Transition from finance book - language and links

docs/notes/dev-tools/google-colab/overview.qmd

@@ -13,6 +13,9 @@ When creating a new notebook, practice giving the document a title (e.g. \"My Fi
 
 From any notebook, it is possible to create a new notebook, make a copy of the notebook, or download the notebook, using the \"File\" menu.
 
+![Google Colab File menu](../../../images/colab-file-menu.png){.img-fluid style="max-height:350;"}
+
+
 ::: {.callout-warning}
 When you access a notebook that has been shared with you, unless you have \"Editor\" privileges, you won't be able to save your work, and any edits you make in that document will be lost.
 

docs/notes/fetching-data/csv.qmd

@@ -36,7 +36,7 @@ First we note the URL of where the data resides. Then we pass that as a paramete
 from pandas import read_csv
 
 # the URL of some CSV data we stored online:
-request_url = "https://raw.githubusercontent.com/prof-rossetti/python-for-finance/main/docs/data/gradebook.csv"
+request_url = "https://raw.githubusercontent.com/prof-rossetti/intro-software-dev-python-book/main/docs/data/gradebook.csv"
 
 df = read_csv(request_url)
 print(type(df))

docs/notes/fetching-data/html-web-scraping.qmd

@@ -57,7 +57,7 @@ First we note the URL of where the data or webpage resides. Then we pass that as
 import requests
 
 # the URL of some HTML data or web page stored online:
-request_url = "https://raw.githubusercontent.com/prof-rossetti/python-for-finance/main/docs/data/my_lists.html"
+request_url = "https://raw.githubusercontent.com/prof-rossetti/intro-software-dev-python-book/main/docs/data/my_lists.html"
 
 response = requests.get(request_url)
 print(type(response))
@@ -175,7 +175,7 @@ import requests
 from bs4 import BeautifulSoup
 
 # the URL of some HTML data or web page stored online:
-request_url = "https://raw.githubusercontent.com/prof-rossetti/python-for-finance/main/docs/data/my_tables.html"
+request_url = "https://raw.githubusercontent.com/prof-rossetti/intro-software-dev-python-book/main/docs/data/my_tables.html"
 
 response = requests.get(request_url)
 

docs/notes/fetching-data/json.qmd

@@ -42,7 +42,7 @@ First we note the URL of where the data resides. Then we pass that as a paramete
 import requests
 
 # the URL of some JSON data we stored online:
-request_url = "https://raw.githubusercontent.com/prof-rossetti/python-for-finance/main/docs/data/gradebook.json"
+request_url = "https://raw.githubusercontent.com/prof-rossetti/intro-software-dev-python-book/main/docs/data/gradebook.json"
 
 response = requests.get(request_url)
 print(type(response))

docs/notes/fetching-data/overview.qmd

@@ -4,7 +4,7 @@ So far, we have studied many data processing techniques using example data.
 
 But the real fun comes from working with real life data.
 
-The Internet is a boundless source of data, including financial data.
+The Internet is a boundless source of data, across a variety of domains, including financial data.
 
 ## Data Formats
 

docs/notes/fetching-data/xml.qmd

@@ -69,7 +69,7 @@ First we note the URL of where the data resides. Then we pass that as a paramete
 import requests
 
 # the URL of some XML data we stored online:
-request_url = "https://raw.githubusercontent.com/prof-rossetti/python-for-finance/main/docs/data/gradebook.xml"
+request_url = "https://raw.githubusercontent.com/prof-rossetti/intro-software-dev-python-book/main/docs/data/gradebook.xml"
 
 response = requests.get(request_url)
 print(type(response))

docs/why-python.qmd

@@ -20,11 +20,11 @@ Python is one of the most popular programming languages. According to recent rep
 
 This popularity translates into a strong and vibrant community that contributes to the language's development and support.
 
-For finance professionals, this means a wealth of resources, tutorials, and community support is readily available. Sites like [Stack Overflow](https://stackoverflow.com/) are great for asking and answering programming related questions.
+This means a wealth of resources, tutorials, and community support is readily available. Sites like [Stack Overflow](https://stackoverflow.com/) are great for asking and answering programming related questions.
 
-Additionally, the high demand for Python skills in the job market creates numerous career opportunities for analysts and programmers proficient in Python, especially in the world of Finance.
+Additionally, the high demand for Python skills in the job market creates numerous career opportunities for analysts and programmers proficient in Python, especially in the world of data science and software development.
 
-![Top jobs related to Python programming and Finance. Source: [US News and World Report](https://money.usnews.com/careers/best-jobs/rankings/the-100-best-jobs)](/images/python-top-jobs.png)
+![Top jobs related to Python programming. Source: [US News and World Report](https://money.usnews.com/careers/best-jobs/rankings/the-100-best-jobs)](/images/python-top-jobs.png)
 
 
 ## Ease of Use
@@ -37,7 +37,7 @@ The language is well-documented, providing extensive resources and help when nee
 
 Python boasts a rich ecosystem of open-source third-party libraries or plugins known as *packages*. These packages provide users with a wide array of tools and functionalities that extend the core capabilities of Python. This extensibility makes it easier to perform complex tasks without needing to write code from scratch, thereby saving time and effort for developers and analysts.
 
-Python packages offer powerful capabilities in data processing, analysis, visualization, and modeling. For financial applications, this means professionals can efficiently handle large datasets, perform sophisticated analyses, create insightful visualizations, and train predictive models. This robust ecosystem makes Python an indispensable tool in the finance industry, empowering users to make data-driven decisions and develop advanced financial solutions.
+Python packages offer powerful capabilities in data processing, analysis, visualization, and modeling. This means professionals can efficiently handle large datasets, perform sophisticated analyses, create insightful visualizations, and train predictive models. This robust ecosystem makes Python an indispensable tool in the field of data science, empowering users to develop advanced applications and decision support tools.
 
 ## Versatility