DatascienceTrackNotes_datacamp-

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Interactive exercises short videos.

All notes above are for the Datascience Track found under career track.

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Name		Name	Last commit message	Last commit date
Latest commit History 7 Commits
A-B testing statistical thinking.pdf		A-B testing statistical thinking.pdf
AdaBoost(ML).pdf		AdaBoost(ML).pdf
Adding time to the mix- working with dates and times.pdf		Adding time to the mix- working with dates and times.pdf
Basics of hierarchical clustering-cluster analysis.pdf		Basics of hierarchical clustering-cluster analysis.pdf
Basics of k-means clustering- cluster analysis.pdf		Basics of k-means clustering- cluster analysis.pdf
Case Study-School budgeting.pdf		Case Study-School budgeting.pdf
Categorical plot types(DV).pdf		Categorical plot types(DV).pdf
Changing plot style and color(DV).pdf		Changing plot style and color(DV).pdf
Comparing strings (cleaning data).pdf		Comparing strings (cleaning data).pdf
Count plots and bar plots(DV).pdf		Count plots and bar plots(DV).pdf
Data type constraints(cleaning Data).pdf		Data type constraints(cleaning Data).pdf
Dates in python.pdf		Dates in python.pdf
Decision-Tree for Classification(ML).pdf		Decision-Tree for Classification(ML).pdf
Decision-Tree for classification1(ML).pdf		Decision-Tree for classification1(ML).pdf
Do the genders commit different violations-analyzing police.pdf		Do the genders commit different violations-analyzing police.pdf
Docstrings- writing functions.pdf		Docstrings- writing functions.pdf
Does time of day affect arrest rate- analysing police.pdf		Does time of day affect arrest rate- analysing police.pdf
Explicit indexes - Data manipulation.pdf		Explicit indexes - Data manipulation.pdf
Exploring the weather dataset(analyzing police).pdf		Exploring the weather dataset(analyzing police).pdf
Filtering joins - joining data.pdf		Filtering joins - joining data.pdf
Finch beaks and the need for statistics-statistical thinking.pdf		Finch beaks and the need for statistics-statistical thinking.pdf
Formulating and simulating- statistical thinking.pdf		Formulating and simulating- statistical thinking.pdf
Functions as objects- writing functions.pdf		Functions as objects- writing functions.pdf
Generalization Error(ML).pdf		Generalization Error(ML).pdf
Generating bootstrap-statistical thinking.pdf		Generating bootstrap-statistical thinking.pdf
How good is your model(Supervised).pdf		How good is your model(Supervised).pdf
IMporting flat files from the web(Importing Data).pdf		IMporting flat files from the web(Importing Data).pdf
Inner join- joining data.pdf		Inner join- joining data.pdf
Intor to other file types - importing data.pdf		Intor to other file types - importing data.pdf
Intro to APIs and JSONS(intermediate importing data).pdf		Intro to APIs and JSONS(intermediate importing data).pdf
Intro to Data visualization.pdf		Intro to Data visualization.pdf
Intro to Exploratory Data analysis- statistical thinking.pdf		Intro to Exploratory Data analysis- statistical thinking.pdf
Intro to Seaborn(DV).pdf		Intro to Seaborn(DV).pdf
Intro to importing Data.pdf		Intro to importing Data.pdf
Intro to iterators.pdf		Intro to iterators.pdf
Intro to regression- supervised learning.pdf		Intro to regression- supervised learning.pdf
Intro to relational databases- importing data.pdf		Intro to relational databases- importing data.pdf
Intro to relational plots and subplots(DV).pdf		Intro to relational plots and subplots(DV).pdf
Intro to seaborn(Intermediate DV).pdf		Intro to seaborn(Intermediate DV).pdf
Intro to summary statistics - statistical thinking.pdf		Intro to summary statistics - statistical thinking.pdf
Left join- joining data.pdf		Left join- joining data.pdf
List comprehensions(Python Toolbox2).pdf		List comprehensions(Python Toolbox2).pdf
Membership constraints(cleaning data).pdf		Membership constraints(cleaning data).pdf
Otpimal parameters- statistical thinking.pdf		Otpimal parameters- statistical thinking.pdf
Plotting time-series data (DV).pdf		Plotting time-series data (DV).pdf
Preparing your figures to share with others (DV).pdf		Preparing your figures to share with others (DV).pdf
Probabilistic logic and statistical inference - statistical thinking.pdf		Probabilistic logic and statistical inference - statistical thinking.pdf
Quantitative comparisons(DV).pdf		Quantitative comparisons(DV).pdf
README.md		README.md
Reading date and time - working with dates.pdf		Reading date and time - working with dates.pdf
Standford Open policing project.pdf		Standford Open policing project.pdf
Supervised Learning.pdf		Supervised Learning.pdf
The twitter API and Authentication- importing data.pdf		The twitter API and Authentication- importing data.pdf
UTC offsets - working with dates and times.pdf		UTC offsets - working with dates and times.pdf
Uniformity(cleaning data).pdf		Uniformity(cleaning data).pdf
Unsupervised Learning.pdf		Unsupervised Learning.pdf
Unsupervised learning-cluster analysis.pdf		Unsupervised learning-cluster analysis.pdf
Using FacetGrid(intermediate DV).pdf		Using FacetGrid(intermediate DV).pdf
Using Seaborn styles(Intermediate DV).pdf		Using Seaborn styles(Intermediate DV).pdf
Using context managers- writing functions.pdf		Using context managers- writing functions.pdf
Visualizing Hieararchies(Unsupervised).pdf		Visualizing Hieararchies(Unsupervised).pdf
Visualizing the PCA(Unsupervised).pdf		Visualizing the PCA(Unsupervised).pdf
Visualizing your data - Data manipulation.pdf		Visualizing your data - Data manipulation.pdf
case study- school budgeting2.pdf		case study- school budgeting2.pdf
case study- school budgeting4Learning from the expert.pdf		case study- school budgeting4Learning from the expert.pdf
case study-school budeting3Pipelines.pdf		case study-school budeting3Pipelines.pdf
probability density functions - statstical thinking.pdf		probability density functions - statstical thinking.pdf
real world examples - writing functions.pdf		real world examples - writing functions.pdf

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