Python for Data Science

Rationale for this Assignment

The world of technology is moving fast. Traditional textbooks are quickly outdated. Quizzes and tests are not the most useful way to keep knowledge over time. Meanwhile, employers know good and well that you do not and cannot know everything in your field. Rather, they increasingly rely on you to be a self-driven learner. When a new need arises, you won’t be pulling years-old knowledge out of your head. Rather, you’ll be turning to multiple sources of current information, including especially these:

  • Effective web searches
  • Known, dependable, online resources and reference materials
  • Professional networks, both online and offline

In the world we live in, these sources are the only ones capable of keeping up with the pace of change. In this course, I’ll be asking you to pull together knowledge from good web searches and online reference sites to answer specific questions. I’ll ask you to summarize that knowledge in a set of useful notes, complete with references. Thus, after this course is done, you can turn to these notes for a quick reminder and starting point, and then you can develop and update them over time.

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Downloadable Document Template and Research Questions

Download and begin with this document, which includes the concepts and related questions required for this assignment:

1.1 Research Notes Python Data Science.docx

Instructions

Place your first and last name in the appropriate place in the document header.

Then, for each concept:

  • Conduct a web search to find one or more credible sources that help you define it and answer the related questions.
    • NOTE 1: There are many high-quality data- and tech-related resources on the web. The concepts and questions below are frequently discussed on the web, and so answers will not be difficult to find. Over time as you do this work, you’ll identify several credible sources that you can frequently return to.
    • NOTE 2: IN MY CLASS, for these assignments — which are not formal essays but developing general knowledge — Wikipedia counts as a credible source. Wikipedia is often a trustworthy source for tech-related fields.
  • Type out the best definition and answers you find, turning this document into your study notes for future quizzes.
  • When you quote a source directly, use quotation marks and provide an in-text citation in parentheses with a shortened version of the source title.
  • Then at the end of each answer, provide a title and link to the sources you used.
  • Format the entire document well, so that it looks organized and professional.
  • DO YOUR OWN WORK. Do not turn in anyone else’s document as your own. Do not copy significant portions of someone else’s document.

When complete, delete the instructions at the beginning of the document and submit your completed notes.

1.1 Research Notes: Python for Data Science

Place your first and last name in the appropriate place in the header above.

Then, for each concept listed below:

  1. Conduct a web search to find one or more credible sources that help you define it and answer the related questions.
    • NOTE 1: There are many high-quality data- and tech-related resources on the web. The concepts and questions below are frequently discussed on the web, and so answers will not be difficult to find. Over time as you do this work, you’ll identify several credible sources that you can frequently return to.
    • NOTE 2: IN MY CLASS, for these assignments — which are not formal essays but developing general knowledge — Wikipedia counts as a credible source. Wikipedia is often a trustworthy source for tech-related fields.
  2. Type out the best definition and answers you find, turning this document into your study notes for future quizzes.
  3. When you quote a source directly, use quotation marks and provide an in-text citation in parentheses with a shortened version of the source title.
  4. Then at the end of each answer, provide a title and link to the sources you used.
  5. Format the entire document well, so that it looks organized and professional.
  6. DO YOUR OWN WORK. Do not turn in anyone else’s document as your own. Do not copy significant portions of someone else’s document.

When complete, remove these instructions and submit your completed notes.

Data Analytics Concepts

  1. Define Data Science in 20-30 words. Then list 5 to 7 key kinds of activity included under this heading.
  2. What is Python?
    1. Provide a definition in 20-30 words.
    2. When was it created?
    3. What can Python be used for, and what are some major companies and/or products that use Python? (Provide a representative list of 5-8 ways in which Python is used.)
    4. Why is Python such a useful tool for data science? What kinds of data-science related tasks is it useful for?
  3. What is the pandas Python library?
    1. Provide a definition in 20-30 words.
    2. How does Python with pandas compare to R, the statistical and analytics language?
    3. What are some advantages Python with pandas has over R?
  4. What is the Anaconda Distribution?
    1. Provide a brief definition of 10-20 words.
    2. What does it contain? (Summarize in 20-30 words.)
    3. Why is this distribution helpful for data-related work?
  5. What is a Jupyter Notebook?
    1. What are some advantages of using Jupyter Notebooks for data analysis?
    2. What major cloud services use versions of Jupyter Notebooks for their data science tools?

Example Answer: Data Science

The following illustrates an A answer for the first concept, Data Science. (I recommend adding a nice heading before each concept, which will help you when you study.)

Data Science

Definition

Data science is “a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.” It “employs techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, and information science” (Wikipedia). (Links to an external site.)

Data Science Activities

There are five key types of activity under the heading of data science, including:

  • Capturing data (data acquisition, data entry, data extraction, etc.)
  • Storing and maintaining data (data warehousing, datea cleansing, etc.)
  • Processing data (data mining, clustering, classification, etc.)
  • Analyzing data (exploratory, predictive, regression, etc.)
  • Communicating (reporting, visualization, business intelligence, etc.)

This image illustrates the activities involved in a data science life cycle:

(Berkeley School of Information)

Sources

“Data Science.” Wikipedia.
https://en.wikipedia.org/wiki/Data_science (Links to an external site.)

“What is Data Science.” Berkeley School of Information. https://datascience.berkeley.edu/about/what-is-data-science/

1.2 Discussion: Using Udemy

Use this discussion board to share successes, problems, tips, and recommendations, and discussion regarding purchasing and using the required Udemy titles.

As a reminder, the required Udemy titles and my notes about purchasing them are in this post: REQUIRED “Texts”: Video Tutorials from Udemy

Your input may help ensure others get the sale price for the titles, or tackle some other unexpected challenge. Thus, I’m requiring everyone to post something — whether a celebration, question, a reflection, or feedback to other users.

Here are recommended ideas to prompt your contributions:

  • Was it difficult to find the sale price for the required titles?
    • Remember: If you don’t see the sale price ($25 or less per title) at first, try refreshing the page, or leaving the site and coming back later, or search online for a Udemy coupon.
    • What worked for you? Do you have tips for others?
  • Did you run into any problems in the purchase process?
  • Did you discover any helpful tips in this process? Please share it.

1.3 Mini-Project: Turn on File Name Extensions

This is a quick thing, but it can make a nice difference for you this semester and in the future!

We will be working with files a lot. There will be many times when you’ll want to see not just the file’s name, but the type of file it is, which is revealed by the extension at the end. For example:

  • filename.docx (a Word document)
  • filename.xlsx (an Excel document)
  • filename.csv (a comma-separated values document)
  • filename.sql (an SQL query)
  • filename.cs (a C# program)
  • and so on

Unfortunately, most of our operating systems don’t show this by default. So you’ll have to turn on this feature.

Below are easy instructions for doing this in Windows 10 and Mac OS X.

What to Submit

When you’re done, take a screenshot of your File Explorer (Windows) or Finder (Mac) window, showing some of your files with their file extensions.

Upload and submit the screenshot image!

If you need help with taking screenshots, I’ve created this help page for you, with videos and/or text instructions for both Windows and Mac:

HOW TO: Take a Screenshot

Windows 10 Instructions

  • Open File Explorer

2.Select the View tab. Check the box for “File name extension.”

Mac OS X Instructions

  • Open Finder. In the Finder top menu, select Finder > Preferences …
  • In the Finder Preferences window, select the Advanced tab. Then check “Show all filename extensions.

1.4 Project: Anaconda Installation and Setup

Required Learning Resources

Data Analysis with Pandas and Python – Udemy Course (Links to an external site.)
https://www.udemy.com/course/data-analysis-with-pandas/ (Links to an external site.)
NOTE: I’ll refer to this resource as “Python Pandas”.

Section 1: Installation and Setup

    • 1. Introduction …
      • BE SURE TO download the resource files.
    • 2. About Me
      • Always worth knowing something about who’s speaking.
    • All videos relevant to Installing and Setting Up Anaconda for your OS (Mac or Windows), including:
      • Download the Anaconda Distribution
      • Install Anaconda Distribution
      • Create conda Environment and install pandas and Jupyter notebook
      • Unpack Course Materials + The Start and Shutdown Process
    • 13. Intro to the Jupyter Notebook Interface
    • 14. Cell Types and Cell Modes in Jupyter Notebook
    • 15. Code Cell Execution in Jupyter Notebook
    • 16. Popular Keyboard Shortcuts in Jupyter Notebook
    • 17. Import Libraries into Jupyter Notebook

Instructions

Part 1. Follow the assigned video tutorials to accomplish the following:

  • Learn about the tutorial series
  • Download the resource files (sample data files) the author provides with the Udemy title (see video 1).
  • Install Anaconda on your system.
  • Set up an Anaconda environmentbut do it slightly differently:
    • Instead of naming it as the course author does, set it up for our courses together.
    • Name it something like: Newman_Analytics
  • Set up a Folder Structure for our course. I’d recommend doing it like this — which is slightly different than the author’s instructions.
    • A folder for this course and all its materials, perhaps named: Data Programming
    • A folder for our code projects, perhaps named: Code Projects
    • A folder for the Python Pandas projects related to this Udemy title, perhaps named: PythonPandas
    • The resources file provided with the course, renamed from pandas to data.
      NOTE: Contrary to the Udemy author, I highly recommend having your data files organized within a folder named data.
      This makes our file structure much easier to scan as we create jupyter notebook files. This structure is very common in our field.
    • Thus, it should look something like this:

  • Practice the startup and shutdown process.
  • Practice using Jupyter Notebooks and get familiar with how it works.
  • Practice importing libraries into a Jupyter Notebook.

What to Submit

  • Take two screenshots:
    • A screenshot of your Jupyter notebook in which you’ve imported the libraries.
    • A screenshot of your project folder.
  • Upload and submit both screenshots.
  • If desired, use the text entry field to add any comments you’d like me to see.

Then, don’t forget to contribute to the discussion for this project.

UDEMY ACCOUNT;

Email: gallaf.oussama@gmail.com

Password: 1702Oussama@

1.4 Discussion: Installing Anaconda

This discussion is for notes, tips, and questions about installing and setting up the Anaconda Distribution of Python on your own computer.

For instance, sometimes there are minor things that have changed since the recording of the installation videos, and we can note those here.

Thus, I’m requiring everyone to post something — whether a celebration, question, a reflection, or feedback to other users.

Here are recommended ideas to prompt your contributions:

  • Was anything in the process different from the recorded video instruction?
  • Did you run into any problems in the installation process?
  • What about the setup process?
  • Do you have any questions you’d like to ask for clarification?
  • Did you discover any helpful tips? Please share

1.5 Project: First Jupyter Notebooks

Required Learning Resources

I’ll refer to this resource as “Python Pandas”:

Data Analysis with Pandas and Python – Udemy Course
https://www.udemy.com/course/data-analysis-with-pandas/ (Links to an external site.)

Section 2: BONUS: Python Crash Course

    • 18. Intro to the Python Crash Course
    • 19. Comments
    • 20. Basic Data Types
    • 21. Operators
    • 22. Variables
    • 23. Built-in Functions

Prepare a Folder Structure for this Course

  • Create a folder for this course. Name it something like: Data_Programming. (It’s helpful to avoid spaces in file and folder names when we’re programming, so I recommend using an underscore or camel case. Here I’ve used both underscores and camel case.)
  • If you have not already done so, then download and unzip the Resource files provided with the Udemy course. (See Section 1, video 1.)
  • Change the name of the downloaded resources folder from pandas to data. (I prefer this to the course author’s approach.)
  • Place the renamed data folder inside your Pandas folder.

Your resulting folder structure should be organized like this:

  • Data_Programming – The top folder which will contain all your work for this course.
    • PythonPandas – a child folder (inside your course folder), which will contain your work related to this Udemy course.
      • data – containing the data files provided with this Udemy course

Here is a view of the folders and files, using the Mac Finder:

Note: Starting Up Jupyter Notebook

There are 2-3 good ways to start up Jupyter Notebook on your system:

1. Use the Command Line

2. Use the Anaconda Navigator to launch Jupyter Notebooks.

This saves the step of opening the Windows Command Line or Mac Terminal.

Windows users will also find a shortcut to launch Jupyter Notebooks under Anaconda3 in the Start Menu .

Now Follow the Project Guide

To complete this assignment, download and follow this project guide:

First Jupyter Notebooks Guide.docx

1.5 Discussion: First Jupyter Notebooks

Use this discussion board to share tips, recommendations, and discussion regarding the assigned project(s).

To foster dialogue, I’m requiring everyone to post something — whether a question, a reflection, or feedback to other users. Here are recommended ideas to prompt your contributions:

  • Have you gotten stuck? You’re undoubtedly not alone. Please describe your problem and include screenshots if relevant.
  • Can you provide help in response to another student’s question? If so, please do!
  • Did you discover any helpful tips in this process? Please share it. Provide relevant screenshots, links, etc.

1.6 Project: Second Jupyter Notebooks

Overview

Continue where you left off in the First Jupyter Notebooks project and continue your learning curve with Python fundamentals.

Required Learning Resources

Python Pandas (aka Data Analysis with Pandas and Python – Udemy Course)
https://www.udemy.com/course/data-analysis-with-pandas/ (Links to an external site.)

Required Videos

    • 24. Custom Functions
    • 25. String Methods
    • 26. Lists
    • 27. Index Positions and Slicing
    • 28. Dictionaries

Project Guide

Download and follow the project guide to finish this assignment:

Second Jupyter Notebooks Guide.docx

 

1.6 Discussion: Second Jupyter Notebooks

Use this discussion board to share tips, recommendations, and discussion regarding the assigned project(s).

To foster dialogue, I’m requiring everyone to post something — whether a question, a reflection, or feedback to other users. Here are recommended ideas to prompt your contributions:

  • Have you gotten stuck? You’re undoubtedly not alone. Please describe your problem and include screenshots if relevant.
  • Can you provide help in response to another student’s question? If so, please do!
  • Did you discover any helpful tips in this process? Please share it. Provide relevant screenshots, links, etc.

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