Specialized IT Courses

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IMPORTANT MESSAGE

SET YOUR CALENDARS!

ONLINE REGISTRATION for Winter 2021 Part-Time Specialized IT courses IS ONGOING!

Courses are all on a first come, first serve basis and fill up quickly! So be sure not to miss out!!!

Note : All courses are offered online

Courses

NETWORK & SECURITY

*Schedule subject to change

Linux – Operating System Details >

Course Title	Linux – Operating System
Course Number	900-003-EQ-02
Platform	Linux
Duration	40 hours
Emploi-Québec fee (taxes incl.)	$80
General Public fee (taxes incl.)	$722.50
Schedule	Thursday : 6 p.m. – 10 p.m.
Dates	January 21, 28, February 4, 11, 18, 25, March 4, 11, 18, 25
Prerequisites	Background in IT This is a beginner/intermediate level course.
Target Audience	Individuals who wish to learn how to apply multi-user operating system concepts with an emphasis on Unix.
Instructor	Oswaldo Moreno
Location	Online format

NB: This is a non-credit course. Certificate provided for all participants who have completed 80% of course hours.

Course Description

The main focus will be the application and administration of the Linux file system. Protocols and configurations dealing with security will also be studied and applied.

Topics to be covered in this course

Summarize the history and nature of Linux
Explain correctly the purpose of an operating system.
Outline correctly the basic structure of an operating system.
Outline briefly and concisely the history of Linux.
Compare correctly Linux to other operating systems.

Utilize a Linux shell
Explain correctly the kernel-shell relationship.
Enter Linux commands and their parameters successfully and appropriately at the shell prompt.
Interpret the output from Linux commands correctly.
Customize the shell environment appropriately.
Make appropriate use of shell utilities.
Create and execute shell script files correctly to run operating system commands.Install and configure a Unix/Linux system

Install correctly a default configuration of Unix system in a PC (Intel architecture).
Install correctly a Unix system in dual-boot mode (sharing hardware resources with Windows).
Explain correctly the differences between server, workstation, and custom installation of a Unix system.
Install correctly a Unix/Linux system in a virtual machine (VMware).

Install Software Packages in a Unix/Linux system
Install correctly a Software Package using RPMs.
Install correctly a Software Package using DEB files.
Automate installation of packages with yum and with apt-get.
Update packages already installed in a Unix/Linux system.
Compile packages directly from the source code.

Administer a Linux system
Complete basic administration tasks.
Create, modify, suspend, and close user accounts correctly.
Create, modify, and remove groups.
Create and administer hard disk partitions.
Administer runlevels and correctly shutdown/reboot a Unix/Linux system.
Configure the graphical (X Window) system correctly.

Implement Internet access
Implement and configure effectively TCP/IP correctly on a Linux system.
Use networking commands correctly and appropriately on a Linux system.
Make use of Internet services on a Linux system.

Secure a Linux system
Explain clearly the difference between the super-user, users, and groups in Unix/Linux.
Describe accurately the components of a Linux user account.
Properly use the user/group rights to secure files and directories.
Administer globally the permissions of all users.

Install and Configure a web server
Explain clearly how to start/stop/query services in a Unix/Linux system.
Describe accurately the basic components of a web server.
Install correctly the Apache web server.
Properly configure the Apache web server.
Start, stop, and query the Apache web server.
Configure Virtual Hosts in the Apache web.

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Linux Security Details >

BIG DATA

*Schedule subject to change

Management and Processing of Big Data–Level I Details >

Course Title	Management and Processing of Big Data – Level I
Course Number	900-058-EQ-02
Platform	Linux
Duration	48 hours
Gouvernement du Québec fee (taxes incl.)	$96
General Public fee (taxes incl.)	$867.00
Schedule	Saturdays 9:00 a.m.- 4:00 p.m. (30 minute lunch); Sunday, February 28: 9:30 a.m. – 12:00 p.m.
Dates	February 6, 13, 20, 27, March 6, 13, 20 & Sunday, February 28: 9:30am-12:00pm
Prerequisites	Comfortable in Linux environment, some programming experience is required (ideally JAVA )
Target Audience	Developers
Instructor	Deepak Parameshwarappa
Location	Online Format

NB: This is a non-credit course. Certificate provided for all participants who have completed 80% of course hours

Course Description:

This course provides practical foundation level training that enables participation in big data projects. Participants will be introduced to big data technology and tools, including MapReduce and Hadoop. They will learn how to install and configure the Hadoop in cluster environment, how to write complex MapReduce programs, and how to analyze Big Data using Pig and Hive.

Topics Covered in this Course

Introduction into Hadoop

What is Big Data?
- Introduction to Big data and its challenges
Importance of Big Data and Hadoop in the industry
Various dimensions of Big Data
Big Data Use Cases in different domains like telecom, banking, social media, and so on
Traditional systems vs Big Data tools
Scope and future of Big Data in industry

Components of Hadoop: Basic Concepts and HDFS

Installation, configuration and setup of Hadoop cluster
Working with Hadoop in pseudo-distributed mode
Brief introduction to Hadoop and its ecosystem
Hadoop cluster Architecture
Hadoop Administration and troubleshooting
Running basic HDFS commands

Hadoop MapReduce Framework

MapReduce Architecture
What is YARN?
- YARN components
- YARN Architecture
Input splits, HDFS blocks
Distributed Cache

Apache Sqoop

What is Sqoop? Why do we need it?
Sqoop Architecture
Importing and exporting data from RDBMS
Hands-on Sqoop commands

Apache Pig – Alternative to MapReduce

Drawbacks of MapReduce
What is Pig? – Architecture Overview
Hands-on Pig scripts

Pig Use case – Olympic Data analysis using Apache Pig

Apache Hive – Analysis and Data Warehousing tool in Hadoop Ecosystem

What is Hive? – Architecture Overview
Hive history and its components
Hands on Hive – orderBy, clusterBY, distributeBy and so on
Hive SerDe
Hive – internal vs external tables
Different file formats – avro, JSON, parquet, RC, ORC

Hive Use case – Olympic Data analysis

Apache Hive Advanced – Optimization

Partitioning and Bucketing
Optimized advanced Joins
Indexes and Views
Analytical and window functions in Hive – rank, dense_rank, lag, lead.

Hive Use case – Retail data analysis, Consumer Complaints analysis

Apache HBase – Introduction to NoSQL DataBase

Difference between RDBMS and NOSQL
HBASE architecture
HBase – Hands On

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Management and Processing of Big Data – Level 2 Details >

Course Title	Management and Processing of Big Data – Level II
Course Number	900-068-EQ-02
Platform	Linux
Duration	45 hours
Gouvernement du Québec fee (taxes incl.)	$90
General Public fee (taxes incl.)	$812.82
Schedule	Saturday: 9 a.m. – 4 p.m. May 15: 9 a.m. – 3:30 p.m. 30 minute lunch
Dates	March 27, April 10 ,17, 24 ,May 1,8, May 15: 9 a.m. -3:30 p.m. No class on April 3: Easter Saturday
Prerequisites	Absolute prerequisite: Management and Processing of Big data – Level I
Target Audience	Developers
Instructor	Deepak Parameshwarappa
Location	Online format

NB: This is a non-credit course. Certificate provided for all participants who have completed 80% of course hours

Course Description

A continuation of the course Management and Processing of Big Data –level I, this course provides practical foundation level training that enables participation in big data projects. Participants will be introduced to big data technology and tools, including MapReduce and Hadoop. They will learn how to install and configure the Hadoop in cluster environment, how to write complex MapReduce programs, and how to analyze Big Data using Pig and Hive.

Topics covered in this course

Introduction to PySpark

What is Apache Spark and why it is needed?
Spark VS Hadoop

o How does Spark fits in Hadoop Ecosystem?

Spark Use Case

Brief Introduction to Python for Apache Spark

Data types, variables
Conditional statements and Loops
Python files I/O Functions
Numbers, Strings and related operations
Lists, Tuples, Dictionaries, Sets and related operations
Hands-On

Functions, OOPS in Python

Lambda functions
Object Oriented Concepts
Standard Libraries and Modules used in Python
Hands-On

Deep Dive into Apache Spark

Spark Architecture and its Components

o Understanding SparkContext, SparkSession, Driver, Stages and Tasks.

Spark features and characteristics
Introduction to PySpark Shell and how to submit a PySpark Job
Installation of Apache Spark in windows
Brief discussion on Spark Web UI

Spark RDDs

What are RDDs, features and characteristics.
RDDs Transformations and Actions
Key-Value Pair RDDs and operations
Brief discussion on RDD Lineage
RDD Partitioning

o Coalesce

o Repartition

Hands-On

o Read and write data using RDDs

o Use cases on map, flatMap, reduceByKey, aggregateBykey, groupByKey, fold, sortByKey

o UBER data analysis

DataFrames and Spark SQL

Drawbacks of RDDs
What are DataFrames and Spark SQL and what is the need of it?
Spark SQL Architecture and Catalyst optimizers
A tale of 3 Spark API – RDDs VS DataSets VS DataFrames
Interoperating with RDDs
Brief overview of different file formats
Avro
Parquet
JSON
CSV
RC and ORC
Partitioning and Bucketing in DataFrames
Advanced Analytical Windowing functions
Performance tuning in Spark
Spark – Hive integration
Hands-On

o Stock Market data analysis

o UBER data analysis

o Aviation data analysis

o How to use complex Joins to make analysis

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BIG DATA Reporting and Prediction Details >

Course Title	BIG DATA – Reporting and Prediction
Course number	900-059-EQ-02
Platform	MS teams,Tableau Public, Knime & Google Cloud Platform
	45 hours
Prerequisites	Basic understanding of databases is preferred. Otherwise, Excel spreadsheet processing experience and an understanding of computer software systems. Basic understanding of data mining.
Target Audience	Data analysts; Computer Analysts; Individuals in any role dealing with small or large amounts of data needing to analyze it and produce insightful and actionable dashboards.
Dates	February 2, 4, 9, 11, 16, 18, 23, 25, March 2, 4, 9, 11, 16, 18, 23
Instructor	Diego Perea – Ph.D.
Room	Online format
Schedule	Tuesday & Thursday : 6:30 p.m. – 9:30 p.m.
Gouvernement du Québec fee (taxes incl.)	$90.00
General public fee (taxes incl.)	$812.82

Recommended textbook: Tableau and Google Cloud Platform on-line documentation

NB: This is a non- credit course. Certificate provided for all participants who have completed 80% of course hours.

Course Description

This course provides an introduction to data mining and big data. It gives participants the concepts and software skills needed to research, load, process and analyze data to obtain actionable insights. It focuses on developing software skills in Tableau, Knime and Google Big Query required to process data and prepare presentations and dashboards that highlight the data’s added value and support decisions and actions to follow.The course methodology is based on lectures led by the instructor who will present the concepts using industry examples. Each lecture is followed by a lab where participants complete specific tasks designed to reinforce the concepts introduced in the lecture.

In addition, participants are expected to complete a small assignment during the course and present it to the class. This will give them the confidence to apply the data mining skills learned in the course and to present the data insights in a clear, concise and engaging way.

Topics Covered in this Course

Data processing stages in data mining
Hardware and software systems for data mining
Extracting, transforming and loading data
Principles of data analysis
Effective reports and dashboards displaying the data insights
Big Data distributed processing systems: Hadoop, Spark and Google Big Query
Connecting analytics to Big Data distributed processing systems
Forecasting and prediction. Introduction to machine learning.

Weekly Topics Please note that the instructor reserves the right to modify this schedule
Week 1	Introduction and lab preparation and topic 1
Week 2	Topics 1 and 2 Data processing stages in data mining Hardware and software systems for data mining
Week 3	Topics 3 and 4 Extracting, transforming and loading data
Week 4	Topics 4 and 5 Principles of data analysis Effective reports and dashboards displaying the data insights
Week 5	Topic 5 Mid-course review Project presentation.
Week 6	Topics 6 and 7 Big Data distributed processing systems Connecting analytics to Big data systems
Week 7	Topic 8 Forecasting and prediction. Introduction to Machine Learning.
Week 8	Project presentations and concluding remarks.

SOFTWARE TO BE USED

For the course, we will primarily use Tableau public to load, process and analyze data, and produce reports and dashboards. For the large data portion of the course, we will use Google Big Query platform. Knime will be used for prediction and basic machine learning techniques. Other complimentary software includes MS Excel and MS Access. Data analytics software tools besides Tableau and Knime will be discussed in the course to provide participants a holistic view of data mining.

LABS and DATASETS

In the labs, participants will practice the skills needed for the different stages of the data mining process. Namely, ETL (Extraction Transformation and Load), Analysis and Reporting. Students are encouraged to bring their own datasets. However, for the lectures and labs we will use the following datasets.

Uber trip data: trip information including Uber service type, source, destination, distance, duration and paid fare. Example

https://public.tableau.com/views/Lab4-DatacharacterizationA-categoricalfields/Dashboard2

On-line store purchasing behavior data: Characterization of on-line purchasing behavior. Example:

https://public.tableau.com/views/Lab3-Gizmoon-linestore/Dashboard2

Mobile video trending data: Characterization and trending analysis of video consumption from mobile devices. Example:

https://public.tableau.com/shared/XX6T2DZZ2

Google Cloud NOAA data: Worldwide meteorological information including temperature, wind and rain for more than 60 years. Example:

https://public.tableau.com/shared/D3MPD5GYH

Google Cloud Shakespeare data: word count of all Shakespeare works. Example:

https://public.tableau.com/views/Lab12A-Shakespearedataset/Story1

Students work on a small project during the course to get comfortable using the techniques learned in class. Find below a link to some of the student projects.

https://public.tableau.com/views/StudentAssignmentsFall2017/StudentAssignmentsStory

In addition, in the middle of the course we host a friendly competition in the style of a hackathon where students are given a dataset and a problem to solve. They work for half a day and present a dashboard showing how the problem was solved and the main data insights. Please find below links to the project winners in the spring of 2019.

First place: Analysis of Bixi Ridership data in Montreal

https://public.tableau.com/profile/sfmdorval#!/vizhome/Bixiproject-2/BIXIrides

Second place: Dashboard for Real State sales in King County https://public.tableau.com/shared/WHDM2W78D

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DATABASES (SQL SERIES)

*Schedule subject to change

SQL – Introduction Details >

SQL – Intermediate Details >

SQL – Advanced Details >

SQL – Reporting Services (SSRS) – Section 1 Details >

SQL – Reporting Services (SSRS) – Section 2 Details >

SQL Analysis Services (SSAS) Details >

Course title	SQL Analysis Services (SSAS)
Course number	900-049-EQ-02
Platform	Windows
Duration	24 hours
Emploi-Québec fee (taxes incl.)	$48
General public fee (taxes incl.)	$433.50
Schedule	Monday & Wednesday: 6:00p.m.-10:00p.m.
Dates	March 29, 31, April 7, 12, 15, 19 (no class April 5, Easter Monday)
Prerequisites	SQL – Beginner, Intermediate, Advanced and/or experience with Microsoft SQL Server
Target audience	This course is aimed at data analysts, Business Intelligence developers, consultants or members of BI development teams who are about to create a project using Microsoft SQL Server Analysis Services Tabular Model.
Instructor	Khattar Daou
Location	Online Format
*NB: This is a non-credit course.*Certificate provided for all participants who have completed 80% of course hours.

Course Description:

Microsoft SQL Server Analysis Services (SSAS) provides Online Analytical Processing (OLAP) and data mining features and capabilities to business intelligence (BI) projects and solutions. The tabular model in Analysis Services provides an easy-to-use and handy way for business users to access their data.

Analysis Services also offers rich experiences to BI users through interoperability in Microsoft Office and Microsoft SharePoint Server, helping people gain, use, and share insight.

This training course describes how to use SQL Server Data Tools to develop and deploy an Analysis Services project and we will be focusing exclusively on Tabular Model. You will learn how to simplify the process of building complex solutions by taking advantage of the rich modeling capabilities in Analysis Services.

Topics to be covered:

Module 1 – Microsoft SQL Server Analysis Services (SSAS)
• Introducing Microsoft SQL Server Analysis Services (SSAS)
• What Is Analysis Services and Why Should I Use It?
• Describes how to use SQL Server Data Tools to develop and deploy an Analysis Services project
• Understanding the SQL Server Data Tools Tabular Model Authoring Environment

Module 2 – Analysis Services 2012 Architecture
• The Tabular Model
• Data Sources
• Import Data
• Understanding Data Analysis Expressions (DAX) in Tabular Models

Module 3 – Microsoft SQL Server Data Tools
• Working with SQL Server Data Tools
• Create a new tabular model project in SQL Server Data Tools
• Import data from a SQL Server relational database into a tabular model project
• Create and manage relationships between tables in the model
• Create and manage calculations, measures, and Key Performance Indicators that help users analyze model data
• Create and manage perspectives and hierarchies that help users more easily browse model data by providing business and application specific viewpoints
• How to create partitions that divide table data into smaller logical parts that can be processed independent

Module 4 – Building a Tabular Model
• Loading Data into Tables
• Working in the Diagram View
• Rename columns
• Creating calculated columns and measures using DAX formulas
• Deployment

Module 5 – Querying a Tabular Model in Excel
• Connecting to a Tabular Model
• Browsing a Workspace Database
• Connecting to a Deployed Database
• Filter the table data prior to importing

Module 6 – Querying a Tabular Model in Power View
• Creating a Connection to a Tabular Model
• Building a Basic Power View Report
• Adding Charts and Slicers
• Interacting with a Report

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SQL Server Integration Services (SSIS) – Introduction Details >

Course Title	SQL Integration Services (SSIS) – Introduction
Course Number	900-083-EQ-02
Platform	Windows
Duration	21 hours
Gouvernement du Québec fee (taxes incl.)	$42
General Public fee (taxes incl.)	$373.31
Schedule	Saturday: 9:00am-3:30pm (30 minute lunch) Last class: 9:00am-12:00pm
Dates	March 27, April 10, 17, 24 (no class April 3, Easter Saturday)
Prerequisites	SQL Level 2 or working knowledge of relational databases (database design skills)
Target Audience	Participants interested in acquiring the knowledge and skills needed to implement Microsoft SQL Server Integration Services (SSIS)
Instructor	Khattar Daou
Location	Online Format

NB. This is a non-credit course. Certificate provided for all participants who have completed 80% of course hours

*Textbook:

Course Description:

Microsoft SQL Server Integration Services (SSIS) is a platform for building high performance data integration solutions, including extraction, transformation, and load (ETL) packages for data warehousing.

The primary objective of this course is to provide students with the knowledge and skills to use SSIS Designer to create a simple Microsoft SQL Server Integration Services package. The created package takes then data from a flat file, reformats the data, and then inserts the reformatted data into a fact table.

This course walks you through SSIS Designer to create a simple ETL package that includes looping, configurations, error flow logic and logging.

Topics to be covered:

Module 1 – Overview of SQL Server Integration Services

Overview of SSIS
Integration Services (SSIS) Development and Management Tools
Integration Services (SSIS) Projects and Solutions
Install/ Upgrade Integration Services
Change Data Capture (SSIS)
Integration Services (SSIS) Connections
Control Flow
Integration Services Containers
Integration Services Tasks
Precedence Constraints

Module 2 – Create a simple ETL package

Create a Project and Basic Package with SSIS
Adding Looping with SSIS
Add Logging with SSIS
Add Error Flow Redirection with SSIS
Add SSIS Package Configurations for the Package Deployment Model
Using Parameters with the Project Deployment Model in SSIS

Module 3 – Deploy Packages with SSIS

Preparing to Create the Deployment Bundle
Create the Deployment Bundle in SSIS
Install SSIS Packages

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SQL Server Integration Services (SSIS) – Advanced Details >

Course Title	SQL Integration Services (SSIS)- Advanced
Course Number	900-092-EQ-01
Platform	Windows
Duration	14 hours
Gouvernement du Québec fées ( taxes incl.)	$28.00
General public fees (taxes incl.)	$252.88
Schedule	Monday & Wednesday: 6 p.m.- 10 p.m. Saturday : 9 a.m.-3:30 p.m. (30 minute lunch )
Dates	January 4,6,9
Prerequisites	SQL Server Integration Services (SSIS) – Advanced
Target Audience	This course is not designed for students who are new to SQL Server BI technologies; it is targeted at BI professionals with experience of implementing solutions with SQL Server database Engine, SQL Server Integration Services, SQL Server Analysis Services, and SQL Server Reporting Services.
Instructor	Khattar Daou
Location	Online format

Please note that this is a non-credit course. Certificate provided for all participants who have completed 80% of course hours

Recommended Textbook

Exam Ref 70-767 Implementing a SQL Data Warehouse

Jose Chinchilla, Raj Uchhana

Course Description

This training course teaches database and business intelligence (BI) professionals how to plan and design a BI solution that is based on Microsoft SQL Server and other Microsoft BI technologies.

This course describes how to implement a BI platform to support information worker analytics. Students will learn how to create a data warehouse with Microsoft SQL Server, implement ETL with SQL Server Integration Services, and validate and cleanse data with SQL Server Data Quality Services and SQL Server Master Data Services.

Topics Covered in this Course

Weekly Topics

Please note that the instructor reserves the right to modify this schedule

Module 1

Overview of Data Warehousing

This module provides an introduction to the key components of a data warehousing solution and the high-level considerations that you must take into account when you embark on a data warehousing project.

After completing this module, you will be able to:

• Describe the key elements of a data warehousing solution

• Describe the key considerations for a data warehousing project.

Module 2

Designing and Implementing a Data Warehouse

In this module, you will learn how to implement the logical and physical architecture of a data warehouse based on industry-proven design principles.

After completing this module, you will be able to:

• implement a logical design for a data warehouse

• implement a physical design for a data warehouse

Module 3

Creating an ETL Solution with SSIS

This module discusses considerations for implementing an ETL process, and then focuses on Microsoft SQL Server Integration Services (SSIS) as a platform for building ETL solutions.

After completing this module, you will be able to:

• Describe the key features of SSIS

• Explore source data for an ETL solution

• Implement a data flow by using SSIS

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SQL – Data Quality Services (DQS) Details >

Course Title	Data Quality Services (DQS)
Course Number	900-078-EQ-01
Platform	Microsoft Windows
Duration	21 hours
Gouvernement du Québec fee ( taxes incl)	$42
General public feee ( taxes incl)	$379.31
Schedule	Monday & Wednesday: 6 p.m. -9:30 p.m.
Dates	February 1, 3, 8, 10, 15, 17

Prerequisites	SQL Level 2 or working knowledge of relational databases (database design skills
Target Audience	Data quality is one of the common challenges in every database system, especially when data comes from different kinds of sources. This course will explain the concept of data quality through a new Microsoft engine for data cleansing named Data Quality Services.
Instructor	Khattar Daou
Location	Online format

Course Description
Please note that this is a non-credit* course. Certificate provided for participants who have completed 80% of course hours*
SQL Server Data Quality Services (DQS) enables you to cleanse, match, standardize, and enrich data, so you can deliver trusted information for business intelligence, a data warehouse, and transaction processing workloads. By the end of this course, you will have good understanding of the most common challenges in data quality and you will also learn how to design and implement a solution with Data Quality Services to overcome these challenges and provide better data quality for the data warehouse.

Topics Covered in this Course

Module 1: Introducing Data Quality Services

· Unit 1: SQL Server Data Quality Services (DQS) & Master Data Services (MDS)

· Unit 2: Prerequisites and Setup

· Unit 3: Configure MDS using Master Data Services Configuration Manager

· Unit 4: Setting up the Database and Website

Module 2: A Knowledge-Driven Solution

· Unit 1: Deploying Sample Models and Data

· Unit 2: Install and Configure DQS using Data Quality Server Installer

· Unit 3: Install Master Data Services Add-in for Excel

· Unit 4: Creating DQS Knowledge Base

Module 3: Data Cleansing

· Unit 1: Cleansing Data using Knowledge Base

· Unit 2: Matching Data to Remove Duplicates Values

· Unit 3: Storing Data in MDS

· Unit 4: Reference Data Services in DQS

Weekly Topics Please note that the instructor reserves the right to modify this schedule
Week 1	Module 1
Week 2	Module 2
Week 3	Module 3

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PYTHON PROGRAMMING LANGUAGE

*Schedule subject to change

Python – Level 1 – Section 4 Details >

Python – Level 1 – Section 5 Details >

Python – Level 2 – section 4 Details >

Python – Level 2 – section 5 Details >

Python – Level 3 – Section 2 Details >

DATA SCIENCE

*Schedule subject to change

Data Science – Level 1 – Section 3 Details >

Data Science – Level 1 – Section 4 Details >

Course Title	Data Science – Level 1 – Section 4
Course number	900-080-EQ-04
Platform	Windows desktop
Duration	24 hours
Prerequisites	Python I, programming experience. This is a programming intensive course. Students must be comfortable working with text files (opening, reading and writing text files using a programming language). Knowledge of conditionals, loops, functions, indexing, slicing, lists, dictionaries and arrays is essential.
Target Audience	All those interested in an introduction to the world of data science. Students must have programming background. See prerequisites.
Dates	March 2, 4, 9, 11, 16, 18, 23
Instructor	José Rafael Porras
Room	Online format
Schedule	Tuesday & Thursday: 6 p.m. – 9:30 p.m. First class on March 2: 6:30 p.m. – 9:30 p.m.
Gouvernement du Québec fee	$48.00
General public fee	$433.50

Recommended textbook: Class Notes

NB: Certificate provided for all participants who have completed 80% of course hours

Course Description

Please note that this is a non-credit course.

This course is the first in a series of three courses on the subject of data science. The course will introduce the participant to the concepts of data extraction, transformation and loading using the Python language and the Pandas module.The course will also explore concepts of storing data in relational databases (SQL), Consuming APIS, JSON and basic web scraping. Beyond ETL concepts, the course will also introduce the basics of data visualization and statistical concepts as applied. The course will end with an introduction to simple linear regression and K-Means clustering.

It is strongly recommended that students have competed the course Python I prior to registering in this course.

This is a programming intensive course. The student must be comfortable working with text files (opening, reading and writing text files using a programming language).
Knowledge of conditionals, loops, functions, indexing, slicing, lists, dictionaries, tuples is essential.

Topics Covered in this Course

Introduction to ETL processes and data wrangling using Python
Using Pandas module to explore data
Working with Databases and SQL
Introduction to APIS
Introduction to static Web Scraping
Introduction to Data Visualization using Matplotlib
Overview of simple linear regression and K-Means Clustering techniques

Data Science – Level 1 – Section 5 Details >

Course Title	Data Science – Level 1 – Section 5
Course number	900-080-EQ-05
Platform	Windows desktop
Duration	24 hours
Prerequisites	Python I, programming experience. This is a programming intensive course. Students must be comfortable working with text files (opening, reading and writing text files using a programming language). Knowledge of conditionals, loops, functions, indexing, slicing, lists, dictionaries and arrays is essential.
Target Audience	All those interested in an introduction to the world of data science. Students must have programming background. See prerequisites.
Dates	March 1, 3, 8, 10, 15, 17, 22
Instructor	José Rafael Porras
Room	Online format
Schedule	Monday & Wednesday: 6 p.m. – 9:30 p.m. First class on March 1: 6:30 p.m. – 9:30 p.m.
Gouvernement du Québec fee	$48.00
General public fee	$433.50

Recommended textbook: Class Notes

NB: Certificate provided for all participants who have completed 80% of course hours

Course Description

Please note that this is a non-credit course.

It is strongly recommended that students have competed the course Python I prior to registering in this course.

Topics Covered in this Course

Introduction to ETL processes and data wrangling using Python
Using Pandas module to explore data
Working with Databases and SQL
Introduction to APIS
Introduction to static Web Scraping
Introduction to Data Visualization using Matplotlib
Overview of simple linear regression and K-Means Clustering techniques

Data Science – Level 2 Details >

Course Title	Data Science – Level 2
Course number	900-081-EQ-02
Platform	MS Teams, Knime and Python
Duration	24 hours
Prerequisites	Basic understanding of Probability and Algebra. Basic Data analysis and processing in Excel, Tableau or any database systems. Data manipulation and basic Linear regression in Python. Completion of the Data Science Level 1 course.
Target Audience	Data Analysts; Computer Analysts; Individuals in any role dealing with small or large amounts of data needing to model the data to obtain predictions.
Dates	March 25, 29, April 1, 6, 8, 13, 15
Instructor	Diego Perea – Ph.D.
Room	Online Format
Schedule	Tuesday & Thursday: 6 p.m. – 9:30 p.m. First class on March 25 : 6:30 p.m. – 9:30 p.m.
Gouvernement du Québec fee	$48.00
General public fee	$433.50

NB: Certificate provided for all participants who have completed 80% of course hours

Recommended textbook

[1] An Introduction to Statistical Learning with Applications in R by G. James, D. Whitten, R. Tibshirani and T. Hastie.

Course Description

Please note that this is a non-credit course.

The Descriptive analytic methods, seen in previous big data courses, form the basis for the Predictive Analytic methods that are at the heart of this new course. In this course, participants will learn the standard statistical methods currently used in industry to perform predictive analytics. These include linear and non-linear regression and several classification methods such as Logistic regression, KNN, Decision Trees and SVM. Participants will learn how to research the available data and choose the best predictive method to apply. Key components of this course are the understanding of these methods, the methodology to evaluate them and the criteria to choose the best method.The course methodology is based on on-line short lectures in MS-Teams presenting the concepts using industry examples. Each lecture is followed by a lab using real data, where the participants will complete specific tasks in Knime and Python designed to reinforce the concepts introduced in the lecture.

Topics Covered in this Course

Introduction to Statistical Learning for Data Science
Basic statistical analysis in Knime and Python: Histograms, box and scatter plots
Continuous variables regression linear
Regression beyond linearity
Maximum Likelihood and Logistic regression Classifier. K-Nearest Neighbors (KNN) classifier
Decision trees for regression and classification
Support Vector Machine Classifier and Practical applications of Classification and regression.

Weekly Topics Please note that the instructor reserves the right to modify this schedule
Week 1	Topics 1 and 2 Introduction, course description and Knime overview. Basic statistical analysis: Histograms, box and scatter plots.
Week 2	Topics 3 and 4 Continuous variables regression beyond linearity
Week 3	Topics 5 and 6 Supervised classification
Week 4	Topic 7 Support Vector Machines and concluding remarks

SOFTWARE TO BE USED

For the course, we will mainly use Python, which is the industry standard for statistical learning and provides functions for most of the methods. Other software used for statistical learning is Knime that has a friendly graphical interface. MS-Teams will be the on-line learning platform which allows on-line meetings and presentations, posting of polls and assignments and lecture recording for the students review of the lecture.

LABS and DATASETS

In the labs, the participant will apply the prediction and classification methods seen in class using different datasets.

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Data Science – Level 3 Details >

Course Title	Data Science-Level 3
Course number	900-082-EQ-01
Platform	Python, Knime
Duration	21 hours
Gouvernement du Québec fee ( taxes incl.)	$42
General Public fee	$379.31
Schedule	Tuesday & Thursday 6:30 p.m.- 9 :30 p.m.
Dates	January 7,12,14,19,21,26, 28
Prerequisites	Data Science – Level 2 course
Target Audience	Data Analysts; Computer Analysts; Professionals dealing with small or large amounts of data needing to apply Machine Learning Methods.
Instructor	Diego Perea Ph.D.
Room	Online format

NB: This is a non-credit course. Certificate provided for all participants who have completed 80% of course hours.

Recommended Textbook

“An Introduction to Statistical Learning with applications in R” by G. James, D. Whitten, R. Tibshirani and T. Hastie.

Course Description

This course deals with advanced methods of Machine Learning for data science. At the end of the course the participant will have at disposal a large set of methods to apply for regression, supervised and unsupervised classification problems.

The course methodology is based on lectures led by the instructor, who will present the concepts using examples followed by a lab using real data where the participants will complete specific tasks in Knime and Python designed to reinforce the concepts introduced in the lecture.

Students will complete a small data prediction project with data of their choice during the course, where they apply the methods learned in the course. Examples of previous students projects are listed below

https://public.tableau.com/views/StudentProjects/StudentProjects

Topics Covered in this Course

1. Regression Review and advanced variable selection methods: Ridge, Lasso.

2. Supervised and unsupervised classification methods

3. Forecasting

4. Big data and commercial machine learning systems

5. Project presentation

Weekly Topics

Please note that the instructor reserves the right to modify this schedule

Week 1

Topics 1

Week 2

Topic 2

Week 3

Topic 3

Week 4

Topic 4

SOFTWARE TO BE USED

For the course, we will mainly use Knime and Python, which are the industry standard for statistical learning and provides functions for most of the methods. Other software will be addressed in the course to give the participant a holistic view of statistical learning.

LABS and DATASETS

In the labs, the participant will apply the prediction and classification methods seen in class using practical datasets. We will use datasets from the textbook and from public sources such:

https://www.kaggle.com/

https://archive.ics.uci.edu/ml/datasets.html

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VIRTUALIZATION TECHNOLOGIES

*Schedule subject to change

Microsoft Azure – Fundamental Concepts – Section 001 Details >

Course Title	Microsoft Azure Fundamental Concepts
Course Number	900-067-EQ-01
Platform	Cloud Technologies
Duration	45 hours
Gouvernement du Québec fee ( taxes incl.)	$90
General public fee (taxes incl.)	$812.82
Prerequisites	· A basic understanding of virtualization · A basic understanding of Active Directory concepts, including domains, users and domain controllers. · An understanding of Web Sites A basic understanding of database concepts including tables and simple queries
Schedule	Saturday & Sunday : 9 a.m.-5 p.m. ( 30 minute lunch)
Dates	January 16,17,23,24,30,31,
Prerequisites	Windows and /or Linux Servers experience /or knowledge
Target Audience	Individuals ( Developers, Administrators, IT professionals, Database administrators ) who want to evaluate, deploy ,configure and administer services and VMs using Azure
Instructor	Michael Hughes
Location	Online format

Please note that this is a non-credit course. Certificate provided for all participants who have completed 80% of course hours

Course Description

This course provides the skills required to set up and sustain an Office 365 tenant and user. Participants will learn to evaluate, plan , deploy and operate Microsoft Office 365 services that include : identities, dependencies, requirements, supporting technologies

After completing this course, students will be able to:
1. Describe cloud computing, Azure, and Azure subscriptions.
2. Create Azure Hands-on Lab (HOL)
3. Create and configure VMs in Microsoft Azure using HOL
4. Create an Azure virtual network using HOL
5. Describe Azure AD.
6. Describe and create a simple Azure Web App(s) using Visual Studi0

Topics Covered in this Course

Weekly Topics
Please note that the instructor reserves the right to modify this schedule

Module 1: Getting Started with Microsoft Azure

This module introduces students to cloud services and the various Azure services. It describes how to use the Azure portal to access and manage Azure services, and to manage Azure subscription and billing.

Lessons

o What is cloud computing?

o What is Azure?

o Managing Azure

o Subscription management, support, and billing

Lab: Using the Azure portals

o Customizing the Azure portal interface

o Viewing billing, usage, and quotas data

Module 2: Microsoft Azure management tools

This module explains Azure PowerShell and its use in managing Azure subscriptions. It also describes the Azure Software Development Kit (SDK) and the Azure cross-platform command-line interface, and explains their benefits and uses.

Lessons

o What is Azure PowerShell

o The Azure SDK and the Azure CLI

Lab: Using Microsoft Azure management tools

o Using the Azure PowerShell modules

o Explain the Azure CLI

Module 3: Virtual machines in Microsoft Azure

This module explains how to use Azure to deploy virtual machines. It also explains the highly available and scalable configuration of Azure virtual machines, and the management of virtual machine disks by using Azure.

Lessons

o Creating and configuring VMs

Lab: Creating a VM in Azure

o Create a VM from the Azure Portal by using an Azure Marketplace images (Linux and Windows)

o Verify the functionality of the VM

o Log into and explore the virtual machines crated using different clients

Module 4: Creating and Configuring Virtual Networks

This module explains Azure virtual networks and how to create them. It also covers variety of virtual network components.

Lessons

o Getting started with virtual networks

o Creating a virtual network

Lab: Create a Virtual Network

o Creating a virtual network

o Verifying virtual network functionality

Module 5: Web Apps and Cloud Services

This module explains how to create, configure, and monitor Web apps by using Azure. It also describes the creation and deployment of Cloud Services on Azure.

Lessons

o Creating and configuring web apps

o Deploying and monitoring web apps

o Creating and deploying PaaS cloud services

Lab: Web Apps and Cloud Services

o Creating and configuring web apps (Using selected tools)

o Create a cloud service

o Logging into Azure and Configuring Web Apps using Visual Studio

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Microsoft Azure – Fundamental Concepts – Section 002 Details >

Course Title	Microsoft Azure Fundamental Concepts
Course Number	900-067-EQ-02
Platform	Cloud Technologies
Duration	45 hours
Gouvernement du Québec fee ( taxes incl.)	$90
General public fee (taxes incl.)	$812.82
Prerequisites	· A basic understanding of virtualization · A basic understanding of Active Directory concepts, including domains, users and domain controllers. · An understanding of Web Sites A basic understanding of database concepts including tables and simple queries
Schedule	Saturday & Sunday : 9 a.m.-5 p.m. ( 30 minute lunch)
Dates	February 27, 28, March 6, 7, 13, 14
Prerequisites	Windows and /or Linux Servers experience /or knowledge
Target Audience	Individuals ( Developers, Administrators, IT professionals, Database administrators ) who want to evaluate, deploy ,configure and administer services and VMs using Azure
Instructor	Michael Hughes
Location	Online format

Please note that this is a non-credit course. Certificate provided for all participants who have completed 80% of course hours

Course Description

Topics Covered in this Course

Weekly Topics
Please note that the instructor reserves the right to modify this schedule

Module 1: Getting Started with Microsoft Azure

Lessons

o What is cloud computing?

o What is Azure?

o Managing Azure

o Subscription management, support, and billing

Lab: Using the Azure portals

o Customizing the Azure portal interface

o Viewing billing, usage, and quotas data

Module 2: Microsoft Azure management tools

Lessons

o What is Azure PowerShell

o The Azure SDK and the Azure CLI

Lab: Using Microsoft Azure management tools

o Using the Azure PowerShell modules

o Explain the Azure CLI

Module 3: Virtual machines in Microsoft Azure

Lessons

o Creating and configuring VMs

Lab: Creating a VM in Azure

o Create a VM from the Azure Portal by using an Azure Marketplace images (Linux and Windows)

o Verify the functionality of the VM

o Log into and explore the virtual machines crated using different clients

Module 4: Creating and Configuring Virtual Networks

This module explains Azure virtual networks and how to create them. It also covers variety of virtual network components.

Lessons

o Getting started with virtual networks

o Creating a virtual network

Lab: Create a Virtual Network

o Creating a virtual network

o Verifying virtual network functionality

Module 5: Web Apps and Cloud Services

This module explains how to create, configure, and monitor Web apps by using Azure. It also describes the creation and deployment of Cloud Services on Azure.

Lessons

o Creating and configuring web apps

o Deploying and monitoring web apps

o Creating and deploying PaaS cloud services

Lab: Web Apps and Cloud Services

o Creating and configuring web apps (Using selected tools)

o Create a cloud service

o Logging into Azure and Configuring Web Apps using Visual Studio

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Amazon Web Services – Section 001 Details >

Amazon Web Services – Section 002 Details >

Container Based System Architecture: Docker – Section 001 Details >

Container Based System Architecture: Docker – Section 002 Details >

Hyper-V Details >

MICROSOFT APPLICATIONS

*Schedule subject to change

VBA for Excel Details >

Eligibility

$2/hour – funded by Services Québec de l’Ile de Montréal

Am I eligible?

Determine if you are eligible for the subsidized rate of $2 per hour.

Eligible Participants

Non-Eligible Participants

Employed and self-employed individuals working in areas related to IT

Individuals working in an unrelated field, but with an educational background in IT

Individuals employed in a position for which they are overqualified in relation to their diploma, and the training is related to their field of study
Unemployed individuals residing on the Island of Montreal and the training is related to their professional experience or educational background

Students (even if they are working part-time)

Unemployed individuals not residing on the Island of Montreal

Individuals receiving CSST or SAAQ benefits
Any employee of a government entity, including public, para-public, (Québec/Canada) or municipal
Individuals seeking a career change or taking the course for personal purposes

Although individuals in the Greater Montreal area (Montréal, Laval, Laurentides, Lanaudière et Montérégie) are eligible to take these courses, individuals who live and/or work on the Island of Montreal have priority.

Participants cannot be registered in more than two (2) courses AT THE SAME TIME. However, a subsidized participant may apply for as many courses as they wish over the course of the year, just not more than two (2) simultaneously.

No more than three (3) employees from the same company may be registered in any one course at the same time.

Registration

How do I register?

Carefully read the eligibility requirements above. If you are ELIGIBLE for the course at the rate of $2/hr:

For each course for which you register:

Step 1

Select the course found in the Schedule tab
On the course page, click on the “REGISTER” button. This will take you to Mia, the online registration module
In the left-hand column, select #1 “PART-TIME IT COURSES (SERVICES QUÉBEC)”
Select the course from the resulting list
Select “Add to cart”
Select “Confirm registration and go to payment”

Step 2

Login to your Omnivox account with your email address and password. If you do not have an account, select “First-time Access” to create on and fill in the required information in order to use the system for the first time.
Complete the Registration Form
Proceed to the payment for your course by credit card (Visa or MasterCard)
A “Web registration receipt” will appear (you will receive this by e-mail as well)

Step 3

In order to complete your registration, you MUST fill out the form required by the Gouvernement du Québec (a link to the form is provided in your e-mail receipt)
NOTE: Your form will not be submitted until you have clicked on the “Confirm your answers” button at the end of the process

Upon receipt of your registration, the Centre for Continuing Education will review your registration form to confirm your eligibility.

If it is determined that you are not eligible for the subsidized rate of $2/hr, the Centre for Continuing Education will contact you. You will be required to pay the fee that is applicable to the General Public (see the course description) OR you may withdraw from the course with no penalty and a complete refund of any payment will be mailed to you.

If you are NOT ELIGIBLE for the course at the rate of $2/hr
Please note that priority is given to participants who meet the eligibility criteria. However, there may be space available to the General Public, at an additional cost (see course description for the exact cost). If you are interested in taking a course and you know in advance that you do not meet the eligibility criteria, please follow the steps above.

Note:

You will still need to complete the mandatory form required by Services Québec, in order for your registration to be processed
In this form, respond “NO” to the question “I meet the eligibility requirements…”. You will be redirected to the end of the form
The Centre for Continuing Education will review your form and the registration module will apply the non-subsidized cost for the course (General Public fee) to your Omnivox account
You will be notified by e-mail to proceed to the online registration module, to pay your balance

Important Information:

The Centre for Continuing Education will confirm receipt of your application form and payment via email
You will receive an email approximately 5 working days before the class starts, providing all course details including class location
You cannot register for more than 2 courses AT THE SAME TIME. However, a subsidized participant can apply for as many courses as they wish over the course of the year, just not more than 2 simultaneously
We cannot accept more than three employees from the same company in the same course
The cost of manuals or textbooks is not included in the course fees, unless otherwise indicated.
Taxes are included in course fees.

The part-time IT specialized courses are popular and have very limited spaces available. Register early to avoid disappointment. Please refer back to the website often for regular updates & information and Services Québec specific eligibility requirements.

If you experience technical problems with our online registration module for part-time IT courses, please call 514-457-5036 or e-mail conted@johnabbott.qc.ca.

Refund Policy

The Centre for Continuing Education reserves the right to cancel any course due to insufficient registration prior to the start of classes. Participants registered in a cancelled class will be contacted and receive a full refund for the course. If the Centre for Continuing Education determines that a participant is not eligible for the course at the reduced rate of $2 / hour, the participant will be notified and may pay the balance of the fee offered to the general public, or receive a full refund. Tuition fees for non-credit IT courses offered in collaboration with the Services Québec are refundable upon receipt of written notification as follows:

Full refund 7 days or more before the first scheduled class
No refund 6 days before the first scheduled class

For withdrawal requests, all applicants are required to notify the Centre for Continuing Education in writing, as soon as possible at: iteq@johnabbott.qc.ca.

Course Title	Container Based System Architecture – Docker
Course number	900-090-EQ-01
Platform	Linux
Duration	15 hours
Gouvernement du Québec fee (taxes incl.)	$30.00
General public fee (taxes incl.)	$270.94
Schedule	Saturday & Sunday : 9 a.m.-5 p.m. ( 30 minute lunch)
Dates	January 9, 10
Prerequisites	Good Knowledge of 2016 Server
Target audience	Individuals currently working in IT with either Windows and/or Linux Servers experience
Instructor	Michael Hughes
Location	Online format
NB: This is a non-credit course. Certificate provided for all participants who complete 80% of course hours.
Course description Linux-based containers provide an isolated and portable operating environment that works consistently and repeatedly when deployed in development, staging or in production.
Topics to be covered include:

Weekly Topics Please note that the instructor reserves the right to modify this schedule.
· Fundamentals of containers · Understanding how containers work explore some of the most commonly used cases of containers · Learn the basics about container hosts, container images and working with images registries
· Day 1	Container technology is an established technology. Various distributions of Linux can run Docker containers in your own environment or on the cloud. In this course, you implement Containers on a Debian distribution of linux. You will deploy and manage containers on a Debian server Install and Use Docker on Debian 9 Using the Docker Command Working with Docker Images Running a Docker Container Managing Docker Containers Committing Changes in a Container to a Docker Image Pushing Docker Images to a Docker Repository Push and Pull images from Docker Hub Deploying containers in Docker
Day 2	How to Scale an App Deploy your app on the swarm cluster Using Dockerfiles for Repeatable, Consistent Builds How to Setup DNS Server with Docker Container Docker Compose Scheduling, Cluster Management, and Orchestration Using a Docker Registry for Container Management
Time Permitting	· Examining the basics of Docker networking · Docker Compose

Course Title	Container Based System Architecture – Docker
Course number	900-090-EQ-02
Platform	Linux
Duration	15 hours
Gouvernement du Québec fee (taxes incl.)	$30.00
General public fee (taxes incl.)	$270.94
Schedule	Saturday & Sunday : 9 a.m.-5 p.m. ( 30 minute lunch)
Dates	February 20 & 21, 2021
Prerequisites	Good Knowledge of 2016 Server
Target audience	Individuals currently working in IT with either Windows and/or Linux Servers experience
Instructor	Michael Hughes
Location	Online format / BH-311
NB: This is a non-credit course. Certificate provided for all participants who complete 80% of course hours.
Course description Linux-based containers provide an isolated and portable operating environment that works consistently and repeatedly when deployed in development, staging or in production.
Topics to be covered include:

Weekly Topics Please note that the instructor reserves the right to modify this schedule.
· Fundamentals of containers · Understanding how containers work explore some of the most commonly used cases of containers · Learn the basics about container hosts, container images and working with images registries
· Day 1	Container technology is an established technology. Various distributions of Linux can run Docker containers in your own environment or on the cloud. In this course, you implement Containers on a Debian distribution of linux. You will deploy and manage containers on a Debian server Install and Use Docker on Debian 9 Using the Docker Command Working with Docker Images Running a Docker Container Managing Docker Containers Committing Changes in a Container to a Docker Image Pushing Docker Images to a Docker Repository Push and Pull images from Docker Hub Deploying containers in Docker
Day 2	How to Scale an App Deploy your app on the swarm cluster Using Dockerfiles for Repeatable, Consistent Builds How to Setup DNS Server with Docker Container Docker Compose Scheduling, Cluster Management, and Orchestration Using a Docker Registry for Container Management
Time Permitting	· Examining the basics of Docker networking · Docker Compose

*IMPORTANT MESSAGE*

Courses

NETWORK & SECURITY

Linux – Operating System

Linux Security

BIG DATA

Management and Processing of Big Data–Level I

Management and Processing of Big Data – Level 2

BIG DATA Reporting and Prediction

DATABASES (SQL SERIES)

SQL – Introduction

SQL – Intermediate

SQL – Advanced

SQL – Reporting Services (SSRS) – Section 1

SQL – Reporting Services (SSRS) – Section 2

SQL Analysis Services (SSAS)

SQL Server Integration Services (SSIS) – Introduction

SQL Server Integration Services (SSIS) – Advanced

SQL – Data Quality Services (DQS)

PYTHON PROGRAMMING LANGUAGE

Python – Level 1 – Section 4

Python – Level 1 – Section 5

Python – Level 2 – section 4

Python – Level 2 – section 5

Python – Level 3 – Section 2

DATA SCIENCE

Data Science – Level 1 – Section 3

Data Science – Level 1 – Section 4

Data Science – Level 1 – Section 5

Data Science – Level 2

Data Science – Level 3

VIRTUALIZATION TECHNOLOGIES

Microsoft Azure – Fundamental Concepts – Section 001

Microsoft Azure – Fundamental Concepts – Section 002

Amazon Web Services – Section 001

Amazon Web Services – Section 002

Container Based System Architecture: Docker – Section 001

Container Based System Architecture: Docker – Section 002

Hyper-V

MICROSOFT APPLICATIONS

VBA for Excel

Eligibility

Am I eligible?

Registration

How do I register?

The part-time IT specialized courses are popular and have very limited spaces available. Register early to avoid disappointment. Please refer back to the website often for regular updates & information and Services Québec specific eligibility requirements.

If you experience technical problems with our online registration module for part-time IT courses, please call 514-457-5036 or e-mail conted@johnabbott.qc.ca.

Refund Policy

IMPORTANT MESSAGE