The Maryland Smith Python Programming for Business course is designed for professionals who recognize technology has drastically transformed how organizations use data to support their daily and strategic decision-making. The goal is to help you better communicate with programmers and data analysts, realizing this communication is no longer limited to individuals in technology roles.
You will experience a learning-by-doing atmosphere allowing you to see how Python is used to analyze data and solve business problems. No prior coding experience is expected! Starting from the beginning and throughout the course, you will see many examples and practice problems aimed at helping you internalize and apply the concepts you are learning.
Why take this course?
The mission of this course is to equip participants to:
- Describe the value of programming to solve business problems.
- Determine computing logic necessary to convert manual solutions to programmatic solutions.
- Write Python code to process and analyze data for data-driven decision-making.
- Communicate technical concepts to technical and non-technical staff at multiple organizational levels.
During this module, you will gain an overview of the power of Python programming. A gentle, hands-on, introductory approach to programming fundamentals will jump-start your problem-solving mindset and ability to communicate with other technical and non-technical professionals.
During this module, you will teach a computer how to make decisions using selection control structures and repeat a set of instructions (or logic) using repetition control structures. With more hands-on programming experience, your problem-solving mindset will continue growing.
During this module, you will build functions to promote code reuse and work with several built-in Python data structures. Good programmers appreciate reusable code. Additionally, programmers rely on data structures, or containers, to store data. You will understand and be able to communicate the similarities and differences between Python’s built-in data structures.
During this module, you will analyze numerical data with NumPy, a popular Python library for numerical analysis. Whether it’s financial data, such as stock quotes, or multi-dimensional data, you will be able to perform basic processing and derive insights on large amounts of numerical data.
During this module, you will add to your analytical toolkit by analyzing mixed data containing numbers and strings with Pandas, another popular Python library. Whether it’s analyzing weekly sales data or gathering insights from product reviews, you will be able to perform basic processing and derive trends using data tables and data collected over time.
During this module, you will be exposed to practical considerations you will face when working with real data sets, enabling you to address inaccurate and missing data. This will facilitate further techniques, such as categorization, for determining business insights.
Format & Structure
The course is completely self-paced. It will take you approximately 50 hours to complete all six modules. Activities include video lessons, readings, and self-reflection activities. Upon completing this course, you will receive a certificate of completion:
This is an asynchronous, self-paced course. That means that you can work at your own speed. There are review questions after most videos and readings. These questions are ungraded and designed for you to check your understanding of the content before moving on.
Executive Education Cancellation and Refund Policy
Virtual or in-person workshops with synchronous (real-time) delivery of content:
- Cancellation requests must be submitted in writing at least seven days before the program start date to receive a full refund. No refunds are provided within seven days of the program start date, but participants may defer their enrollment to another session of the same program offered later.
Online courses with asynchronous (self-paced) delivery of content:
- Online course purchases may be refunded within 14 days of purchase if the learner has not accessed the course.