Merge pull request #326 from flegaspi700/main

Minor typo fix

Author: paladique

1-Introduction/01-defining-data-science/README.md

@@ -67,11 +67,11 @@ Vast amounts of data are incomprehensible for a human being, but once we create
 
 ## Types of Data
 
-As we have already mentioned, data is everywhere.  We just need to capture it in the right way!  It is useful to distinguish between **structured** and **unstructured** data. The former is typically represented in some well-structured form, often as a table or number of tables, while the latter is just a collection of files.  Sometimes we can also talk about **semistructured** data, that have some sort of a structure that may vary greatly.
+As we have already mentioned, data is everywhere.  We just need to capture it in the right way!  It is useful to distinguish between **structured** and **unstructured** data. The former is typically represented in some well-structured form, often as a table or number of tables, while the latter is just a collection of files.  Sometimes we can also talk about **semi-structured** data, that have some sort of a structure that may vary greatly.
 
 | Structured                                                                   | Semi-structured                                                                                | Unstructured                            |
 | ---------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------- | --------------------------------------- |
-| List of people with their phone numbers                                      | Wikipedia pages with links                                                                     | Text of Encyclopaedia Britannica        |
+| List of people with their phone numbers                                      | Wikipedia pages with links                                                                     | Text of Encyclopedia Britannica        |
 | Temperature in all rooms of a building at every minute for the last 20 years | Collection of scientific papers in JSON format with authors, data of publication, and abstract | File share with corporate documents     |
 | Data for age and gender of all people entering the building                  | Internet pages                                                                                 | Raw video feed from surveillance camera |
 

1-Introduction/02-ethics/README.md

@@ -12,7 +12,7 @@ Market trends tell us that by 2022, 1-in-3 large organizations will buy and sell
 
 Trends also indicate that we will create and consume over [180 zettabytes](https://www.statista.com/statistics/871513/worldwide-data-created/) of data by 2025. As **Data Scientists**, this gives us unprecedented levels of access to personal data. This means we can build behavioral profiles of users and influence decision-making in ways that create an [illusion of free choice](https://www.datasciencecentral.com/profiles/blogs/the-illusion-of-choice) while potentially nudging users towards outcomes we prefer. It also raises broader questions on data privacy and user protections.
 
-Data ethics are now _necessary guardrails_ for data science and engineering, helping us minimize potential harms and unintended consequences from our data-driven actions. The [Gartner Hype Cycle for AI](https://www.gartner.com/smarterwithgartner/2-megatrends-dominate-the-gartner-hype-cycle-for-artificial-intelligence-2020/) identifies relevant trends in digital ethics, responsible AI ,and AI governances as key drivers for larger megatrends around _democratization_ and _industrialization_ of AI.
+Data ethics are now _necessary guardrails_ for data science and engineering, helping us minimize potential harms and unintended consequences from our data-driven actions. The [Gartner Hype Cycle for AI](https://www.gartner.com/smarterwithgartner/2-megatrends-dominate-the-gartner-hype-cycle-for-artificial-intelligence-2020/) identifies relevant trends in digital ethics, responsible AI ,and AI governance as key drivers for larger megatrends around _democratization_ and _industrialization_ of AI.
 
 ![Gartner's Hype Cycle for AI - 2020](https://images-cdn.newscred.com/Zz1mOWJhNzlkNDA2ZTMxMWViYjRiOGFiM2IyMjQ1YmMwZQ==)
 
@@ -179,7 +179,7 @@ Here are a few examples:
 
 | Ethics Challenge | Case Study  | 
 |--- |--- |
-| **Informed Consent** | 1972 - [Tuskegee Syphillis Study](https://en.wikipedia.org/wiki/Tuskegee_Syphilis_Study) - African American men who participated in the study were promised free medical care _but deceived_ by researchers who failed to inform subjects of their diagnosis or about availability of treatment. Many subjects died & partners or children were affected; the study lasted 40 years. | 
+| **Informed Consent** | 1972 - [Tuskegee Syphilis Study](https://en.wikipedia.org/wiki/Tuskegee_Syphilis_Study) - African American men who participated in the study were promised free medical care _but deceived_ by researchers who failed to inform subjects of their diagnosis or about availability of treatment. Many subjects died & partners or children were affected; the study lasted 40 years. | 
 | **Data Privacy** |  2007 - The [Netflix data prize](https://www.wired.com/2007/12/why-anonymous-data-sometimes-isnt/) provided researchers with _10M anonymized movie rankings from 50K customers_ to help improve recommendation algorithms. However, researchers were able to correlate anonymized data with personally-identifiable data in _external datasets_ (e.g., IMDb comments) - effectively "de-anonymizing" some Netflix subscribers.|
 | **Collection Bias**  | 2013 - The City of Boston [developed Street Bump](https://www.boston.gov/transportation/street-bump), an app that let citizens report potholes, giving the city better roadway data to find and fix issues. However, [people in lower income groups had less access to cars and phones](https://hbr.org/2013/04/the-hidden-biases-in-big-data), making their roadway issues invisible in this app. Developers worked with academics to _equitable access and digital divides_ issues for fairness. |
 | **Algorithmic Fairness**  | 2018 - The MIT [Gender Shades Study](http://gendershades.org/overview.html) evaluated the accuracy of gender classification AI products, exposing gaps in accuracy for women and persons of color. A [2019 Apple Card](https://www.wired.com/story/the-apple-card-didnt-see-genderand-thats-the-problem/) seemed to offer less credit to women than men. Both illustrated issues in algorithmic bias leading to socio-economic harms.|

1-Introduction/04-stats-and-probability/README.md

@@ -250,7 +250,7 @@ Use the sample code in the notebook to test other hypothesis that:
 
 Probability and statistics is such a broad topic that it deserves its own course. If you are interested to go deeper into theory, you may want to continue reading some of the following books:
 
-1. [Carlos Fernanderz-Granda](https://cims.nyu.edu/~cfgranda/) from New York University has great lecture notes [Probability and Statistics for Data Science](https://cims.nyu.edu/~cfgranda/pages/stuff/probability_stats_for_DS.pdf) (available online)
+1. [Carlos Fernandez-Granda](https://cims.nyu.edu/~cfgranda/) from New York University has great lecture notes [Probability and Statistics for Data Science](https://cims.nyu.edu/~cfgranda/pages/stuff/probability_stats_for_DS.pdf) (available online)
 1. [Peter and Andrew Bruce. Practical Statistics for Data Scientists.](https://www.oreilly.com/library/view/practical-statistics-for/9781491952955/) [[sample code in R](https://github.com/andrewgbruce/statistics-for-data-scientists)]. 
 1. [James D. Miller. Statistics for Data Science](https://www.packtpub.com/product/statistics-for-data-science/9781788290678) [[sample code in R](https://github.com/PacktPublishing/Statistics-for-Data-Science)]