Practitioner Level
4 Hands-on labs

Using Python for Data Management and Reporting

By Larry Fritts

Get a look at accessing data in common databases, explore Excel operations, and learn about simple report formatting and data visualization.

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6 hours
  • 28 Lessons
  • 4 Hands-On Labs

About the course

Data abounds in our world. It is collected in various forms, various ways, and various qualities. It is collected for a multitude of purposes, including collecting for some undefined future use. Being able to access that data and make some sense of it is an important skill for now and the future.

Python is an excellent resource for exploring data and making it usable for a variety of purposes. Using Python and Jupyter Notebooks, we will look at accessing data in some common databases, such as MySQL, PostgreSQL, and MongoDB. We will explore Excel operations, such as writing and reading to Excel. We will also explore simple report formatting and data visualization.

Lab Highlights

Common Operations on a PostgreSQL Database Generate a Complete Report Simple pandas Operations In Jupyter Notebook Use pandas DataFrames on Excel Data
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  • Chapter 1 3 Lessons Overview 4:18

    An Important Note About A Cloud Guru and Linux Academy Courses

    1:19

    About This Course

    2:15

    About the Author

    0:44
  • Chapter 2 5 Lessons Using Jupyter Notebooks 42:48

    Installing Jupyter Notebooks, Opening a Notebook, and Setting the Kernel, Part 1

    4:20

    Installing Jupyter Notebooks, Opening a Notebook, and Setting the Kernel, Part 2

    15:04

    Installing Jupyter Notebooks, Opening a Notebook, and Setting the Kernel, Part 3

    6:01

    Using and Evaluating Both Code and Markdown Cells, Part 1

    8:55

    Using and Evaluating Both Code and Markdown Cells, Part 2

    8:28
  • Chapter 3 6 Lessons panda DataFrames in Jupyter Notebooks 1:17:33

    Creating a panda DataFrame and Examining Its Properties, Part 1

    6:44

    Creating a panda DataFrame and Examining Its Properties, Part 2

    7:17

    Slicing and Other Operations on panda DataFrames, Part 1

    6:12

    Slicing and Other Operations on panda DataFrames, Part 2

    5:15

    Slicing and Other Operations on panda DataFrames, Part 3

    7:05

    Simple pandas Operations in Jupyter Notebook

    45:00 Hands-On Lab
  • Chapter 4 5 Lessons SQL Python Database Packages 1:23:57

    PEP 249 and Python Database Packages

    6:16

    Connecting to and Operations on a Database, Part 1

    4:44

    Connecting to and Operations on a Database, Part 2

    10:57

    Connecting to and Operations on a Database, Part 3

    2:00

    Common Operations on a PostgreSQL Database

    1:00:00 Hands-On Lab
  • Chapter 5 1 Lesson MongoDB Package: pymongo 9:57

    Using Python MongoDB Package - pymongo

    9:57
  • Chapter 6 2 Lessons Delimited Data Text Files 9:02

    What Are Delimited Data Files

    4:34

    Reading and Writing CSV Files as pandas DataFrames

    4:28
  • Chapter 7 2 Lessons Excel Data Files 37:17

    Using pandas DataFrames with Excel

    7:17

    Use pandas DataFrames on Excel Data

    30:00 Hands-On Lab
  • Chapter 8 3 Lessons Writing the Report 1:53:33

    Brief Overview of Adding LaTeX to a Report

    5:56

    Good Charting Practices and Review of a Complete Report

    17:37

    Generate a Complete Report

    1:30:00 Hands-On Lab
  • Chapter 9 1 Lesson Conclusion 2:12

    What's Next

    2:12

What you will need

  • Basic Python skills would be helpful and will aid in understanding the material presented.

What are Hands-on Labs

What's the difference between theoretical knowledge and real skills? Practical real-world experience. That's where Hands-on Labs come in! Hands-on Labs are guided, interactive experiences that help you learn and practice real-world scenarios in real cloud environments. Hands-on Labs are seamlessly integrated in courses, so you can learn by doing.

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