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Data Science Certificate

Unlock the power of data.

Develop the skills you need to thrive in a growing profession.

Four-part certificate program
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This four-course program, developed in partnership with the School of Continuing Studies at the University of Toronto, gives you the opportunity to learn from experts about advanced statistical modelling, machine learning, and big data tools. You will cover content essential for the toolbox of a predictive analytics professional, including neural networks and deep learning; programming languages and software used in data extraction and analysis; and data security, compliance, and privacy issues.

The course content covers the seven domains of INFORMS' Certified Analytics Professional (CAP) certification. You will also learn how to represent data visually to help decision-makers understand your findings. Whether you work in operations, business intelligence or marketing communications, are a recent math or science graduate, or want to change or advance your career, this certificate will open up the in-demand field of big data.

 

  • Explore the evolution of data science and predictive analytics.
  • Know statistical concepts and techniques including regression, correlation, and clustering.
  • Apply data management systems and technologies that reflect concern for security and privacy.
  • Adopt techniques and technologies including data mining, neural network mapping, and machine learning.
  • Represent big data findings visually to aid decision-makers.

Course 1: Foundations of Data Science

 
  • Anaconda and Jupyter Notebooks: Master the 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.
  • Introduction to Python: Learn the basics of Python, the versatile programming language that powers data science.
  • NumPy for Data Analysis: Dive into NumPy, the powerful library for numerical computing and handling large arrays efficiently.
  • Pandas for Data Manipulation: Harness the power of Pandas to clean, manipulate, and analyze data with ease.
  • Data Collection and Cleaning: Acquire techniques for gathering and cleaning data to ensure accuracy and reliability.
  • Descriptive Statistics and Data Visualization: Understand and present data effectively using descriptive statistics and visualization techniques.
  • Time Series Analysis and Forecasting: Gain insights into time series data and learn forecasting methods for predictive analytics.
  • Introduction to Machine Learning: Get introduced to the fundamentals of machine learning and its applications in data science.
  • Databases, Analysis Tools, and Data Privacy: Learn about database management, essential analysis tools, and best practices for ensuring data privacy.

Course 2: Statistics for Data Science
  • Probability: Understand and apply the fundamental concepts of probability, including calculating and interpreting probabilities in various contexts.
  • Distribution of Random Variables: Gain insights into different types of random variables and their distributions, and learn to describe and analyze their behavior using probability distribution functions, expected values, and variances.
  • Introduction to Statistical Inference: Explore the principles of statistical inference, including estimation and confidence intervals, and learn to draw conclusions from data to make predictions.
  • Hypothesis Testing: Understand the process of hypothesis testing, including formulating hypotheses, conducting tests, and interpreting results to make informed decisions.
  • ANOVA, Goodness of Fit, and Bootstrapping: Gain knowledge of analysis of variance (ANOVA) techniques, goodness of fit tests, and bootstrapping methods to compare multiple groups, test for distributional assumptions, and improve the reliability of statistical estimates.
  • Linear Regression: Learn the fundamentals of linear regression analysis, including model building, interpretation, and application to predict relationships between variables.
  • Logistic Regression: Understand logistic regression techniques for modeling binary outcome variables, and learn to interpret and apply these models in various fields.
  • Time Series: Gain insights into time series analysis and forecasting methods, including identifying patterns, trends, and seasonal effects in time-ordered data. 
  • Introduction to Causal Inference: Explore the basics of causal inference, including methods to determine causality, and learn to apply these techniques to establish cause-and-effect relationships in data.

Course 3: Machine Learning
  • Introduction to Machine Learning: Understand the foundational concepts and practical applications of machine learning.
  • The Machine Learning Process: A Worked Example: Follow a practical example to grasp the complete machine learning workflow.
  • Classification: Learn techniques for categorizing data into predefined classes.
  • Unsupervised Learning and Clustering: Discover methods for identifying patterns and groupings in unlabeled data.
  • Training Models and Feature Selection: Explore how to train machine learning models and select the most relevant features.
  • Dimensionality Reduction: Master techniques to reduce the number of variables while preserving essential information.
  • Support Vector Machines (SVM): Understand the workings of SVMs for effective classification and regression tasks.
  • Introduction to Neural Networks and Keras: Gain insights into neural networks and how to implement them using Keras.
  • Optimizing Deep Neural Networks: Learn strategies for improving the performance of deep neural networks.
  • Using TensorFlow Interactively and Using Custom Functions: Explore interactive use of TensorFlow and how to create custom functions for advanced tasks.

Course 4: Big Data Management Systems and Tools
  • Reliable and Scalable Data-Intensive Applications: Learn to design applications that handle large volumes of data reliably and efficiently.
  • Relational Databases: Understand the principles and uses of traditional relational database systems.
  • NoSQL: Explore non-relational databases for flexible and scalable data management.
  • Distributed Datastores: Discover how to manage data across multiple servers for high availability and performance.
  • Introduction to Spark: Get introduced to Apache Spark for fast, in-memory data processing.
  • Analytics with Spark: Learn to perform large-scale data analysis using Apache Spark.
  • Spark Streaming: Understand real-time data processing with Spark Streaming.
  • Data in Motion: Explore the concepts of data streams and real-time analytics.
  • Big Data Architecture: Gain insights into designing and implementing big data solutions.
  • Cloud Analytics: Learn how to leverage cloud platforms for scalable big data analytics.
  • MLOps: Discover best practices for managing machine learning operations and deployments.

The certificate program is comprised of four courses, designed and developed to be taken in order for natural progressive learning. These four courses are 12 weeks in length each and they offer a fully online learning experience, with instructor support, weekly webinars, peer networking, and group assignments. It is designed for working professionals who need to fit their learning around a busy schedule. As such, there is no requirement to attend synchronous sessions and you can work on them at a time that is convenient for you to complete the assignments.

You will complete weekly text-based modules in Jupyter notebooks and complete practical assignments (individual and group), which provide opportunities to practice coding and working with data as you learn. In addition, there are recorded presentations by guest lecturers, who talk about current issues in the field of data science. You will showcase your learning by completing assignments and major projects that are relevant to tasks you need to master as a data science professional.

Note: Students need to commit 8 - 12 hours per week for each of the Data Science courses. You have three years from the start date of your first course to complete the certificate.

It is recommended that you the courses in the following order:

1. Foundations of Data Science

 

2. Statistics for Data Science

 

3. Machine Learning

 

4. Big Data Management Systems and Tools

 

Interested in taking a graduate program?

The Master of Data Science and Artificial Intelligence is a coursework program designed to meet the growing global demand in the fields of Data Science and Artificial Intelligence. Currently, the two program options include full-time co-op and part-time regular (non-co-op).

Learn about the Program and admissions

 

Receive a certificate from the University of Waterloo

Upon successful completion of all four courses, you will receive a professional education certificate from the University of Waterloo.

  • 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.

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

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.

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Hear from our learners

The Data Science program provided me with the foundation of data sciences and laid the groundwork to build on my acquired skills. As an oil and gas professional, I enrolled in this program to expand my hard skills and enable my pivot to the tech industry. I found the program to be very practical, with lots of content and teaching support. The final group projects in every course also led me to expand my network.

Yasser Khan | Data Lead, 303 Sparky
Data Science Certificate Program