Python for Data Science & Analytics
Description
This introductory course provides a gentle introduction to programming in Python and its applications in the world of big data. As organizations continue to digitize and generate increasingly large amounts of data, open-source tools like Python can enable data manipulation and exploration, machine learning, and statistical analysis in a scalable manner. A basic understanding of Python enables businesses to take advantage of available data to drive business strategy and value.
This 15-hour course uses practical, hands-on approaches to learning Python programming and foundational data science libraries (Pandas, NumPy and Matplotlib) to explore, manipulate, and visualize data. Learners will gain practical experience in attaining insights from data. In addition to lectures, discussions, and practical instruction, you'll explore key topics through group exercises and assignments.
This 15-hour course uses practical, hands-on approaches to learning Python programming and foundational data science libraries (Pandas, NumPy and Matplotlib) to explore, manipulate, and visualize data. Learners will gain practical experience in attaining insights from data. In addition to lectures, discussions, and practical instruction, you'll explore key topics through group exercises and assignments.
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.
Overview
- Institution: University of Toronto
- Level: University
- Language: English
- Course Code: 3968
- Delivery Method: Fully Online/Distance
Disclaimer:
Check with the institution regarding start/end dates, prices, and delivery method. These may vary according to program, section, and/or semester.
Check with the institution regarding start/end dates, prices, and delivery method. These may vary according to program, section, and/or semester.