Specialized IT Courses

Overview
Registration
Courses
Refund Policy

IMPORTANT MESSAGE

Registration starts on Monday, August 28, 2020 at 9:00am
Note : All courses are offered online

Overview & Eligibility

$2/hour – funded by Gouvernement du Québec, Service Quebec

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 #2 “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 Admissions 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 the Gouvernement du 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 Gouvernement du 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.

Courses

Window Server Administration

*Schedule subject to change

Windows Server 2019 Details >

Windows Operating System Details >

Windows Server 2019 – Active Directory Details >

Exchange Server 2016 Details >

Windows Security – Introduction Details >

Network Security

*Schedule subject to change

Computer Systems 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-01
Platform	Linux
Duration	48 hours
Gouvernement du Québec fee (taxes incl.)	$96
General Public fee (taxes incl.)	$867.00
Schedule	Saturday 9:00 a.m.- 4:00 p.m. (30 minute lunch); Sunday, November 1: 9:30 a.m. – 12:00 p.m.
Dates	September ,12, 19, 26, October, 3, 17, 24, 31 & Sunday, November1 (no class on October 10 )
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

Management and Processing of Big Data – Level 2 Details >

Course Title	Management and Processing of Big Data – Level II
Course Number	900-068-EQ-01
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.; Sunday, December 6: 9 a.m. – 3:30 p.m. (30 minute lunch)
Dates	Saturday : November 7 ,14, 21, 28 , December 5 ,12 & Sunday, December 6
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
- 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
- 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
- Coalesce
- Repartition
Hands-On
- Read and write data using RDDs
- Use cases on map, flatMap, reduceByKey, aggregateBykey, groupByKey, fold, sortByKey
- 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
Stock Market data analysis
UBER data analysis
Aviation data analysis
How to use complex Joins to make analysis

Processing of Big Data with SPARK Details >

Course Title	Processing of Big Data with SPARK
Course Number	900-091-EQ-01
Platform	Linux
Duration	24 hours
Gouvernement du Québec fee (taxes incl.)	$48
General Public fee (taxes incl.)	$433.50
Schedule	Saturday 9:00 a.m. – 3:30 p.m. (30 minute lunch)
Dates	January 9, 16, 26, 30
Prerequisites	Management and Processing of Big Data-level I and Level 2 Basic SQL. Good understanding of any of the following Python/Java/Scala Knowledge of Linux Shell Commands. Hive and HDFS
Target Audience	Data engineer/architect; Big data developer; Enterprise application developer
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

Recommended Textbook

We will not follow any specific Textbook

Course Description:

Spark is an open source distributed processing engine built around speed, ease of use, and analytics. Its unique in-memory data processing technique reduces processing time drastically compared to a typical MapReduce program. It has well integrated APls for Batch, SQL, Streaming, Machine Learning and Graph processing in popular programming languages including Java, Scala, Python and R. These advantages made it top level Apache project in 2014.

This course will help you understand and practice various transformation and action Spark APls for data processing pipeline. You will also get familiar with data holder APls including RDD, Dataframe and Dataset.

Topics Covered in this Course:

Introduction to Apache Kafka
- Kafka and its importance in Big Data field
- Configuring Kafka Cluster
- Kafka features, concepts, architecture and components.
- Kafka Producer, Consumer, and Kafka Internals.
- Hands-On
  - How to produce messages?
  - How to consume messages?
Apache Spark Streaming
- What is Spark Streaming and why is Streaming necessary?
- Brief overview on DStreams and Transformation on DStreams
- Spark Streaming Features and Workflow
- Window Operators
- Slice, Window, ReduceByWindow Operators Stateful Operators
- Hands-On
  - Twitter Hash Tag analysis
Machine Learning using Spark MLLib
- What is Machine Learning and why is it needed?
- Where is Machine Learning used?
  - Different Types of Machine Learning Techniques
- Introduction to MLlib
- Various ML algorithms supported by MLlib
- Hands-On
  - Supervised Learning: Linear Regression, Logistic Regression, Decision Tree, Random Forest
  - Unsupervised Learning: K-Means Clustering and how it works with MLlib?

Reporting and Prediction of Big Data Details >

Course Title	BIG DATA – Reporting and Prediction
Course number	900-059-EQ-01
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	September 15,17,22,24,29; October 1,6,8,13,15,20,22,27,29: November 3
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

Data Science – Level 1 Details >

Course Title	Data Science – Level 1
Course number	900-080-EQ-01
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	November 3,5,10,12,17,19,24
Instructor	José Rafael Porras
Room	Online format
Schedule	Tuesday & Thursday: 6 p.m. – 9:30 p.m. First class on November 3 : 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 2 Details >

Course Title	Data Science – Level 1 – Section 2
Course number	900-080-EQ-02
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	November 2,4,9,11,16,18,23
Instructor	José Rafael Porras
Room	Online format
Schedule	Monday & Wednesday: 6 p.m. – 9:30 p.m. First class on November 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.

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-01
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	November 26; December 1,3, 8,10,15,17
Instructor	Diego Perea – Ph.D.
Room	Online Format
Schedule	Tuesday & Thursday: 6 p.m. – 9:30 p.m. First class on November 26 : 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.

No SQL- Mongo DB Details >

Databases (“SQL Series”)

*Schedule subject to change

SQL – Introduction Details >

SQL – Intermediate Details >

SQL – Advanced Details >

SQL Analysis Services (SSAS) Details >

Course title	SQL Analysis Services (SSAS)
Course number	900-049-EQ-01
Platform	Windows
Duration	24 hours
Emploi-Québec fee (taxes incl.)	$48
General public fee (taxes incl.)	$433.50
Schedule	Wednesday: 6 p.m. – 10 p.m. Saturday: November 21 & 28:1p.m. – 4 p.m. Saturday, December 5: 9 a.m. – 3:30 p.m. (30 minute lunch).
Dates	November 18,21,25,28; December 2, 5
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

SQL Server Integration Services (SSIS) Details >

Course Title	SQL Integration Services (SSIS) – Introduction
Course Number	900-083-EQ-01
Platform	Windows
Duration	21 hours
Gouvernement du Québec fee (taxes incl.)	$42
General Public fee (taxes incl.)	$373.91
Schedule	Monday,& Wednesday: 6 p.m.-10 p.m.; Saturday: 9 a.m.-3:30 p.m. (30 minute lunch). December 16 : 6 p.m.-9 p.m.
Dates	December 7,9,12,14,16
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

Linux

*Schedule subject to change

Linux – Operating System Details >

Course Title	Linux – Operating System
Course Number	900-003-EQ-01
Platform	Linux
Duration	40 hours
Emploi-Québec fee (taxes incl.)	$80
General Public fee (taxes incl.)	$722.50
Schedule	Tuesday : 6 p.m. – 10 p.m.
Dates	September 22, 29; October 6, 13, 20 ,27, November 3,10, 17,24
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.

Programming Language

*Schedule subject to change

Python – Level 1 Details >

Python - level 1 – section 2 Details >

Python – Level 1 – Section 3 Details >

Python – Level 2 Details >

Python – Level 2 – Section 2 Details >

Python – Level 2 – section 03 Details >

Python – Level 3 Details >

MS Project

*Schedule subject to change

Project Management (MS Project) Details >

Course Title	Project Management (MS Project)
Course number	900-089-EQ-01
Platform	Windows
Duration	66 hours
Gouvernement du Québec fee (taxes incl.)	$132.00
General public fee (taxes incl.)	$1584.45
Schedule	TBA
Dates	TBA
Prerequisites	Good Knowledge of computer systems and their components
Target audience	Independent entrepreneurs leading their own businesses Individuals just starting out in a leadership position (or already in a leadership position) and needing to sharpen their understanding of projects management fundamentals Employees wanting to move ahead in their present company by acquiring project management tools to build their career Individuals seeking general knowledge on project management to apply it directly or indirectly in their workplace
Instructor	Peter Hanna
Location	TBA
NB: This is a non-credit course. Certificate provided for all participants who complete 80% of course hours.

Course description Please note that this is a non-credit course.
This course will provide project management skills that are required in today’s workplace where professionals & independent entrepreneurs face challenges to manage various projects with different magnitudes. this hands-on course, based on theoretical material presentation, workshops and real life context examples is for participants who wish to better understand how to apply the tools, techniques and methods required for the management of projects.

Topics to be covered include:
In this comprehensive course, Participant will learn: The fundamentals of project management: how to initiate, plan, execute, control and close a project in order to meet the objectives of the involved stakeholders During these project phases, you will learn: how to manage time/scope/cost trade-offs, perform risk assessment and planning, ensure project quality, guarantee effective communications between stakeholders and manage your procurement channels while managing your resources and keeping your stakeholders satisfied how to use Microsoft Project (MS) and all its related details as an effective project management tool Apply this theoretical material practical example using Microsoft Project in a real-life group project

Weekly Topics Please note that the instructor reserves the right to modify this schedule.
	Material
	Introduction to Project Management Fundamentals Project Integration Project Scope Project schedule
	Microsoft Project (MS): introduction MS: working with tasks MS: critical path Practice MS material learned in lab
	MS: working with resources MS; setting project baseline MS: tracking progress Practice MS material learned in lab
	MS: tasks types MS: generating reports Working in group on Demo-Project(s) using MS
	Work in groups on Mini-Project using MS
	Project cost earned value management Present Mini-Project [Part #1]
	Quality management Communication management Risk management Present Mini-Project [Part #2]
	Work in group on Final Project using MS
	Human resource management Stakeholder management Procurement management Work in groups on Final Project Using MS
	Present Final Project [Part #1]
	Present Final Project [Part #2] Review concepts & material

Virtualization

*Schedule subject to change

VMware Details >

Microsoft Applications

*Schedule subject to change

VBA for Excel Details >

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 Gouvernement du 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.

*IMPORTANT MESSAGE*

Overview & 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 Gouvernement du 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.

Courses

Window Server Administration

Windows Server 2019

Windows Operating System

Windows Server 2019 – Active Directory

Exchange Server 2016

Windows Security – Introduction

Network Security

Computer Systems Security

BIG DATA

Management and Processing of Big Data – Level I

Management and Processing of Big Data – Level 2

Processing of Big Data with SPARK

Reporting and Prediction of Big Data

Data Science – Level 1

Data Science – Level 1 – Section 2

Data Science – Level 2

No SQL- Mongo DB

Databases (“SQL Series”)

SQL – Introduction

SQL – Intermediate

SQL – Advanced

SQL Analysis Services (SSAS)

SQL Server Integration Services (SSIS)

Linux

Linux – Operating System

Programming Language

Python – Level 1

Python ­- level 1 – section 2

Python – Level 1 – Section 3

Python – Level 2

Python – Level 2 – Section 2

Python – Level 2 – section 03

Python – Level 3

MS Project

Project Management (MS Project)

Virtualization

VMware

Microsoft Applications

VBA for Excel

Refund Policy

IMPORTANT MESSAGE

Python - level 1 – section 2