Data Analytics Bootcamps Montreal

In this program, participants will learn the key skills and tools needed to succeed as a data analyst, including Excel, SQL, and Tableau. Attendees will learn how to use Excel to create graphs, summarize data with PivotTables, and analyze data. In SQL, attendees will learn the essentials of relational databases, queries, functions, and more. Finally, participants will learn Tableau to create visualizations to express their data. The program is open to beginners and has flexible scheduling options. Tuition includes hands-on training, a free retake, and training manuals for each course.

Learn to problem solve, and effectively communicate, like an analyst. This course teaches you to use industry-standard tools to make ethical, data-driven decisions. Experience hands-on training to master SQL, Excel, Tableau, PowerBI, and Python – tools listed in virtually every data analytics job posting across industries.

Data Analytics Flex is a 6-month part-time program designed to get you a job as a data analyst. You will begin by learning how to master Excel and storytell with Powerpoint. Afterwards, you'll learn how to query data using SQL and visualize data using Tableau. Once you've mastered these building blocks, you'll receive business case studies to simulate real-world work projects and you'll also be exposed to statistics. Finally, you'll learn how to clean data using Python and work on your capstone project. This program comes with a tuition backed guarantee if you don't land a job within 6 months of graduation.

In this 8-day intensive Data Analytics bootcamp, students with little to no background in data analytics will learn how to use Microsoft Excel, SQL, and Tableau to summarize, display, and discover key insights hidden in data sets. By the end of this bootcamp, students will be able to use Excel to aggregate and summarize data, sort data with PivotTables, create charts and tables in Excel, use SQL to filter and sort large data sets, use advanced database queries, create visualizations with Tableau, and much more. Students will also be able to reinforce what they learn through the numerous projects they encounter throughout the course. This course also offers a free retake within six months to allow students to revisit any concepts needing to be refreshed and walk away with a much stronger grasp of the material.

Explore the world of data analytics in this intensive six-week bootcamp. The ability to extract and apply data makes you a valuable team member. Specific course topics include addressing data concerns, determining data collection methods, ensuring data quality, gaining insights from existing data, maximizing tools like Tableau and Excel, and publishing results.

If you want to become a data scientist, you should enroll in this intensive twelve-week bootcamp. Participants learn how to extract data from multiple sources to empower decision-makers. Specific course topics include Python, GitHub, and other data foundations, matplotlib, Seaborn, and other data visualization tools, Flask, Docker, and other data engineering platforms, data wrangling with JSON, SQL, and XML, machine learning models, and deep learning techniques.

This nine-week bootcamp gives participants the skills they need to join data science teams or accelerate their current career growth. Learn how to convert raw data into insightful information. Specific course topics include understanding basic statistical theories, using Python to write code, using seaborn and matplotlib to visualize data, using SQL to develop databases, publishing findings, data processing, using performance metrics, overfitting, unsupervised learning, managing images and text, understanding neural networks, developing computer vision, machine learning, and a capstone project.

Alter your career trajectory in this thirty-week bootcamp that leads to a data science diploma. Through a combination of live instruction, individual and group projects, and class discussions, participants explore topics like Python fundamentals, statistical and arithmetical analysis, machine learning, data engineering, and business data science.