Unit 1: Programming refresher

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02: Programming Basics Running jupyter notebooks Dog Simulation
04: Python Data Types & Operators Data types, operators, files & error handling In-class demo
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05: Loops & conditionals If statements, for & while loops, range() function In-class demo
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06: Python functions Defining & calling functions, arguments, generators In-class demo
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Unit 2: Applied data science with Python

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09: NumPy Arrays & array operations, indexing, slicing In-class demo
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Extra NumPy practice
Extra NumPy practice (Solutions)
Extra NumPy slicing & indexing practice
Extra NumPy slicing & indexing practice (Solutions)
10: Pandas DataFrames, series, data manipulation In-class demo
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Strings & Sorting practice
Series practice
Datetime practice
DataFrame practice
INC1: Incremental capstone 1 Import and clean data Incremental capstone 1 solution
11: Data visualization Matplotlib, Seaborn In-class demo
Vocareum activity solution
12: Math and statistics fundamentals Linear algebra, statistics basics Feature space and model complexity demo
Statistical data types demo
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Anscombe's quartet demo
13: Probability distributions Discrete and continuous distributions, central limit theorem In class demo
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14: Advanced statistics Confidence intervals, hypothesis testing In class demo
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15: Data Wrangling Manipulating and cleaning data In class demo
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16: Feature Engineering Creating and transforming features In class demo
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Unit2_Competition: Unit 2 Competition Become Obsessive about a r2 AndrewT - (0.29638)

Unit 3: Machine learning

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19: Supervised learning regression Regression models: types, training, evaluation, regularization In-class demo
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20: Supervised learning classification Classification models: types, training, evaluation, regularization Demo
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