3250B - Using Python II: Clean, Predict and Inform
Course Description
If you already know how to use Python to organize and analyze data, this second-level micro course is for you. You’ll learn to apply data cleansing techniques and inferential statistical methods. You’ll develop a clearer understanding of how predictive models can support more informed business decisions, and create one using the insights you pulled. You’ll explore the fascinating world of data visualization and machine learning and emerge with greater confidence in the stories you tell and the data they’re based on.
Within 4-6 weeks of successfully completing this course, you will receive your micro-credential indicating achievement of the outlined learning outcomes and competencies/skills. Micro-credentials are tamper proof, verifiable, blockchain-based and 100% digital. They can be shared on social media, including LinkedIn and Facebook, embedded in websites or downloaded as PDFs.
Learning Outcomes
By the end of this micro course, you'll be able to:
- Apply data cleansing techniques to your dataframe.
- Apply inferential statistical methods.
- Build a basic predictive model and draw insights.
Competencies/skills developed in this micro course include:
- Cleaning Data
- Exploring Data
- Predictive Modeling
- Drawing Inferences from Data
- Assessing Data Quality
- Data Privacy
- Data Security
Notes
Eligible learners may apply to the Ontario Student Assistance Program (OSAP) for this micro-credential. You can find more information on our Financial Aid page.Recommendations
3250A Using Python I: Organize and Analyze DataThis course may be applied towards the SCS Certificate(s) in
- Data Science : Required Courses
Reena Shaw has six years' experience across data and analytics, with roles spanning the Supply Chain, Logistics, Marketing and Foot Traffic sectors. Her experience as a Data Scientist, Data Analyst, as well as two years of teaching experience has honed a talent for lucidly explaining technical concepts to a non-technical audience, creative lesson plans, her solution-based approach, and for creating personalized approaches to learning. Reena is a graduate of Queen's University's M.Sc (Computer Science) program with a Thesis in Deep Learning.