Foundations of Data Science

12 weeks8 - 12 hours per week

There was an error: {{ status.errorMessage }}

Online learningLive recorded sessions

There was an error: {{ status.errorMessage }}

If you want to work in the growing field of data science, and have some prior knowledge and experience of basic programming, this course is for you.

You will learn how to help organizations leverage the increasing variety and volume of data they own or can find on the Internet. You will explore the evolution of the fields of data science and predictive analytics. You will learn up-to-date techniques for data retrieval, preparation, analysis and visualization. Through hands-on exercises and group assignments, you will build critical programming skills you can use in subsequent courses in the certificate program.

This course is developed in partnership with the School of Continuing Studies at the University of Toronto.

NOTE: The courses in this certificate program cover most of the body of knowledge of INFORM's Certified Analytics Professional (CAP) Certification.

Module 1: Introduction	Anaconda and Jupyter Notebooks: Gain proficiency in essential tools for data science with Anaconda and Jupyter, enabling a seamless and interactive coding environment. History of Data Science and Evolution of Analytics: Explore the roots and progression of data science, understanding its impact on modern analytics. Multidisciplinary Nature of Data Science: Discover how data science integrates concepts from various fields to address complex problems. Framework for Big Data Definition: Apply a structured framework to understand and define the concept of "Big Data." Predictive Modelling: Learn the foundational processes and applications of predictive modeling in data analysis. Data Mining: Explore the techniques and underlying processes used in extracting valuable insights from large datasets. Machine Learning: Get an introduction to the processes and practical applications of machine learning in data science.
Module 2: Introduction to Python	Large Language Models (LLMs): Explore how LLMs, like ChatGPT, can enhance business functions, processes, and innovations across the enterprise. Generative AI Imaging Applications: Discover how various industries are leveraging generative AI imaging to boost organizational performance and competitiveness. “Replace, Reimagine, Recombine” Model: Apply the “replace, reimagine, recombine” framework using generative AI to rethink processes, product and service innovations, and business models.
Module 3: NumPy (Numerical Python package)	Python Skill Development: Further enhance your Python skills to handle more advanced tasks. NumPy Data Analysis: Use the NumPy library to efficiently work with large arrays for data analysis.
Module 4 and 5: Pandas	Object-Oriented Programming (OOP): Learn the basics of OOP and how to implement it in Python. Advanced Python Functions: Explore the use of `map()` and `lambda` functions, along with defining function arguments. Pandas Library: Get introduced to Pandas, including Series and DataFrames, for data manipulation and analysis. Data Handling with Pandas: Learn to create, modify, and manage DataFrames, including reading from and writing to .csv files. Data Selection and Modification: Master techniques for selecting, slicing, and indexing data within DataFrames. Data Aggregation and Grouping: Understand data aggregation using `groupby()`, and techniques like `stack()/unstack()` and `reset_index()`. Pandas Case Studies: Apply your knowledge in practical case studies to reinforce your understanding of Pandas.
Module 6: Data Collection and Cleaning	Types of Data: Identify and understand the different types of data you’ll encounter. Web Data Collection: Learn how to retrieve data from the web for analysis. Data Cleaning with Pandas: Use Pandas to clean up problematic data for more accurate results. Handling Missing Data: Develop techniques to effectively manage and handle missing data.
Module 7: Descriptive Statistics and Visualization	Descriptive Statistics Concepts: Review key concepts such as mean, median, and standard deviation. Correlation Analysis: Refresh your understanding of correlation and its significance. Pandas for Descriptive Statistics: Learn to calculate descriptive statistical measures using Pandas. Classic Examples: Gain insights from classic examples to solidify your understanding. Effective Visualization Design: Pick up tips for designing impactful visualizations. Interactive Web Visualizations: Explore new technologies behind rich, interactive web visualizations. Matplotlib for Data Visualization: Use Matplotlib to create data plots and charts.
Module 8: Time Series	Forecasting Techniques: Learn how to predict future trends using advanced forecasting methods. Time Series Analysis: Understand the unique characteristics and analysis methods of time series data. Pandas for Time Series: Utilize Pandas to effectively manipulate and analyze time series data.
Module 9: Introduction to Machine Learning Part 1	Machine Learning Problems: Explore different types of machine learning challenges, including classification and regression. Supervised and Unsupervised Learning: Understand the distinction between supervised and unsupervised learning methods. Regression in Supervised Learning: Learn how regression serves as a fundamental tool in supervised machine learning. Building Prediction Models: Gain insights into the steps required to build effective prediction models. Model Evaluation: Discover techniques for evaluating the accuracy and performance of machine learning models. Statistical Meaning of Regression: Understand the statistical significance and interpretation of regression analysis.
Module 10: Introduction to Machine Learning Part 2	Probabilistic Classification: Apply a probabilistic approach to solve classification problems. Multi-Variable Logistic Regression: Explore and apply multi-variable logistic regression to real-world datasets. Support Vector Machines (SVM): Understand and utilize SVMs as a powerful family of machine learning models. Decision Trees: Dive into Decision Trees as a versatile family of machine learning algorithms. Ensemble Learning and Random Forests: Get introduced to Ensemble Learning, focusing on the Random Forest algorithm as an example.
Module 11: Databases, Analysis Tools, and Privacy	Relational Database Concept: Understand the fundamental concept of a relational database. Schemas Structure and Function: Explore the structure and function of schemas in database design. Overview of SQL Databases: Gain an overview of databases that utilize SQL for data management. History of SQL: Learn about the brief history and evolution of SQL. Writing Simple SQL Queries: Develop the ability to write basic SQL queries for data retrieval and manipulation. Graphical and Model-Building Tools: Discover tools for creating visualizations and models for modestly sized datasets. Data Privacy in Canada: Understand the data privacy requirements specific to Canada.

Business associates, operations managers, project managers, and intelligence analysts.
Finance, securities, and insurance professionals.
Digital marketing and communication specialists.
Professionals from every level or industry who work with analytics or data.

Need to refresh your skills? Check out Python I Need basic coding experience? Check out Data Analytics for Behavioural Insights

Weekly webinars review key concepts and provide an opportunity for live interaction and Q&A with the instructors. The webinars will be recorded so if you are unable to attend you can watch the webinar recording at a time that is convenient to you.

Academic requirements

A degree in Engineering, Mathematics, or Computer Science is recommended, but not required. Basic knowledge of programming and programming languages is strongly recommended.
Need to brush up on your programming skills? Take our Introduction to Python 3 six-week online course.

System requirements

Anaconda/Jupyter (software that you are required to install)
Waterloo LEARN

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.

Headshot of Larry Simon

Larry Simon, MBA

Founder and Managing Director, Inflection Group

Larry Simon is an entrepreneur, management consultant, and angel investor, specializing in IT strategy and data analytics. He has over 30 years of experience advising startups, global corporations, and government institutions. He is the founder and managing director of Inflection Group. Prior to this he was a partner with Ernst & Young Consulting, their CTO and national director of their strategy and delivery centres.

He has previously served on the faculty of the Rotman School of Management, as the head judge of the Canadian Information Productivity Awards (CIPA), and as a councillor of the Institute of Certified Management Consultants of Ontario. Larry holds an MBA from the University of Toronto and a B.Math (Computer Science) from the University of Waterloo.

Sign up to attend an upcoming workshop!

Get a brochure

*indicates a required field

First name *

Last name *

Email address *

Company / organization name *

Position / title *

Phone number

Do you provide us consent to send you information?

WatSPEED at the University of Waterloo will use the information you provide on this form to email you details, news, reminders and updates about our courses, programs, and events. To stop receiving messages from us, click the unsubscribe link in the footer of any email or contact us at watspeed@uwaterloo.ca. View our Privacy Policy for additional information.

I consent.

Hear from our learners

It was time for me to deep dive into the new world of data science and I made a perfect choice! The Data Science certificate program had very well-structured content, and the instructors were not only subject matter experts, but also had deep industry knowledge. The individual and group assignments were relevant, and I have been able to show some of those unique data science use cases in my portfolio.

Debjani Mukherjee | Consultant, Green Connect Solutions Inc.

Data Science Certificate Program