Merge pull request #33 from LaunchCodeEducation/Early_Chapter_Typos

Fixes some typos on Homepage and Chapters 1-3

content/_index.md

@@ -8,9 +8,9 @@ draft = false
 
 This course is an introduction to Data Analysis concepts and tools, such as Google Sheets, Python, SQL, and Tableau. Data Analysis is a multi-faceted field that includes working with business leaders, writing code, and digging through data to find solutions to issues a business is facing. As more and more companies turn to data to assist in making decisions, Data Analysis has grown exponentially. Not only is this a field that many companies need, but the analytical and learning skills you pick up in this course will serve you as you navigate today's technical landscape.
 
-We use Google Sheets to master spreadsheets as it is a tool that is available to all our students and it is robust enough for us to dive into complex datasets and start to seek out answers to important questions. To further dive into the data, we will be using Python. Python is a programming language that is used by many data analysts worldwide to perform complex functions and navigate very large datasets. Python also has other uses beyond data analystics so after learning the basics of programming and Python, you will find that you can adapt that skillset to many other stages of your learning journey. SQL is a different type of programming language that helps us navigate databases. We will learn more about what a database is later, but for now, you should know that a database holds large quantities of data. Companies store their data different ways, but by diving into the basics with SQL, we hope to give you the foundation you need to confidently explore a company's data storage and get the numbers you require. Finally, we will be using Tableau to put together visuals and graphics to help us communicate our findings to our colleagues. Tableau is one of the industry's most powerful visualization tools and we will use this tool to foster conversation about what makes a visual effective and how we can communicate our findings to the appropriate team. 
+We use Google Sheets to master spreadsheets as it is a tool that is available to all our students and it is robust enough for us to dive into complex datasets and start to seek out answers to important questions. To further dive into the data, we will be using Python. Python is a programming language that is used by many data analysts worldwide to perform complex functions and navigate very large datasets. Python also has other uses beyond data analytics so after learning the basics of programming and Python, you will find that you can adapt that skillset to many other stages of your learning journey. SQL is a different type of programming language that helps us navigate databases. We will learn more about what a database is later, but for now, you should know that a database holds large quantities of data. Companies store their data different ways, but by diving into the basics with SQL, we hope to give you the foundation you need to confidently explore a company's data storage and get the numbers you require. Finally, we will be using Tableau to put together visuals and graphics to help us communicate our findings to our colleagues. Tableau is one of the industry's most powerful visualization tools and we will use this tool to foster conversation about what makes a visual effective and how we can communicate our findings to the appropriate team. 
 
-As you work through this course, we encourage you to remember that learning data analytics is not only valuable and challenging, it can also be fun! Every moment inspires us to keep going forward and to learn new things. You may find some concepts difficult to understand at first, but these will also be the skills you may take the most pride in mastering making the journey long and winding and rewarding.
+As you work through this course, we encourage you to remember that learning data analytics is not only valuable and challenging, it can also be fun! Every moment inspires us to keep going forward and to learn new things. You may find some concepts difficult to understand at first, but these will also be the skills you may take the most pride in mastering, making the journey long and winding and rewarding.
 
 From the moment you started reading this book, you became a data analyst. We hope you enjoy your journey with us!
 
@@ -31,6 +31,6 @@ There are no academic pre-requisites for this course.
 In order to participate, you will need to bring your own laptop. A Chromebook or tablet will not be sufficient. Your laptop should meet the following requirements:
 
 1. Be younger than 4 years old and able to support the latest operating system.
-1. You have administrator privileges and am able to install new software.
+1. You have administrator privileges and are able to install new software.
 1. Has an i5 or i7 Intel chip or an M1 or newer Apple processor.
 1. Has at least 8 GB of RAM.

content/intro-to-spreadsheets/reading/expanding-spreadsheets/_index.md

@@ -11,7 +11,7 @@ As you work more on spreadsheets, you may find that you want to add a row or col
 Let's revisit our Car Info workbook. While we already have some great data stored about the two cars, we need some more information. 
 
 1. We want to insert a column between "Model" and "Car Mileage". Right-click on the D for the "Model" column and select *Insert 1 column right*. Alternatively, you can right-click on the E for the "Car Mileage" column and select *Insert 1 column left*.
-1. You now have an empty column that is the column E and "Car Mileage" is now column F. Type "Current Value" in E1.
+1. You now have an empty column that is column E and "Car Mileage" is now column F. Type "Current Value" in E1.
 1. Add the current value for both the cars you created on the previous page.
 
 You can do the same for rows by right-clicking on the row number and inserting a new row above or below the one you selected.

content/intro-to-spreadsheets/reading/formulas-and-calculations/_index.md

@@ -17,11 +17,11 @@ Sometimes, we want to set up a whole column where each value is the result of a
 
 1. In the "Overview" tab, add a new header to column G called "Tax Value".
 1. We checked the tax rate and on cars right now, it is 2%, so the taxes we owe would be 2% of the current value.
-1. Starting with G2, write in that cell the formula for finding the tax value of the car. As you write the cell identifuer for the current value of row 2, Google Sheets will highlight the cell that you are referencing which can serve as a helpful visual reminder of what the formula is going to be using.
+1. Starting with G2, write in that cell the formula for finding the tax value of the car. As you write the cell identifier for the current value of row 2, Google Sheets will highlight the cell that you are referencing which can serve as a helpful visual reminder of what the formula is going to be using.
 1. Now we want every row in column G to accurately reflect the tax value of the car. To copy the formula and not the value in G2, hover your mouse over the bottom right corner of G2 and you will see a plus sign appear. Click and drag down to G3. Release once both cells are highlighted and you should see 2% of E3 in G3. To confirm, click on the cell to see what the formula looks like.
 1. If you want to add another row, you can click and drag down as you did above.
 
-The benefit of setting up a formula like this is that we don't have to worry about a human miscalculating and even more importantly, these cars are going to depreciate in value so that "Current Value" column is going to look different over time. Instead of us have to updating both the current value and the tax value when the car depreciates, we can update the current value and the tax value will update to reflect the change.
+The benefit of setting up a formula like this is that we don't have to worry about a human miscalculating and even more importantly, these cars are going to depreciate in value so that "Current Value" column is going to look different over time. Instead of us having to update both the current value and the tax value when the car depreciates, we can update the current value and the tax value will update to reflect the change.
 
 ## Setting up a constant
 
@@ -43,4 +43,4 @@ Finally, in a formula, you may want to reference a cell in another spreadsheet.
 1. To reference another spreadsheet in a formula, we first need to type the name of the spreadsheet, an exclamation point, and then the cell. If we have a spreadsheet named "Budget" and we want to reference A1 in that spreadsheet, then we would write `Budget!A1`. If there are spaces in our spreadsheet name, then it should be wrapped in single quotes like `'Budget 2024'!A1`.
 1. We want to track how much of each car's maintenance budget has been spent. Since there is only one maintenance job for one car in another spreadsheet, we are going to set up a formula and NOT click and drag it to the second car. Use the above to reference the cost of the one maintenance task and divide it by a budget of 2000 to see how much we have used so far.
 
-With formulas, the possibilities are endless! We can also make use of the *Quick Calculations* menu to easy set up sums and averages.
+With formulas, the possibilities are endless! We can also make use of the *Quick Calculations* menu to easily set up sums and averages.

content/learning-data-analysis/reading/why-learn-code/_index.md

@@ -6,10 +6,10 @@ originalAuthorGitHub = "jwoolbright23"
 
 Many might think that every data analyst’s career solely composes of spreadsheets, but spreadsheets are not the only way to explore data. We will also learn to code for a few reasons.
 
-First, as an analyst, every program you use is allowing you to communicate instructions to a machine. Whether it is writing Python code or using a drag-and-drop menu in Tableau, you are asking a machine to perform certain necessary tasks to help you achieve a goal. Code is the undercurrent of all of these programs. By learning to code, you are learning the most universal aspect of all of the tools you will use throughout your career. With this particular knowledge, you will also be better prepared to adapt to the needs of your workplace. Not every company uses Tableau and Azure Data Studio. Some may insist you use a code-centric tools like Matplotlib for visualizations.
+First, as an analyst, every program you use is allowing you to communicate instructions to a machine. Whether it is writing Python code or using a drag-and-drop menu in Tableau, you are asking a machine to perform certain necessary tasks to help you achieve a goal. Code is the undercurrent of all of these programs. By learning to code, you are learning the most universal aspect of all of the tools you will use throughout your career. With this particular knowledge, you will also be better prepared to adapt to the needs of your workplace. Not every company uses Tableau and Azure Data Studio. Some may insist you use code-centric tools like Matplotlib for visualizations.
 
 Through learning to code, you will also gain confidence in handling errors. Errors come up when working with any software program and they also come up when handling data. All it can take is one typo to throw off your analysis of a dataset.
 
-You will also learn concepts that you will use throughout the course as you dive deeper into the world of data. One such concept is data types. Without spoiling too much, data types matter immensely to data analysts. Datasets are not just numbers. They can also holds names, dates, and other information of various different types and data analysts need to be able to work with all of the different types of data. No matter what tool you are using at the moment in the course or throughout your career, you will need to take into account data types when looking at a dataset.
+You will also learn concepts that you will use throughout the course as you dive deeper into the world of data. One such concept is data types. Without spoiling too much, data types matter immensely to data analysts. Datasets are not just numbers. They can also hold names, dates, and other information of various different types and data analysts need to be able to work with all of the different types of data. No matter what tool you are using at the moment in the course or throughout your career, you will need to take into account data types when looking at a dataset.
 
 Now that you are ready to learn how to code, let’s focus in on why we are starting with Python.