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There are no scheduled classes for this course. If you are interested in purchasing this course, please click on the "Express Interest" button, fill in your contact information, and submit a request for the course.Data Automation Mentoring Program
- Course Length:
- 9 weeks
The Data Automation Mentoring is designed for non-programmers who want to create programs in Python to help them automate some of their mundane daily tasks related to gathering and analyzing data. By using hands-on, lab-based programming exercises and a mix of live sessions and programming assignments, it provides an opportunity to the student to define and develop a Python program based on a practical and relevant use case. [Live Session: 1/2 day every week], [One-on-One Mentoring: 1 hour each week], [Self-Study: Python program development, approximately 6 hours average each week]
This workshop is intended for anyone (non-programmers) who wants to build knowledge and skills related to leveraging data tools to be more productive.
After completing this course, the student will be able to:
■ Analyze a problem and design step-by-step ways to automate the task at hand
■ Learn how to manage data in different forms of data structures to load and manipulate data
■ How to use key control structures to manage the process flow
■ Implement solutions based on string manipulation, regular expression processing and loops
■ Implement a data processing exercise using control and data structures including file operations
■ Implement text file and Excel file handling for Input/Output processing
■ Learn how to automate data collection through APIs
■ Python is used as the programming language for all exercises and lab-work
1. Fundamentals of PYTHON 1
1.1 Create and run a program
1.2 int, str, float, print()
1.3 Import - os, sys
Exercise: Program Development Assignment
2. Fundamentals of PYTHON 2
2.1 File operations
2.2 for, if/elif/else, lists, sys.argv
2.3 try, except
Exercise: Program Development Assignment
3. OPENPYXL
3.1 pip install, while
3.2 xlsx - open, create, read, write, save
3.3 chart, sys.argv, tkinter
Exercise: Program Development Assignment
4. PANDAS
4.1 pandas dataframe
4.2 load dataframe, output to xlsx
4.3 add, drop, columns, rows, analysis
Exercise: Program Development Assignment
5. Participant USE CASE - PART 1
5.1 Designing simple, maintainable scripts
5.2 Writing pseudo-code, functions, logical steps
5.3 Package installation, functions
Exercise: Program Development Assignment
6. Participant USE CASE - PART 2
6.1 Types of inputs
6.2 File-based, URL-based, API-based, SQL-based
6.3 Example of invoking an API
Exercise: Program Development Assignment
7. Participant USE CASE - PART 3
7.1 Analysis using python and/or pandas
7.2 Package specific implementations
7.3 Pros and Cons of approaches
Exercise: Program Development Assignment
8. Participant USE CASE - PART 4
8.1 Output the analysis from the USE CASE
8.2 Output format
8.3 Output visualizations
Exercise: Program Development Assignment
9. Participant USE CASE - Final Completion
9.1 Participant USE CASE submission
9.2 Participant USE CASE presentation
9.3 Participant USE CASE demonstration
9.4 Feedback and Wrap-up
