| Program length |
Format |
Program fees |
|---|
48 weeks
This program includes four 12-week courses |
Asynchronous learning with live recorded sessions |
$995 per course |
This four-course program, developed in partnership with the School of Continuing Studies at the University of Toronto, will give you the opportunity to learn from experts about advanced statistical modelling, machine learning and big data tools. You'll 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'll 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.
Register now and receive a 50 per cent instant rebate!
If you are a professional who is currently employed and reside in Canada, you may be eligible for a 50 per cent instant rebate (course fee only, taxes excluded) offered by Scale AI. Rebate must be applied at the time of registration (cannot be applied retroactively). For more information about elegibility, visit the program registration page.
Who should enrol
- 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
What you will learn
- 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.
Program lead
Larry Simon, MBA
Mr. 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 a 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.
Courses
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.
Please take the courses in the following order:
Foundations of Data Science | 12-week course | Next start date: September 26, 2022
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'll learn how to help organizations leverage the increasing variety and volume of data they own or can find on the Internet. You'll explore the evolution of the fields of data science and predictive analytics. You'll learn up-to-date techniques for data retrieval, preparation, analysis and visualization. Through hands-on exercises and group assignments, you'll build critical programming skills you can use in subsequent courses in the certificate program.
The courses in this certificate program cover most of the body of knowledge of INFORM's Certified Analytics Professional (CAP) Certification.
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.
What you'll learn
- Understand the basic techniques, evolution and promise of data science.
- Build hands-on skills in programming languages such as Python and SQL.
- Extract data from databases, websites and social media.
- Store, clean and analyse data using Pandas.
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 6 week online course.
System requirements
Statistics for Data Science | 12-week course | Next start date: September 26, 2022
Build on the skills you learned in Foundations of Data Science and get the grounding in basic statistics you need to successfully complete your Data Science Certificate. In this course, you'll explore probability and descriptive statistics, cover data analysis from both a classical and contemporary viewpoint and learn to extract insight from datasets. You'll get a math notation refresher, learn to form hypotheses, design experiments that collect valid data, test your hypotheses using statistical techniques and work with causal models. Concepts are solidified through individual, and group based practical assignments.
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.
What you'll learn
- Design experiments to collect data that answer business or scientific questions.
- Formulate statistical tests to answer business questions.
- Use multiple linear and logistic regressions as predictive models.
- Use Python for model building.
- Cover data analysis from both a classical and causal viewpoint.
Prerequisites
- Foundations of Data Science or a passing grade on prior learning assessment for equivalent skills
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.
System requirements
Machine Learning | 12-week course | Next start date: September 26, 2022
This course will equip you with the basic machine learning and artificial intelligence (AI) tools for mining datasets, and extracting insights for decision making. You will learn how to identify correlations and patterns in datasets, build more sophisticated predictive models using machine learning and deep learning software and evaluate the performance of those models through individual and group based practical assignments.
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.
What you'll learn
- Find correlations between variables in a dataset.
- Identify clusters in data such as market segments.
- Predict future outcomes based on hidden relationships in historical data.
- Use tools to classify events or observations by type.
- Evaluate and combine models for best performance.
Prerequisites
- Completed Foundations of Data Science and Statistics for Data Science or,
- Completed or been granted a passing grade on a Prior Learning Assessment (PLA) for Foundations of Data Science and Statistics for Data Science
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.
System requirements
Big Data Management Systems and Tools | 12-week course | Next start date: September 26, 2022
Big Data involves massive data volumes and diverse data types. Modern organizations need people who can help implement the tools they need to deal with these huge data sets. In this course, you’ll learn the technology of Big Data and build in-demand skills. You’ll get hands-on experience using big data analysis tools (with and emphasis on Spark), distributed database management systems such as MongoDB and practice the concepts presented through individual and group assignments. Get in on the explosion in new NoSQL technologies and Big Data tools.
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.
What you'll learn
- Understand the architecture of reliable Big Data systems.
- Know how they differ from the traditional way of building systems.
- Describe when to use different types of NoSQL databases.
- Address current processing challenges of Big Data.
- Analyze data using tools like Spark and big data databases such as MongoDB.
Prerequisites
- Foundations of Data Science or a passing grade on Prior Learning Assessment for equivalent skills
- Machine Learning or a passing grade on a Prior Learning Assessment for equivalent skills
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.
System requirements
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).
Your learning experience with Waterloo
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Taxation
Online Data Science courses taken through WatSPEED at the University of Waterloo will have applicable taxes applied at the time of purchase. A T2022A tax slip is not issued for these courses.
Withdrawal and refund policy
You can withdraw from this course, or transfer to the next start date, up to 14 days after the start date. You must request this in writing (watspeed@uwaterloo.ca) before 4:30 pm (EST) on the 14th day. Please indicate if you want a refund or a transfer. There is an administrative fee of $75 (+ applicable taxes) for either option.
