About This Online Course
Continue your learning path in artificial intelligence, machine learning and data science techniques by getting acclimated to using Python for data analysis.
In this online training course from statistics.com, you will gain knowledge about Python, a general-purpose programming language that’s powerful, easy to learn and fast to code. Python is rapidly becoming the language of choice for scientists and researchers of “all stripes.” Python code can be written like a traditional program to execute an entire series of instructions at once or it can also be executed line by line or block by block, making it perfect for working with data interactively.
After you successfully finish this online course, you will be able to read and write data, group, aggregate, merge and join data frames, create effective visualizations for your customers (internal or external) and more. And you will be well positioned to take more advanced courses in Python.
There is no required text for this course and all materials will be provided online. If you are interested in a reference, Python for Data Analysis (O’Reilly Media, 2012), is recommended.
What You Will Learn
- Construct conditional statements and loops
- Work with strings, lists, dictionaries, and variables
- Read and write data
- Use Pandas for data analysis
- Group, aggregate, merge and join
- Handle time series and data frames
- Use matplotlib for visualization
- Create format, and output figures
Dr. David Masad is a consultant specializing in computational social science. He uses agent-based modeling, data science, network analysis and other computational techniques to study complex social systems. Dr. Masad has been a statistics.com instructor since 2013.
Dr. Masad received his Doctorate from George Mason University, Virginia, and his Bachelor of Arts from the University of Chicago, Illinois.
Who Should Take This Course
This course is designed for data scientists, statisticians and software engineers who need to use Python for data analytics, including web scraping, pulling data, data cleaning, data preparation and data analysis.
You should have familiarity with programming, even if it is not programming. Newcomers to programing can consider taking the statistics.com course, Introduction to Python Programming.
After finishing this course, you can continue your learning path in AI, machine learning and data science by moving to the statistics.com predictive analytics series, starting with Predictive Analytics 1 – Machine Learning Tools Using Python.
A record of completion will be issued, along with professional development credits in the form of continuing education units upon 50-percent completion.
In addition, a Credly badge to add to your LinkedIn profile will be issued upon 80-percent completion of this online training course.
This self-paced, online training course takes place at The Institute for Statistics Education at statistics.com for four weeks. During each session week, you can participate at times of your own choosing—there are no set times for the lessons. Participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.
At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.
$599 (per person)
Register through FedLearn using the special promo code FedLearn22 and receive a five-percent discount on the original online course price.
Continuing Education Unit Credits
This online course provides 5.0 CEUs upon 50-percent completion.
This course is also recommended for 3.0 upper division college credits by the American Council on Education upon 80-percent completion.