md
|
MD
|
2.1 KB
|
|
|
03. Unpacking with Dictionaries.md
|
MD
|
1.2 KB
|
|
|
03. Unpacking.webm
|
WEBM
|
4.5 MB
|
|
|
03. Using Scrapers for Site Testing.webm
|
WEBM
|
22.2 MB
|
|
|
03. Using conda to Install Packages.webm
|
WEBM
|
9.4 MB
|
|
|
03. Wrapping Up.webm
|
WEBM
|
27.6 MB
|
|
|
04. Arguments and Parameters.webm
|
WEBM
|
6.7 MB
|
|
|
04. Booleans.webm
|
WEBM
|
17.6 MB
|
|
|
04. Box Plot.webm
|
WEBM
|
14.6 MB
|
|
|
04. Chart Types & Reasons to Use.webm
|
WEBM
|
18.9 MB
|
|
|
04. Cleaning A CSV Part 2.webm
|
WEBM
|
24.4 MB
|
|
|
04. Common Issues with Data Scraping.md
|
MD
|
1.7 KB
|
|
|
04. Creating the Study Log.webm
|
WEBM
|
29.1 MB
|
|
|
04. Domain Computations.webm
|
WEBM
|
29.3 MB
|
|
|
04. Exploring Our New Problems.webm
|
WEBM
|
41.3 MB
|
|
|
04. Family Tree.webm
|
WEBM
|
20.1 MB
|
|
|
04. Getting Good Data is Hard.webm
|
WEBM
|
37.6 MB
|
|
|
04. Handle Exceptions.webm
|
WEBM
|
20 MB
|
|
|
04. Indexing.webm
|
WEBM
|
31.6 MB
|
|
|
04. Is Our Data Normal.webm
|
WEBM
|
10.7 MB
|
|
|
04. Iterating with Ranges.webm
|
WEBM
|
6.4 MB
|
|
|
04. Lets Talk About Scope.webm
|
WEBM
|
11 MB
|
|
|
04. Machine Learning Review.webm
|
WEBM
|
7.8 MB
|
|
|
04. Manipulation.webm
|
WEBM
|
43.5 MB
|
|
|
04. Membership Testing.webm
|
WEBM
|
4.1 MB
|
|
|
04. Method Arguments.webm
|
WEBM
|
13.5 MB
|
|
|
04. More Soup in the Tureen.webm
|
WEBM
|
23.6 MB
|
|
|
04. Multidimensional Lists.webm
|
WEBM
|
14.2 MB
|
|
|
04. Other Languages.webm
|
WEBM
|
20.2 MB
|
|
|
04. Plotting.webm
|
WEBM
|
38.7 MB
|
|
|
04. Presenting Your Findings.webm
|
WEBM
|
30.4 MB
|
|
|
04. Raising Exceptions.webm
|
WEBM
|
16 MB
|
|
|
04. Saving the Data.webm
|
WEBM
|
22.3 MB
|
|
|
04. Subclassing Built-ins.webm
|
WEBM
|
28.5 MB
|
|
|
04. Syntax and Errors.webm
|
WEBM
|
23.1 MB
|
|
|
04. The Endless Web.webm
|
WEBM
|
38.4 MB
|
|
|
04. Tuple Syntax.md
|
MD
|
2.6 KB
|
|
|
04. Unpacking, a Practical Example.webm
|
WEBM
|
5 MB
|
|
|
04. Update and Mutate Dictionaries.webm
|
WEBM
|
6.3 MB
|
|
|
04. Wrap Up.webm
|
WEBM
|
6.1 MB
|
|
|
04. Yatzy Scoring.webm
|
WEBM
|
14.1 MB
|
|
|
04. miniconda.webm
|
WEBM
|
20.4 MB
|
|
|
05. Being a Good Citizen.webm
|
WEBM
|
31.2 MB
|
|
|
05. Cleaner Code Through Refactoring.webm
|
WEBM
|
21.1 MB
|
|
|
05. Cleaning A CSV Part 3.webm
|
WEBM
|
24 MB
|
|
|
05. Constructicons.webm
|
WEBM
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19.3 MB
|
|
|
05. Count and Index.webm
|
WEBM
|
6.9 MB
|
|
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05. Deletion.webm
|
WEBM
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14.6 MB
|
|
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05. Design.webm
|
WEBM
|
31.5 MB
|
|
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05. Domain Infrastructure.webm
|
WEBM
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17.5 MB
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|
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05. Function Gotchas.md
|
MD
|
1.6 KB
|
|
|
05. If, Else and Elif.webm
|
WEBM
|
22.9 MB
|
|
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05. Multidimensional Arrays.webm
|
WEBM
|
35.3 MB
|
|
|
05. No Problem.webm
|
WEBM
|
17.9 MB
|
|
|
05. Saving Your Work.webm
|
WEBM
|
12 MB
|
|
|
05. Variables.webm
|
WEBM
|
20.3 MB
|
|
|
05. Visualizing Data.webm
|
WEBM
|
16 MB
|
|
|
05. Where to Now.webm
|
WEBM
|
15.7 MB
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|
|
05. While Loops.webm
|
WEBM
|
24.6 MB
|
|
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05. Wrapping Up.webm
|
WEBM
|
7.3 MB
|
|
|
06. Charting Our Data Part 1.webm
|
WEBM
|
9.1 MB
|
|
|
06. Cleaning A CSV Part 4.webm
|
WEBM
|
23.7 MB
|
|
|
06. Code Challenges.md
|
MD
|
921.6 B
|
|
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06. Code Samples Membership Testing, Count, and Index.md
|
MD
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1.2 KB
|
|
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06. Comparisons.webm
|
WEBM
|
25.4 MB
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|
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06. For Loops.webm
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WEBM
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11.2 MB
|
|
|
06. Multiple Arguments and Parameters.webm
|
WEBM
|
6 MB
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06. Special Methods.webm
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WEBM
|
19.9 MB
|
|
|
06. Wrapping Up.webm
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WEBM
|
18.8 MB
|
|
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07. Charting Our Data Part 2.webm
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WEBM
|
12.6 MB
|
|
|
07. Concatenation and Multiplication.webm
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WEBM
|
4.4 MB
|
|
|
07. Input and Coding Style.webm
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WEBM
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25.8 MB
|
|
|
08. Sequence Operations Cheat Sheet.md
|
MD
|
2.7 KB
|
|
|
1.Manipulation.webm
|
WEBM
|
5.5 MB
|
|
|
1.Meet Series.webm
|
WEBM
|
12 MB
|
|
|
2.Combining DataFrames.webm
|
WEBM
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8.9 MB
|
|
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2.Vectorization and Broadcasting Review.webm
|
WEBM
|
13.7 MB
|
|
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3.Meet DataFrames.webm
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WEBM
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11.6 MB
|
|
|
3.Until Next Time.webm
|
WEBM
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13.7 MB
|
|
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4.Onwards.webm
|
WEBM
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4.8 MB
|
|
|
About This Course.png
|
PNG
|
300.7 KB
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Accessing a DataFrame.png
|
PNG
|
718 KB
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Accessing a Series.png
|
PNG
|
680 KB
|
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Beginning Data Science.md
|
MD
|
6.6 KB
|
|
|
Combining DataFrames.png
|
PNG
|
1.2 MB
|
|
|
Common Issues with Data Scraping.md
|
MD
|
1.7 KB
|
|
|
Creating a DataFrame.png
|
PNG
|
465.1 KB
|
|
|
Creating a Series.png
|
PNG
|
511.5 KB
|
|
|
Data Analysis Basics.md
|
MD
|
3.9 KB
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|
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Data from APIs.md
|
MD
|
1 KB
|
|
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Exploration Methods.png
|
PNG
|
1.2 MB
|
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Functions, Packing, and Unpacking.md
|
MD
|
4.2 KB
|
|
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Grouping.png
|
PNG
|
945.9 KB
|
|
|
Handling Duplicated and Missing Data.png
|
PNG
|
863 KB
|
|
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Introducing Dictionaries.md
|
MD
|
2.9 KB
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|
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Introducing Lists.md
|
MD
|
3.5 KB
|
|
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Introducing Tuples.md
|
MD
|
1.8 KB
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|
|
Introduction to Anaconda.md
|
MD
|
1.1 KB
|
|
|
Introduction to Big Data.md
|
MD
|
4.2 KB
|
|
|
Introduction to Data Visualization with Matplotlib.md
|
MD
|
4.5 KB
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|
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Introduction to NumPy.md
|
MD
|
4.4 KB
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|
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Jupyter Notebooks.md
|
MD
|
1.2 KB
|
|
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Learning SQL.md
|
MD
|
512 B
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ML-machine-learning-basics.zip
|
ZIP
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614.4 B
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Machine Learning Basics.md
|
MD
|
3.4 KB
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Manipulating Text.png
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PNG
|
725.4 KB
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Manipulation Techniques.png
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PNG
|
1.2 MB
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|
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More Visualization.md
|
MD
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1 KB
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Object-Oriented Python.md
|
MD
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7.1 KB
|
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Object-Oriented+Python+2.zip
|
ZIP
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130.1 KB
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Optional Challenge #1 - Top Referrers.png
|
PNG
|
394 KB
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Optional Challenge #2 - Update Users.png
|
PNG
|
399.4 KB
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Optional Challenge #3 - Verified Email List.png
|
PNG
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419.3 KB
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Preparing Data for Analysis.md
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Python Basics.md
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Python Sequences.md
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README.txt
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Scraping Data From the Web.md
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Selecting Data.png
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Series Vectorization and Broadcasting.png
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TeamTreehouse - Beginning Data Science (Track) [Thomas].jpg
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TeamTreehouse - Beginning Data Science (Track) [Thomas].png
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intro_matplotlib.zip
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marathon_results_2017.csv
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preparing-data-for-analysis-student.zip
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python-intro-to-numpy.zip
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python-introducing-pandas-1.2.0.zip
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scraping_data_from_the_web.zip
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![TeamTreehouse - Beginning Data Science (Track) [Thomas]](/static/img/no-torrent-image.png "TeamTreehouse - Beginning Data Science (Track) [Thomas]")
