Torrent Info
Title TeamTreehouse - Beginning Data Science (Track) [Thomas]
Category XXX
Size 3.51GB
Files List
Please note that this page does not hosts or makes available any of the listed filenames. You cannot download any of those files from here.
0.Importing Data.webm 9.01MB
0.Welcome.webm 39.76MB
01. Accessing an API with Python.webm 32.28MB
01. An Intelligent Spider.webm 19.85MB
01. Cleaning A Spreadsheet.webm 35.38MB
01. Complex Relationships.webm 18.73MB
01. Controlling Conversion.webm 19.70MB
01. Everyone Loves Charlotte.webm 27.89MB
01. Examples and Features.webm 12.81MB
01. Functions.webm 30.28MB
01. Indexing.webm 48.99MB
01. Installation and Creating Your First Notebook.webm 17.66MB
01. Installing scikit-learn using Anaconda.webm 18.02MB
01. Introducing Tuples.webm 7.81MB
01. Iterate over Dictionaries.webm 6.28MB
01. Iteration.webm 11.04MB
01. Lets Chat About Sequences.webm 11.60MB
01. Making Better Decisions with Data Analysis.webm 22.24MB
01. Moving Forward.webm 4.88MB
01. New Way of Thinking.webm 55.23MB
01. Numeric.webm 27.06MB
01. Our Data Set - Flower Power.webm 19.09MB
01. Packing.webm 13.49MB
01. Problem Discussion.webm 13.14MB
01. Project Breakdown.webm 17.06MB
01. Recap of Functions.webm 11.44MB
01. Sequence Operations.webm 7.81MB
01. Summarizing Data Maximum, Minimum, Range.webm 17.91MB
01. The Application.webm 18.62MB
01. The Project.webm 17.47MB
01. Understanding Metrics.webm 28.73MB
01. Welcome.webm 35.87MB
01. Welcome.webm 16.24MB
01. Welcome.webm 46.30MB
01. Welcome.webm 19.16MB
01. Welcome to Matplotlib.webm 13.19MB
01. What Are Objects And Classes.webm 29.96MB
01. What is a dictionary.webm 15.55MB
01. What is Anaconda and why use it.webm 21.77MB
01. What Is Cleaning Data.webm 16.69MB
01. What is Data Scraping.webm 31.01MB
01. What is Machine Learning.webm 22.40MB
01. What Problems Does Netflix Have.webm 29.49MB
01. Where is it Being Used.webm 14.11MB
02. Add Items.webm 10.15MB
02. Boolean Array Indexing.webm 45.36MB
02. Characteristics of Big Data.webm 10.04MB
02. Cleaning A Spreadsheet Part 2.webm 21.28MB
02. Comparing and Combining Dice.webm 17.75MB
02. Context.webm 17.35MB
02. Creation.webm 10.18MB
02. Data is Everywhere.webm 25.82MB
02. Decision Process.webm 20.41MB
02. Defining a Function.webm 3.93MB
02. Defining Terms.webm 20.44MB
02. Dictionary Syntax and KeyValue Pairs.webm 12.19MB
02. Functions Recap and Cheat Sheet.md 1.38KB
02. Gather Information.webm 12.63MB
02. Gathering Weather Data.webm 8.34MB
02. Getting Setup.webm 17.49MB
02. Getting Started with Charting.webm 10.19MB
02. How Does Netflix Apply Big Data Tools to Solve these Problems.webm 17.38MB
02. Installing Anaconda.webm 17.66MB
02. Installing Scrapy.webm 11.90MB
02. Iterating with Basic For Loops.webm 8.72MB
02. Labels and Classifiers.webm 10.90MB
02. Lets Make a Class!.webm 7.62MB
02. Loading a Dataset.webm 14.41MB
02. Math.webm 13.95MB
02. Mutability.webm 14.51MB
02. Packing, a Practical Example.webm 5.36MB
02. Packing with Dictionaries.webm 6.16MB
02. Recap.webm 8.99MB
02. Returning Values.webm 24.82MB
02. Running Code in Cells.webm 12.40MB
02. Running Scripts.webm 14.24MB
02. Scatter Plot.webm 17.23MB
02. Scraping APIs.webm 22.92MB
02. Slices.webm 6.99MB
02. Strings and Operators.webm 14.02MB
02. Summarizing Data Mean, Median, Mode.webm 9.26MB
02. Super-Duper!.webm 16.84MB
02. Supervised and Unsupervised Learning.webm 29.39MB
02. Types of Data.webm 22.40MB
02. Universal Functions.webm 42.94MB
02. Web Page Anatomy.webm 18.79MB
03. Accessing Keys and Values.webm 6.29MB
03. Addition.webm 13.26MB
03. All About Returns.webm 8.22MB
03. Analyzing Data Spread.webm 9.54MB
03. Analyzing the Data.webm 14.43MB
03. Bad Data Types.webm 17.52MB
03. Beautiful Soup.webm 27.18MB
03. Branch and Loop.webm 19.29MB
03. Calling a Function.webm 3.11MB
03. Calling the API.webm 25.47MB
03. Cleaning A CSV.webm 22.81MB
03. Crawling Spiders.webm 19.59MB
03. Creating a Spreadsheet.webm 17.91MB
03. Display the List.webm 13.87MB
03. Domain Data Storage.webm 34.58MB
03. Emulating Built-ins.webm 24.24MB
03. Expecting Exceptions.webm 18.57MB
03. Giving a Hand.webm 19.73MB
03. Graphs and Charts.webm 25.31MB
03. Histogram.webm 18.01MB
03. Introducing Arrays.webm 38.46MB
03. Iterating with Enumerate.webm 7.08MB
03. Len, Min, and Max.webm 4.42MB
03. Machine Learning Frameworks.webm 18.93MB
03. Making Predictions with a Classifier.webm 11.34MB
03. Methods.webm 14.04MB
03. Multiple Superclasses.webm 31.27MB
03. Problem Summary and Presentation.webm 8.88MB
03. Routines in Action.webm 38.46MB
03. Slicing.webm 28.20MB
03. Split and Join.webm 13.41MB
03. String Methods.webm 13.39MB
03. The Importance of Big Data.webm 22.77MB
03. The Legend of Charting.webm 10.76MB
03. The Python Shell.webm 20.53MB
03. Tuples vs. Lists.md 2.06KB
03. Unpacking.webm 4.50MB
03. Unpacking with Dictionaries.md 1.16KB
03. Using conda to Install Packages.webm 9.35MB
03. Using Scrapers for Site Testing.webm 22.21MB
03. Wrapping Up.webm 27.63MB
04. Arguments and Parameters.webm 6.65MB
04. Booleans.webm 17.58MB
04. Box Plot.webm 14.64MB
04. Chart Types & Reasons to Use.webm 18.92MB
04. Cleaning A CSV Part 2.webm 24.40MB
04. Common Issues with Data Scraping.md 1.67KB
04. Creating the Study Log.webm 29.11MB
04. Domain Computations.webm 29.31MB
04. Exploring Our New Problems.webm 41.33MB
04. Family Tree.webm 20.08MB
04. Getting Good Data is Hard.webm 37.57MB
04. Handle Exceptions.webm 19.96MB
04. Indexing.webm 31.57MB
04. Is Our Data Normal.webm 10.71MB
04. Iterating with Ranges.webm 6.38MB
04. Lets Talk About Scope.webm 10.96MB
04. Machine Learning Review.webm 7.79MB
04. Manipulation.webm 43.46MB
04. Membership Testing.webm 4.12MB
04. Method Arguments.webm 13.49MB
04. miniconda.webm 20.41MB
04. More Soup in the Tureen.webm 23.56MB
04. Multidimensional Lists.webm 14.17MB
04. Other Languages.webm 20.20MB
04. Plotting.webm 38.74MB
04. Presenting Your Findings.webm 30.37MB
04. Raising Exceptions.webm 15.95MB
04. Saving the Data.webm 22.30MB
04. Subclassing Built-ins.webm 28.47MB
04. Syntax and Errors.webm 23.06MB
04. The Endless Web.webm 38.37MB
04. Tuple Syntax.md 2.58KB
04. Unpacking, a Practical Example.webm 5.00MB
04. Update and Mutate Dictionaries.webm 6.29MB
04. Wrap Up.webm 6.09MB
04. Yatzy Scoring.webm 14.14MB
05. Being a Good Citizen.webm 31.17MB
05. Cleaner Code Through Refactoring.webm 21.10MB
05. Cleaning A CSV Part 3.webm 23.96MB
05. Constructicons.webm 19.30MB
05. Count and Index.webm 6.86MB
05. Deletion.webm 14.60MB
05. Design.webm 31.53MB
05. Domain Infrastructure.webm 17.49MB
05. Function Gotchas.md 1.56KB
05. If, Else and Elif.webm 22.88MB
05. Multidimensional Arrays.webm 35.33MB
05. No Problem.webm 17.91MB
05. Saving Your Work.webm 12.02MB
05. Variables.webm 20.28MB
05. Visualizing Data.webm 15.99MB
05. Where to Now.webm 15.72MB
05. While Loops.webm 24.61MB
05. Wrapping Up.webm 7.32MB
06. Charting Our Data Part 1.webm 9.12MB
06. Cleaning A CSV Part 4.webm 23.67MB
06. Code Challenges.md 908B
06. Code Samples Membership Testing, Count, and Index.md 1.19KB
06. Comparisons.webm 25.42MB
06. For Loops.webm 11.23MB
06. Multiple Arguments and Parameters.webm 5.99MB
06. Special Methods.webm 19.91MB
06. Wrapping Up.webm 18.82MB
07. Charting Our Data Part 2.webm 12.63MB
07. Concatenation and Multiplication.webm 4.42MB
07. Input and Coding Style.webm 25.83MB
08. Sequence Operations Cheat Sheet.md 2.70KB
1.Manipulation.webm 5.47MB
1.Meet Series.webm 11.99MB
2.Combining DataFrames.webm 8.95MB
2.Vectorization and Broadcasting Review.webm 13.69MB
3.Meet DataFrames.webm 11.60MB
3.Until Next Time.webm 13.71MB
4.Onwards.webm 4.77MB
About This Course.png 300.66KB
Accessing a DataFrame.png 718.01KB
Accessing a Series.png 680.00KB
Beginning Data Science.md 6.65KB
Combining DataFrames.png 1.21MB
Common Issues with Data Scraping.md 1.67KB
Creating a DataFrame.png 465.05KB
Creating a Series.png 511.47KB
Data Analysis Basics.md 3.91KB
Data from APIs.md 1.02KB
Exploration Methods.png 1.22MB
Functions, Packing, and Unpacking.md 4.17KB
Grouping.png 945.93KB
Handling Duplicated and Missing Data.png 862.95KB
intro_matplotlib.zip 265.59KB
intro_matplotlib.zip 265.59KB
intro_matplotlib.zip 265.59KB
Introducing Dictionaries.md 2.89KB
Introducing Lists.md 3.54KB
Introducing Tuples.md 1.83KB
Introduction to Anaconda.md 1.13KB
Introduction to Big Data.md 4.20KB
Introduction to Data Visualization with Matplotlib.md 4.51KB
Introduction to NumPy.md 4.41KB
Jupyter Notebooks.md 1.24KB
Learning SQL.md 495B
Machine Learning Basics.md 3.36KB
Manipulating Text.png 725.39KB
Manipulation Techniques.png 1.24MB
marathon_results_2017.csv 4.00MB
ML-machine-learning-basics.zip 614B
More Visualization.md 1.01KB
Object-Oriented+Python+2.zip 130.13KB
Object-Oriented+Python+2.zip 130.13KB
Object-Oriented+Python+2.zip 130.13KB
Object-Oriented+Python+2.zip 130.13KB
Object-Oriented Python.md 7.13KB
Optional Challenge #1 - Top Referrers.png 393.99KB
Optional Challenge #2 - Update Users.png 399.39KB
Optional Challenge #3 - Verified Email List.png 419.33KB
Preparing Data for Analysis.md 2.31KB
preparing-data-for-analysis-student.zip 38.00KB
Python Basics.md 5.63KB
python-introducing-pandas-1.2.0.zip 88.32KB
python-intro-to-numpy.zip 57.71KB
python-intro-to-numpy.zip 57.71KB
python-intro-to-numpy.zip 57.71KB
Python Sequences.md 3.48KB
README.txt 2.25KB
scraping_data_from_the_web.zip 37.26KB
scraping_data_from_the_web.zip 37.26KB
scraping_data_from_the_web.zip 37.26KB
Scraping Data From the Web.md 4.08KB
Selecting Data.png 751.58KB
Series Vectorization and Broadcasting.png 553.42KB
TeamTreehouse - Beginning Data Science (Track) [Thomas].jpg 157.26KB
TeamTreehouse - Beginning Data Science (Track) [Thomas].png 1.02MB
Distribution statistics by country
Russia (RU) 3
Brazil (BR) 1
India (IN) 1
Total 5
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