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Data-Science-For-Beginners
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1-Introduction/01-defining-data-science/README.md
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# Defining Data Science # Defining Data Science
[![Defining Data Science Video](images/video-def-ds.png)](https://youtu.be/pqqsm5reGvs) |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/01-Definitions.png)|
|:---:|
|Defining Data Science - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
---
[![Defining Data Science Video](images/video-def-ds.png)](https://youtu.be/pqqsm5reGvs)
## [Pre-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/0) ## [Pre-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/0)

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1-Introduction/02-ethics/README.md
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# Introduction to Data Ethics # Introduction to Data Ethics
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/02-Ethics.png)|
|:---:|
| Data Science Ethics - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
---
We are all data citizens living in a datafied world. We are all data citizens living in a datafied world.
Market trends tell us that by 2022, 1-in-3 large organizations will buy and sell their data through online [Marketplaces and Exchanges](https://www.gartner.com/smarterwithgartner/gartner-top-10-trends-in-data-and-analytics-for-2020/). As **App Developers**, we'll find it easier and cheaper to integrate data-driven insights and algorithm-driven automation into daily user experiences. But as AI becomes pervasive, we'll also need to understand the potential harms caused by the [weaponization](https://www.youtube.com/watch?v=TQHs8SA1qpk) of such algorithms at scale. Market trends tell us that by 2022, 1-in-3 large organizations will buy and sell their data through online [Marketplaces and Exchanges](https://www.gartner.com/smarterwithgartner/gartner-top-10-trends-in-data-and-analytics-for-2020/). As **App Developers**, we'll find it easier and cheaper to integrate data-driven insights and algorithm-driven automation into daily user experiences. But as AI becomes pervasive, we'll also need to understand the potential harms caused by the [weaponization](https://www.youtube.com/watch?v=TQHs8SA1qpk) of such algorithms at scale.
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## [Pre-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/2) 🎯 ## [Pre-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/2) 🎯
## Sketchnote 🖼
> A Visual Guide to Data Ethics by [Nitya Narasimhan](https://twitter.com/nitya) / [(@sketchthedocs)](https://sketchthedocs.dev)
---
## Basic Definitions ## Basic Definitions
Let's start by understanding the basic terminology. Let's start by understanding the basic terminology.

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1-Introduction/03-defining-data/README.md
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# Defining Data # Defining Data
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/03-DefiningData.png)|
|:---:|
|Defining Data - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
Data are facts, information, observations and measurements that are used to make discoveries and to support informed decisions. A data point is a single unit of data with in a dataset, which is collection of data points. Datasets may come in different formats and structures, and will usually be based on its source, or where the data came from. For example, a company's monthly earnings might be in a spreadsheet but hourly heart rate data from a smartwatch may be in [JSON](https://stackoverflow.com/a/383699) format. It's common for data scientists to work with different types of data within a dataset. Data are facts, information, observations and measurements that are used to make discoveries and to support informed decisions. A data point is a single unit of data with in a dataset, which is collection of data points. Datasets may come in different formats and structures, and will usually be based on its source, or where the data came from. For example, a company's monthly earnings might be in a spreadsheet but hourly heart rate data from a smartwatch may be in [JSON](https://stackoverflow.com/a/383699) format. It's common for data scientists to work with different types of data within a dataset.
This lesson focuses on identifying and classifying data by its characteristics and its sources. This lesson focuses on identifying and classifying data by its characteristics and its sources.

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1-Introduction/04-stats-and-probability/README.md
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# A Brief Introduction to Statistics and Probability # A Brief Introduction to Statistics and Probability
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/04-Statistics-Probability.png)|
|:---:|
| Statistics and Probability - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
Statistics and Probability Theory are two highly related areas of Mathematics that are highly relevant to Data Science. It is possible to operate with data without deep knowledge of mathematics, but it is still better to know at least some basic concepts. Here we will present a short introduction that will help you get started. Statistics and Probability Theory are two highly related areas of Mathematics that are highly relevant to Data Science. It is possible to operate with data without deep knowledge of mathematics, but it is still better to know at least some basic concepts. Here we will present a short introduction that will help you get started.
[![Intro Video](images/video-prob-and-stats.png)](https://youtu.be/Z5Zy85g4Yjw) [![Intro Video](images/video-prob-and-stats.png)](https://youtu.be/Z5Zy85g4Yjw)

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2-Working-With-Data/05-relational-databases/README.md
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# Working with Data: Relational Databases # Working with Data: Relational Databases
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/05-RelationalData.png)|
|:---:|
| Working With Data: Relational Databases - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
Chances are you have used a spreadsheet in the past to store information. You had a set of rows and columns, where the rows contained the information (or data), and the columns described the information (sometimes called metadata). A relational database is built upon this core principle of columns and rows in tables, allowing you to have information spread across multiple tables. This allows you to work with more complex data, avoid duplication, and have flexibility in the way you explore the data. Let's explore the concepts of a relational database. Chances are you have used a spreadsheet in the past to store information. You had a set of rows and columns, where the rows contained the information (or data), and the columns described the information (sometimes called metadata). A relational database is built upon this core principle of columns and rows in tables, allowing you to have information spread across multiple tables. This allows you to work with more complex data, avoid duplication, and have flexibility in the way you explore the data. Let's explore the concepts of a relational database.
## [Pre-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/8) ## [Pre-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/8)

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2-Working-With-Data/06-non-relational/README.md
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# Working with Data: Non-Relational Data # Working with Data: Non-Relational Data
## [Pre-Lecture Quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/10) |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/06-NoSQL.png)|
|:---:|
|Working with NoSQL Data - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
## [Pre-Lecture Quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/10)
Data is not limited to relational databases. This lesson focuses on non-relational data and will cover the basic of spreadsheets and NoSQL. Data is not limited to relational databases. This lesson focuses on non-relational data and will cover the basic of spreadsheets and NoSQL.

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2-Working-With-Data/07-python/README.md
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# Working with Data: Python and the Pandas Library # Working with Data: Python and the Pandas Library
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/07-WorkWithPython.png)|
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|Working With Python - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
[![Intro Video](images/video-ds-python.png)](https://youtu.be/dZjWOGbsN4Y) [![Intro Video](images/video-ds-python.png)](https://youtu.be/dZjWOGbsN4Y)
While databases offer very efficient ways to store data and query them using query languages, the most flexible way of data processing is writing your own program to manipulate data. In many cases, doing a database query would be a more effective way. However in some cases when more complex data processing is needed, it cannot be done easily using SQL. While databases offer very efficient ways to store data and query them using query languages, the most flexible way of data processing is writing your own program to manipulate data. In many cases, doing a database query would be a more effective way. However in some cases when more complex data processing is needed, it cannot be done easily using SQL.

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2-Working-With-Data/08-data-preparation/README.md
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# Working with Data: Data Preparation # Working with Data: Data Preparation
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/08-DataPreparation.png)|
|:---:|
|Data Preparation - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
## Pre-Lecture Quiz ## Pre-Lecture Quiz
[Pre-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/14) [Pre-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/14)

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3-Data-Visualization/09-visualization-quantities/README.md
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# Visualizing Quantities # Visualizing Quantities
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/01-Definitions.png)|
|:---:|
| Visualizing Quantities - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
In this lesson you will explore how to use one of the many available Python libraries to learn how to create interesting visualizations all around the concept of quantity. Using a cleaned dataset about the birds of Minnesota, you can learn many interesting facts about local wildlife. In this lesson you will explore how to use one of the many available Python libraries to learn how to create interesting visualizations all around the concept of quantity. Using a cleaned dataset about the birds of Minnesota, you can learn many interesting facts about local wildlife.
## [Pre-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/16) ## [Pre-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/16)

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3-Data-Visualization/10-visualization-distributions/README.md
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# Visualizing Distributions # Visualizing Distributions
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/10-Visualizing-Distributions.png)|
|:---:|
| Visualizing Distributions - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
In the previous lesson, you learned some interesting facts about a dataset about the birds of Minnesota. You found some erroneous data by visualizing outliers and looked at the differences between bird categories by their maximum length. In the previous lesson, you learned some interesting facts about a dataset about the birds of Minnesota. You found some erroneous data by visualizing outliers and looked at the differences between bird categories by their maximum length.
## [Pre-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/18) ## [Pre-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/18)

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3-Data-Visualization/11-visualization-proportions/README.md
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# Visualizing Proportions # Visualizing Proportions
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/11-Visualizing-Proportions.png)|
|:---:|
|Visualizing Proportions - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
In this lesson, you will use a different nature-focused dataset to visualize proportions, such as how many different types of fungi populate a given dataset about mushrooms. Let's explore these fascinating fungi using a dataset sourced from Audubon listing details about 23 species of gilled mushrooms in the Agaricus and Lepiota families. You will experiment with tasty visualizations such as: In this lesson, you will use a different nature-focused dataset to visualize proportions, such as how many different types of fungi populate a given dataset about mushrooms. Let's explore these fascinating fungi using a dataset sourced from Audubon listing details about 23 species of gilled mushrooms in the Agaricus and Lepiota families. You will experiment with tasty visualizations such as:
- Pie charts 🥧 - Pie charts 🥧

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3-Data-Visualization/12-visualization-relationships/README.md
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# Visualizing Relationships: All About Honey 🍯 # Visualizing Relationships: All About Honey 🍯
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/12-visualizing-relationships.png)|
|:---:|
|Visualizing Relationships - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
Continuing with the nature focus of our research, let's discover interesting visualizations to show the relationships between various types of honey, according to a dataset derived from the [United States Department of Agriculture](https://www.nass.usda.gov/About_NASS/index.php). Continuing with the nature focus of our research, let's discover interesting visualizations to show the relationships between various types of honey, according to a dataset derived from the [United States Department of Agriculture](https://www.nass.usda.gov/About_NASS/index.php).
This dataset of about 600 items displays honey production in many U.S. states. So, for example, you can look at the number of colonies, yield per colony, total production, stocks, price per pound, and value of the honey produced in a given state from 1998-2012, with one row per year for each state. This dataset of about 600 items displays honey production in many U.S. states. So, for example, you can look at the number of colonies, yield per colony, total production, stocks, price per pound, and value of the honey produced in a given state from 1998-2012, with one row per year for each state.

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3-Data-Visualization/13-meaningful-visualizations/README.md
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# Making Meaningful Visualizations # Making Meaningful Visualizations
|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/13-MeaningfulViz.png)|
|:---:|
| Meaningful Visualizations - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
> "If you torture the data long enough, it will confess to anything" -- [Ronald Coase](https://en.wikiquote.org/wiki/Ronald_Coase) > "If you torture the data long enough, it will confess to anything" -- [Ronald Coase](https://en.wikiquote.org/wiki/Ronald_Coase)
One of the basic skills of a data scientist is the ability to create a meaningful data visualization that helps answer questions you might have. Prior to visualizing your data, you need to ensure that it has been cleaned and prepared, as you did in prior lessons. After that, you can start deciding how best to present the data. One of the basic skills of a data scientist is the ability to create a meaningful data visualization that helps answer questions you might have. Prior to visualizing your data, you need to ensure that it has been cleaned and prepared, as you did in prior lessons. After that, you can start deciding how best to present the data.

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