Minor Edits Week 1 and Week 2

Week1-Introduction/README.md

@@ -9,7 +9,7 @@ Welcome to Week 1 of the Introduction to Digital History. This is an exciting st
 
 Before we get started, first let's dive into exactly what this course is:
 
-The first class of the weeks consists of discussion and research on about related topics that vary from historical to recently recorded datasets. 
+The first class of the weeks consists of discussion and research on topics that vary from historical to recently recorded datasets. 
 
 The second class of the weeks are meant to help prepare for these discussions. This portion of the course is a venture BitProject and California State Fullerton have begun to introduce the humanities curriculum with modern analytical thinking tools, primarily implemented through programming.
 
@@ -29,7 +29,7 @@ Every week, you will learn a new tool and how to use it effectively. We will do
 You can reinforce your knowledge by working on graded 'Now Try This' exercises that are included in every tutorial notebook.
 
 ### 📗 Homeworks
-Additionally, you will also have access to homeworks for the 7 Tutorials, these homeworks will be similar to the 'Now Try This' sections.
+Additionally, you will also have access to homework for each of the 7 Tutorials.  These homeworks will be similar to the 'Now Try This' sections.
 
 By the last tutorial, you will be ready and equipped with the complete resources to work through a dataset by utilizing the following skills:
 
@@ -39,11 +39,11 @@ By the last tutorial, you will be ready and equipped with the complete resources
 - Visualize categorical data and perform statistical analysis to draw conclusion.
 - Understand the concept of data modeling.
 
-### 📘 Practicums:
-There are 3 practicums in this course. 
-- Practicum 1 is an individual assignment and requires you to use the concepts learned from Weeks 2,3 and 4. You will be working through the dataset step-by-step to draw insightful information using the resources provided.
-- Practicum 2 is a group-based assignment which will involve using concepts from Weeks 2 through Weeks 8. For this lab, you will be going more in-depth with the dataset provided and will write a report on your findings.
-- Practicum 3 is a two-week final project. This lab will allow you to choose one of the datasets provided and apply the complete data analysis pipeline from start to end. This will give you an opportunity to document your process and your results, in which you will present a final report that details your conclusions and insights.
+### 📘 Projects:
+There are 3 major projects in this course. 
+- Project 1 is an individual assignment and requires you to use the concepts learned from Weeks 2,3 and 4. You will be working through the dataset step-by-step to draw insightful information using the resources provided.
+- Project 2 is a group-based assignment which will involve using concepts from Weeks 2 through Weeks 8. For this lab, you will be going more in-depth with the dataset provided and will write a report on your findings.
+- Project 3 is a two-week project meant to allow you to show all that you've learned over the duration of the course. This lab will allow you to choose one of the datasets provided and apply the complete data analysis pipeline from start to end. This will give you an opportunity to document your process and your results, in which you will present a final report that details your conclusions and insights.
 
 ### 📝 Grading:
 #### Tutorials:
@@ -78,12 +78,12 @@ Data analytics is the science of analyzing raw data in order to make conclusions
 
 Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information. This information can then be used to optimize processes to increase the overall efficiency of a business or system.
 
-Without data analysis, we wouldn't be able to advance as fast to the point where were are today. The reason a field such as data analysis is very important is because, in many cases it is better for us to draw conclusions through our own intuition. This is particularly because of our inate ability to simplify complex patterns in order to be able to comprehend them.
+Without data analysis, we wouldn't be able to advance as fast to the point where were are today. The reason a field like data analysis is so important is because in many cases, it is better for us to draw conclusions through our own intuition. This is particularly because of our inate ability to simplify complex patterns in order to be able to comprehend them.
 
 **One known example is of John Snow (Not the Game of Thrones character)**
 ![John Snow](https://github.com/bitprj/DigitalHistory/blob/master/Week1-Introduction/imgs/john-snow-map.jpg?raw=true)
 
-John Snow was a British doctor who used data collection and data analysis to trace the source of a cholera outbreak in central London, and to come to the conclusion that cholera was transmitted by “an agent in the water” than by the accepted theory that it was transmitted by “bad air”.  
+John Snow was a British doctor who used data collection and data analysis to trace the source of a cholera outbreak in central London, and to come to the conclusion that cholera was transmitted by “an agent in the water” rather than by the accepted theory that cholera was transmitted by “bad air”.  
 
 Snow used data collection to trace the cholera outbreak to two water companies who drew their water from the Thames river, virtually unfiltered. He notes that a huge, double-blind experiment fell into his lap: “No fewer than three hundred thousand people of both sexes, of every age and occupation, and of every rank and station, from gentlefolks down to the very poor, were divided into two groups without their choice, and, in most cases, without their knowledge; one group being supplied water containing the sewage of London, and amongst it, whatever might have come from the cholera patients, the other group having water quite free from such impurity.” 
 

Week2-Introduction-to-Python-_-NumPy/Intro_to_Python.ipynb

@@ -481,7 +481,7 @@
       "source": [
         "## Variable Assignments\n",
         "\n",
-        "we can do a lot more with Python than just using it as a calculator. We can store any numbers we create in **variables**.\n",
+        "We can do a lot more with Python than just using it as a calculator. We can store any numbers we create in **variables**.\n",
         "\n",
         "We use a single equals sign to assign labels or values to variables. Let's see a few examples of how we can do this."
       ]
@@ -674,7 +674,7 @@
         "id": "kHJyh-qD2wYL"
       },
       "source": [
-        "So what have we learned? We learned some of the basics of numbers in Python. We also learned how to do arithmetic and use Python as a basic calculator. We then wrapped it up with learning about Variable Assignment in Python.\n",
+        "So what have we learned? We learned some of the basics of numbers in Python. We also learned how to do arithmetic and use Python as a basic calculator. We then wrapped it up by learning about Variable Assignment in Python.\n",
         "\n",
         "Up next we'll learn about Strings!"
       ]
@@ -1613,7 +1613,7 @@
       "source": [
         "## Booleans\n",
         "\n",
-        "Python comes with *booleans* (values that are essentially binary: True or False, 1 or 0). It also has a placeholder object called None. Let's walk through a few quick examples of Booleans."
+        "Python uses a type of data called *booleans* (values that are essentially binary: True or False, 1 or 0). It also has a placeholder object called None. Let's walk through a few quick examples of Booleans."
       ]
     },
     {
@@ -1713,7 +1713,7 @@
         "id": "0VOMDbyu218x"
       },
       "source": [
-        "That's all to booleans! Next we start covering data structures. First up, lists."
+        "That's all for booleans! Next we start covering data structures. First up, lists."
       ]
     },
     {
@@ -1725,7 +1725,7 @@
       "source": [
         "## Lists\n",
         "\n",
-        "Earlier when discussing strings we introduced the concept of a *sequence*. Lists is the most generalized version of sequences in Python. Unlike strings, they are mutable, meaning the elements inside a list can be changed!\n",
+        "Earlier when discussing strings, we introduced the concept of a *sequence*. Lists is the most generalized version of sequences in Python. Unlike strings, they are mutable, meaning the elements inside a list can be changed!\n",
         "\n",
         "Lists are constructed with brackets [] and commas separating every element in the list.\n",
         "\n",
@@ -1997,7 +1997,7 @@
       "source": [
         "### Basic List Methods\n",
         "\n",
-        "If you are familiar with another programming language, you might start to draw parallels between arrays in another language and lists in Python. Lists in Python however, tend to be more flexible than arrays in other languages for two reasons: they have no fixed size (meaning we don't have to specify how big a list will be), and they have no fixed type constraint (like we've seen above).\n",
+        "If you are familiar with another programming language, you might start to draw parallels between arrays in another language and lists in Python. Lists in Python, however, tend to be more flexible than arrays in other languages for two reasons: they have no fixed size (meaning we don't have to specify how big a list will be), and they have no fixed type constraint (like we've seen above).\n",
         "\n",
         "Let's go ahead and explore some more special methods for lists:"
       ]
@@ -2061,7 +2061,7 @@
         "id": "7CIIhCgYOlAd"
       },
       "source": [
-        "Use **pop** to \"pop off\" an item from the list. By default pop takes off the last index, but you can also specify which index to pop off. Let's see an example:"
+        "Use **pop** to \"pop off\" an item from the list. By default, pop takes off the last index, but you can also specify which index to pop off. Let's see an example:"
       ]
     },
     {
@@ -2300,7 +2300,7 @@
         "id": "1CiTJaLFOlBN"
       },
       "source": [
-        "We can again use indexing to grab elements, but now there are two levels for the index. The items in the matrix object, and then the items inside that list!"
+        "We can again use indexing to grab elements, but now, there are two levels for the index: the items in the matrix object and the items inside that list!"
       ]
     },
     {
@@ -2487,9 +2487,9 @@
       "source": [
         "## Tuples\n",
         "\n",
-        "In Python tuples are very similar to lists, however, unlike lists they are *immutable* meaning they can not be changed. You would use tuples to present things that shouldn't be changed, such as days of the week, or dates on a calendar. \n",
+        "In Python, tuples are very similar to lists, however, unlike lists they are *immutable* meaning they can not be changed. You should use tuples to present things that shouldn't be changed, such as days of the week, or dates on a calendar. \n",
         "\n",
-        "You'll have an intuition of how to use tuples based on what you've learned about lists. We can treat them very similarly with the major distinction being that tuples are immutable."
+        "You'll intuitively know how to use tuples based on what you've learned about lists. We can treat them very similarly with the major distinction being that tuples are immutable."
       ]
     },
     {
@@ -4801,4 +4801,4 @@
       "outputs": []
     }
   ]
-}
+}