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TeamTreehouse - Beginning Data Science (Track) [Thomas]

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TeamTreehouse - Beginning Data Science (Track) [Thomas] (Size: 3.5 GB)
  0.Importing Data.webm 9 MB
  0.Welcome.webm 39.8 MB
  01. Accessing an API with Python.webm 32.3 MB
  01. An Intelligent Spider.webm 19.9 MB
  01. Cleaning A Spreadsheet.webm 35.4 MB
  01. Complex Relationships.webm 18.7 MB
  01. Controlling Conversion.webm 19.7 MB
  01. Everyone Loves Charlotte.webm 27.9 MB
  01. Examples and Features.webm 12.8 MB
  01. Functions.webm 30.3 MB
  01. Indexing.webm 49 MB
  01. Installation and Creating Your First Notebook.webm 17.7 MB
  01. Installing scikit-learn using Anaconda.webm 18 MB
  01. Introducing Tuples.webm 7.8 MB
  01. Iterate over Dictionaries.webm 6.3 MB
  01. Iteration.webm 11 MB
  01. Lets Chat About Sequences.webm 11.6 MB
  01. Making Better Decisions with Data Analysis.webm 22.2 MB
  01. Moving Forward.webm 4.9 MB
  01. New Way of Thinking.webm 55.2 MB
  01. Numeric.webm 27.1 MB
  01. Our Data Set - Flower Power.webm 19.1 MB
  01. Packing.webm 13.5 MB
  01. Problem Discussion.webm 13.1 MB
  01. Project Breakdown.webm 17.1 MB
  01. Recap of Functions.webm 11.4 MB
  01. Sequence Operations.webm 7.8 MB
  01. Summarizing Data Maximum, Minimum, Range.webm 17.9 MB
  01. The Application.webm 18.6 MB
  01. The Project.webm 17.5 MB
  01. Understanding Metrics.webm 28.7 MB
  01. Welcome to Matplotlib.webm 13.2 MB
  01. Welcome.webm 19.2 MB
  01. What Are Objects And Classes.webm 30 MB
  01. What Is Cleaning Data.webm 16.7 MB
  01. What Problems Does Netflix Have.webm 29.5 MB
  01. What is Anaconda and why use it.webm 21.8 MB
  01. What is Data Scraping.webm 31 MB
  01. What is Machine Learning.webm 22.4 MB
  01. What is a dictionary.webm 15.6 MB
  01. Where is it Being Used.webm 14.1 MB
  02. Add Items.webm 10.1 MB
  02. Boolean Array Indexing.webm 45.4 MB
  02. Characteristics of Big Data.webm 10 MB
  02. Cleaning A Spreadsheet Part 2.webm 21.3 MB
  02. Comparing and Combining Dice.webm 17.7 MB
  02. Context.webm 17.3 MB
  02. Creation.webm 10.2 MB
  02. Data is Everywhere.webm 25.8 MB
  02. Decision Process.webm 20.4 MB
  02. Defining Terms.webm 20.4 MB
  02. Defining a Function.webm 3.9 MB
  02. Dictionary Syntax and KeyValue Pairs.webm 12.2 MB
  02. Functions Recap and Cheat Sheet.md 1.4 KB
  02. Gather Information.webm 12.6 MB
  02. Gathering Weather Data.webm 8.3 MB
  02. Getting Setup.webm 17.5 MB
  02. Getting Started with Charting.webm 10.2 MB
  02. How Does Netflix Apply Big Data Tools to Solve these Problems.webm 17.4 MB
  02. Installing Anaconda.webm 17.7 MB
  02. Installing Scrapy.webm 11.9 MB
  02. Iterating with Basic For Loops.webm 8.7 MB
  02. Labels and Classifiers.webm 10.9 MB
  02. Lets Make a Class!.webm 7.6 MB
  02. Loading a Dataset.webm 14.4 MB
  02. Math.webm 13.9 MB
  02. Mutability.webm 14.5 MB
  02. Packing with Dictionaries.webm 6.2 MB
  02. Packing, a Practical Example.webm 5.4 MB
  02. Recap.webm 9 MB
  02. Returning Values.webm 24.8 MB
  02. Running Code in Cells.webm 12.4 MB
  02. Running Scripts.webm 14.2 MB
  02. Scatter Plot.webm 17.2 MB
  02. Scraping APIs.webm 22.9 MB
  02. Slices.webm 7 MB
  02. Strings and Operators.webm 14 MB
  02. Summarizing Data Mean, Median, Mode.webm 9.3 MB
  02. Super-Duper!.webm 16.8 MB
  02. Supervised and Unsupervised Learning.webm 29.4 MB
  02. Types of Data.webm 22.4 MB
  02. Universal Functions.webm 42.9 MB
  02. Web Page Anatomy.webm 18.8 MB
  03. Accessing Keys and Values.webm 6.3 MB
  03. Addition.webm 13.3 MB
  03. All About Returns.webm 8.2 MB
  03. Analyzing Data Spread.webm 9.5 MB
  03. Analyzing the Data.webm 14.4 MB
  03. Bad Data Types.webm 17.5 MB
  03. Beautiful Soup.webm 27.2 MB
  03. Branch and Loop.webm 19.3 MB
  03. Calling a Function.webm 3.1 MB
  03. Calling the API.webm 25.5 MB
  03. Cleaning A CSV.webm 22.8 MB
  03. Crawling Spiders.webm 19.6 MB
  03. Creating a Spreadsheet.webm 17.9 MB
  03. Display the List.webm 13.9 MB
  03. Domain Data Storage.webm 34.6 MB
  03. Emulating Built-ins.webm 24.2 MB
  03. Expecting Exceptions.webm 18.6 MB
  03. Giving a Hand.webm 19.7 MB
  03. Graphs and Charts.webm 25.3 MB
  03. Histogram.webm 18 MB
  03. Introducing Arrays.webm 38.5 MB
  03. Iterating with Enumerate.webm 7.1 MB
  03. Len, Min, and Max.webm 4.4 MB
  03. Machine Learning Frameworks.webm 18.9 MB
  03. Making Predictions with a Classifier.webm 11.3 MB
  03. Methods.webm 14 MB
  03. Multiple Superclasses.webm 31.3 MB
  03. Problem Summary and Presentation.webm 8.9 MB
  03. Routines in Action.webm 38.5 MB
  03. Slicing.webm 28.2 MB
  03. Split and Join.webm 13.4 MB
  03. String Methods.webm 13.4 MB
  03. The Importance of Big Data.webm 22.8 MB
  03. The Legend of Charting.webm 10.8 MB
  03. The Python Shell.webm 20.5 MB
  03. Tuples vs. Lists.md 2.1 KB
  03. Unpacking with Dictionaries.md 1.2 KB
  03. Unpacking.webm 4.5 MB
  03. Using Scrapers for Site Testing.webm 22.2 MB
  03. Using conda to Install Packages.webm 9.4 MB
  03. Wrapping Up.webm 27.6 MB
  04. Arguments and Parameters.webm 6.7 MB
  04. Booleans.webm 17.6 MB
  04. Box Plot.webm 14.6 MB
  04. Chart Types & Reasons to Use.webm 18.9 MB
  04. Cleaning A CSV Part 2.webm 24.4 MB
  04. Common Issues with Data Scraping.md 1.7 KB
  04. Creating the Study Log.webm 29.1 MB
  04. Domain Computations.webm 29.3 MB
  04. Exploring Our New Problems.webm 41.3 MB
  04. Family Tree.webm 20.1 MB
  04. Getting Good Data is Hard.webm 37.6 MB
  04. Handle Exceptions.webm 20 MB
  04. Indexing.webm 31.6 MB
  04. Is Our Data Normal.webm 10.7 MB
  04. Iterating with Ranges.webm 6.4 MB
  04. Lets Talk About Scope.webm 11 MB
  04. Machine Learning Review.webm 7.8 MB
  04. Manipulation.webm 43.5 MB
  04. Membership Testing.webm 4.1 MB
  04. Method Arguments.webm 13.5 MB
  04. More Soup in the Tureen.webm 23.6 MB
  04. Multidimensional Lists.webm 14.2 MB
  04. Other Languages.webm 20.2 MB
  04. Plotting.webm 38.7 MB
  04. Presenting Your Findings.webm 30.4 MB
  04. Raising Exceptions.webm 16 MB
  04. Saving the Data.webm 22.3 MB
  04. Subclassing Built-ins.webm 28.5 MB
  04. Syntax and Errors.webm 23.1 MB
  04. The Endless Web.webm 38.4 MB
  04. Tuple Syntax.md 2.6 KB
  04. Unpacking, a Practical Example.webm 5 MB
  04. Update and Mutate Dictionaries.webm 6.3 MB
  04. Wrap Up.webm 6.1 MB
  04. Yatzy Scoring.webm 14.1 MB
  04. miniconda.webm 20.4 MB
  05. Being a Good Citizen.webm 31.2 MB
  05. Cleaner Code Through Refactoring.webm 21.1 MB
  05. Cleaning A CSV Part 3.webm 24 MB
  05. Constructicons.webm 19.3 MB
  05. Count and Index.webm 6.9 MB
  05. Deletion.webm 14.6 MB
  05. Design.webm 31.5 MB
  05. Domain Infrastructure.webm 17.5 MB
  05. Function Gotchas.md 1.6 KB
  05. If, Else and Elif.webm 22.9 MB
  05. Multidimensional Arrays.webm 35.3 MB
  05. No Problem.webm 17.9 MB
  05. Saving Your Work.webm 12 MB
  05. Variables.webm 20.3 MB
  05. Visualizing Data.webm 16 MB
  05. Where to Now.webm 15.7 MB
  05. While Loops.webm 24.6 MB
  05. Wrapping Up.webm 7.3 MB
  06. Charting Our Data Part 1.webm 9.1 MB
  06. Cleaning A CSV Part 4.webm 23.7 MB
  06. Code Challenges.md 921.6 B
  06. Code Samples Membership Testing, Count, and Index.md 1.2 KB
  06. Comparisons.webm 25.4 MB
  06. For Loops.webm 11.2 MB
  06. Multiple Arguments and Parameters.webm 6 MB
  06. Special Methods.webm 19.9 MB
  06. Wrapping Up.webm 18.8 MB
  07. Charting Our Data Part 2.webm 12.6 MB
  07. Concatenation and Multiplication.webm 4.4 MB
  07. Input and Coding Style.webm 25.8 MB
  08. Sequence Operations Cheat Sheet.md 2.7 KB
  1.Manipulation.webm 5.5 MB
  1.Meet Series.webm 12 MB
  2.Combining DataFrames.webm 8.9 MB
  2.Vectorization and Broadcasting Review.webm 13.7 MB
  3.Meet DataFrames.webm 11.6 MB
  3.Until Next Time.webm 13.7 MB
  4.Onwards.webm 4.8 MB
  About This Course.png 300.7 KB
  Accessing a DataFrame.png 718 KB
  Accessing a Series.png 680 KB
  Beginning Data Science.md 6.6 KB
  Combining DataFrames.png 1.2 MB
  Common Issues with Data Scraping.md 1.7 KB
  Creating a DataFrame.png 465.1 KB
  Creating a Series.png 511.5 KB
  Data Analysis Basics.md 3.9 KB
  Data from APIs.md 1 KB
  Exploration Methods.png 1.2 MB
  Functions, Packing, and Unpacking.md 4.2 KB
  Grouping.png 945.9 KB
  Handling Duplicated and Missing Data.png 863 KB
  Introducing Dictionaries.md 2.9 KB
  Introducing Lists.md 3.5 KB
  Introducing Tuples.md 1.8 KB
  Introduction to Anaconda.md 1.1 KB
  Introduction to Big Data.md 4.2 KB
  Introduction to Data Visualization with Matplotlib.md 4.5 KB
  Introduction to NumPy.md 4.4 KB
  Jupyter Notebooks.md 1.2 KB
  Learning SQL.md 512 B
  ML-machine-learning-basics.zip 614.4 B
  Machine Learning Basics.md 3.4 KB
  Manipulating Text.png 725.4 KB
  Manipulation Techniques.png 1.2 MB
  More Visualization.md 1 KB
  Object-Oriented Python.md 7.1 KB
  Object-Oriented+Python+2.zip 130.1 KB
  Optional Challenge #1 - Top Referrers.png 394 KB
  Optional Challenge #2 - Update Users.png 399.4 KB
  Optional Challenge #3 - Verified Email List.png 419.3 KB
  Preparing Data for Analysis.md 2.3 KB
  Python Basics.md 5.6 KB
  Python Sequences.md 3.5 KB
  README.txt 2.2 KB
  Scraping Data From the Web.md 4.1 KB
  Selecting Data.png 751.6 KB
  Series Vectorization and Broadcasting.png 553.4 KB
  TeamTreehouse - Beginning Data Science (Track) [Thomas].jpg?042148 157.3 KB
  TeamTreehouse - Beginning Data Science (Track) [Thomas].png 1 MB
  intro_matplotlib.zip 265.6 KB
  marathon_results_2017.csv 4 MB
  preparing-data-for-analysis-student.zip 38 KB
  python-intro-to-numpy.zip 57.7 KB
  python-introducing-pandas-1.2.0.zip 88.3 KB
  scraping_data_from_the_web.zip 37.3 KB
  ▲ 258 total files

Description


TeamTreehouse - Beginning Data Science (Track) [Thomas]








Course included
├── 01. Data Analysis Basics
├── 02. Python Basics
├── 03. Introducing Lists
├── 04. Introducing Dictionaries
├── 05. Python Sequences
├── 06. Functions, Packing, and Unpacking
├── 07. Introducing Tuples
├── 08. Object-Oriented Python
├── 09. Learning SQL
├── 10. Introduction to Anaconda
├── 11. Jupyter Notebooks
├── 12. Introduction to NumPy
├── 13. Introduction to pandas
├── 14. Preparing Data for Analysis
├── 15. Introduction to Data Visualization with Matplotlib
├── 16. More Visualization
├── 17. Scraping Data From the Web
├── 18. Data from APIs
├── 19. Introduction to Big Data
├── 20. Machine Learning Basics
├── Beginning Data Science.md
├── Common Issues with Data Scraping
├── README.txt
├── TeamTreehouse - Beginning Data Science (Track) [Thomas].jpg?042148
└── TeamTreehouse - Beginning Data Science (Track) [Thomas].png


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