Python is the top preferred language for data science and research. Its relatively simple syntax is adaptable and user-friendly. People with little-to-no development experience can easily learn Python and use it to manipulate data for research, reporting, predictable or regression analyses, and more.
There is a high demand for professionals with more advanced Python skills in various fields such as data science, machine learning, web development, and automation. Employers are looking for candidates who can not only write basic Python code but also develop sophisticated applications.
Python 2 teaches intermediate python skills for applications in Data Science, Machine Learnine and AI. It is intended for professionals looking to advance their career in technical data-based roles or individuals looking to expand their Python toolkit to include data manipulation, analysis, and visualization.
In this course, you will learn how numpy, pandas, matplotlib, and seaborn are used to enable efficient handling and interpretation of complex data sets to drive informed decision-making and enhance productivity. Discover how to leverage Python as a powerful tool for data and visualization.
Learning Outcomes
- Understand and utilize NumPy: Learners will be able to create and manipulate ndarrays, use aggregate functions, access values via fancy/advanced indexing, masking, broadcasting, and vectorization operations.
- Apply concepts: Learners will be able to apply concepts of testing, logging, timing code, and manipulating time data, as well as forming and utilizing regular expressions
- Perform data manipulation with pandas: Learners will be able to read and write data files from JSON and csv files, perform basic data manipulations, and apply advanced pandas concepts such as updating indices, merging, joining, and reshaping data.
- Handle missing data: Learners will be able to identify and handle missing, corrupt, or regional data.
- Integrate numpy and pandas with matplotlib: Learners will be able to integrate numpy and pandas with matplotlib to create meaningful visualizations for data sets, including bar graphs, pie charts, scatter plots, and box and whisker plots.
- Utilize seaborn for data visualization: Learners will be able to create various types of plots using seaborn, including dist, joint, bar, count, box, and violin plots.
Each course module works through two fully-worked real-world examples to reinforce concepts and learning. These examples have universal applications across a wide range of industries.
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Module 1: Introduction to NumPy |
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Module 2: Advanced NumPy |
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Module 3: Other Python Modules |
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Module 4: Pandas |
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Module 5: Advanced Pandas |
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Module 6: Data Munging |
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Module 7: Graphing and Matplotlib |
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Module 8: Seaborn |
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Learners who have completed WatSPEED's Python I course or have equivalent knowledge are a perfect fit for this course. Learners interested in enrolling for WatSPEED's Data Science certificate or Python for Machine Learning can be even better prepared by taking this course.
Some other learners who should enrol are:
- Aspiring data scientists: Those looking to build a strong foundation in data manipulation and visualization, essential for data science roles.
- Data analysts: Professionals who want to enhance their skills in handling and analyzing large datasets using Python.
- Researchers: Individuals in academia or industry who need to process and visualize data for their research projects.
- Software developers: Developers who want to integrate data analysis and visualization capabilities into their applications.
- Business analysts: Professionals who need to derive insights from data to support business decisions.
- Aspiring machine learning professionals: Those preparing to transition into technical roles in machine learning who need a solid understanding of data handling and preprocessing.
- Financial analysts: Analysts who need to manipulate and visualize financial data for reporting and analysis.
- Anyone interested in Python: Individuals with a basic understanding of Python who want to advance their skills in data manipulation and visualization.
Learn alongside your team!
WatSPEED provides custom learning experiences tailored for large groups from any single organization. Register three or more employees from the same organization and receive 15 per cent off. Contact our team at watspeed@uwaterloo.ca for details.
- Exclusive access to your program author—a University of Waterloo faculty expert in computer science.
- Learn at your own pace with weekly independent online learning and hands-on exercises.
- Attend a live orientation session before your course starts to get up to speed on the curriculum.
- Optional live drop-in sessions twice weekly (time to be determined soon) via Zoom where you can ask questions and receive instructor support on key course concepts.
- Engage directly with your classmates through online discussion boards.
- Complete multiple choice quizzes each week while achieving a 70 per cent grade to complete the course. Receive immediate feedback on quizzes and interactive course components.
- The course consists of texts, visuals, videos, and interactive activities.
- Up to five hours each week (including drop-in sessions, hands-on exercises, discussion
boards, and independent study).
Academic requirements
Learners are recommended to complete our Python I course or have the equivalent knowledge before starting Python 2.
System requirements
- This course is housed in the OpenEdX platform and works best in Google Chrome or Mozilla Firefox. Safari and Microsoft Edge are not recommended.
- No software is needed to complete this course as all exercises can be completed within the course itself.
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Zoom is used for drop-in sessions.
Receive a certificate from the University of Waterloo
Upon successful completion of this program, you will receive a professional education certificate from the University of Waterloo.
Carmen Bruni
Lecturer, Cheriton School of Computer Science | Program Author and Instructor
Dr. Carmen Bruni is a faculty member at the University of Waterloo, David R. Cheriton School of Computer Science.
Before starting at Waterloo in 2015, he obtained his PhD. in Mathematics from the University of British Columbia. He completed his undergraduate and master’s degrees at Waterloo in Computer Science and Pure Mathematics.
From 2015 to 2017, he worked with Waterloo’s Centre for Education in Mathematics and Computing. Since then, he has been teaching in the School of Computer Science. Dr. Bruni has received numerous awards for teaching excellence including a prestigious Killam Graduate Teaching Award in 2013.
His current research interests are in mathematics, education, and pedagogy.