Specialized IT Courses

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IMPORTANT MESSAGE

SET YOUR CALENDARS!

ONLINE REGISTRATION for Fall 2021 Part-Time Specialized IT courses will open on Wednesday, August 11 at 9:00 a.m.

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

CCNA CISCO

*Schedule subject to change

CCNA Cisco –level 1 Details >

Course Title	CCNA Cisco –level 1
Course Number	900-103-EQ-01
Duration Platform Gouvernement du Quebec fee ( taxes incl.)	56 hours Windows $112.00
General public fee (taxes incl.)	$1,014.08
Schedule	Monday ,Tuesday & Thursday : 6 p.m. – 10:00 p.m.
Dates	August 30 to September 28. No class on September 6, Labor Day Class on Wednesday, September 8
Prerequisites	Have a very good working knowledge of the Windows environment and the Internet
Target Audience	Computer network technicians; computer engineering, engineers; IT consultants Cisco level 1 is the first of 3 courses that train individuals to provide level 1, 2, 3 Network and Technical Support in companies. This course is the first of three courses that prepare participants to write the CCNA exam. It is recommended that participants register for level 1, 2, 3 of the CCNA courses

Instructor	Michael Hughes
Location	Online format

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

Course Description

	By the end of this course, the participant will have learned about the key concepts of switching and routing technologies in small business networks, wireless local area networks (WLANs) as well as security concepts. The student will have learned the basics of network configuration and troubleshooting, how to identify and counter threats on the LAN, and how to configure and secure a basic WLAN.

Teaching Methods

	A combination of theory and practical exercises with the use of Cisco quizzes and exams online to prepare for industry certification in the CCNA A certificate of participation is given to people who have attended 80% of the training hours. Participants who score above 75% on online final Cisco Exams in certain modules are eligible for a 60% % reduction on the price of the CCNA exam. It is recommended that participants register for level 1, 2, 3 of the CCNA courses

Required Text / Course Materials

Online Access to the Cisco Curriculum

Topics Covered in this Course

	Networking Concept Overview Network components Network topologies and representations Internet connections Reliable networks Network trends Network security Basic configuration of Cisco switches IP addressing configuration Protocol suites Reference models Role of the physical layer Characteristics of the physical layer Review of Network cabling standards Wireless Technologies Binary system and Hexadecimal system Practical module and questionnaire Data Link Layer Introduction to Routing Configuration of a Cisco router IPv4 addressing Network segmentation VLSM IPv6 addressing Network testing commands Transport layer Application, Presentation and Session Layers Fundamentals of Network Security Threats and security breaches Network attacks Mitigation of network attacks Troubleshooting methodologies and scenarios

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CCNA Cisco –level 2 Details >

Course Title	CCNA Cisco –level 2
Course Number	900-104-EQ-01
Duration Platform Gouvernement du Quebec fee ( taxes incl.)	56 hours Windows $112.00
General public fee (taxes incl.)	$1,014.08
Schedule	Monday ,Tuesday & Thursday : 6 p.m. – 10:00 p.m.
Dates	October 4 to November 4 No class on October 11, Thanksgiving
Prerequisites	Completion of CCNA Level 1 –version 7 or equivalent knowledge Have a very good working knowledge of the Windows environment and the Internet
Target Audience	Computer network technicians; computer engineering, engineers; IT consultants Cisco level 2 is the second of 3 courses that train individuals to provide level 1, 2, 3 Network and Technical Support in companies. This course is the second of three courses that prepare participants to write the CCNA exam. It is recommended that participants register for level 1, 2, 3 of the CCNA courses

Instructor	Michael Hughes
Location	Online format

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

Course Description

	By the end of this course, the participant will have learned about the key concepts of switching and routing technologies in small business networks, wireless local area networks (WLANs) as well as security concepts. The student will have learned the basics of network configuration and troubleshooting, how to identify and counter threats on the LAN, and how to configure and secure a basic WLAN.

Teaching Methods

	A combination of theory and practical exercises with the use of Cisco quizzes and exams online to prepare for industry certification in the CCNA A certificate of participation is given to people who have attended 80% of the training hours. Participants who score above 75% on online final Cisco Exams in certain modules are eligible for a 60% % reduction on the price of the CCNA exam. It is recommended that participants register for level 1, 2, 3 of the CCNA courses

Required Text / Course Materials

Online Access to the Cisco Curriculum

Topics Covered in this Course

	Basic Cisco and router device configuration Secure remote access Verification of directly connected networks Switching concepts Overview of virtual local area networks (VLANs) VLANs in a multi-switch environment VLAN configuration VLAN trunks DTP (Dynamic Trunking Protocol) Inter-VLAN routing Inter-VLAN routing with the Router-on-a-stick method Configure inter VLAN routing using Layer 3 switches Troubleshooting inter-VLAN routing STP Concepts Operation of the STP protocol EtherChannel Concepts EtherChannel configuration EtherChannel verification and troubleshooting DHCPv4 concepts Configuration of a Cisco IOS DHCPv4 server SLAAC • DHCPv6 Configuring the DHCPv6 server First hop redundancy protocols HSRP Layer 2 security threats Implementation of Switch security Mitigation of DHCP attacks Mitigation of STP attacks Introduction to Wireless WLAN components Operation of a WLAN network Threats to the WLAN network Secure WLANs WLAN configuration WPA2-Enterprise configuration of a WLAN on the WLC Troubleshooting WLAN Network Problems Practice and summary of the module Routing concepts Packet forwarding Review of basic router configuration IP routing table Static and dynamic routing Configuration of static IP routes Configuration of default static IP routes Configuration of static floating routes Configure static host routes Troubleshooting static and default route configuration

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CCNA Cisco –level 3 Details >

Course Title	CCNA Cisco –level 3
Course Number	900-105-EQ-01
Duration Platform Gouvernement du Quebec fee ( taxes incl.)	56 hours Windows $112.00
General public fee (taxes incl.)	$1,014.08
Schedule	Monday ,Tuesday & Thursday : 6 p.m. – 10:00 p.m.
Dates	November 8 – December 7
Prerequisites	Completion of CCNA Level 1 & level 2–version 7 or equivalent knowledge Have a very good working knowledge of the Windows environment and the Internet
Target Audience	Computer network technicians; computer engineering, engineers; IT consultants Cisco level 3 is the last of 3 courses that train individuals to provide level 1, 2, 3 Network and Technical Support in companies. This course is the second of three courses that prepare participants to write the CCNA exam. It is recommended that participants register for level 1, 2, 3 of the CCNA courses

Instructor	Michael Hughes
Location	Online format

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

Course Description

	By the end of this course, the participant will have seen the architecture, components, operations, and security of large complex networks, including wide area network (WAN) technologies. The course emphasizes network security concepts and introduces network virtualization and automation. The participant will learn how to configure, troubleshoot, and secure corporate network devices. The participant will learn how application programming interfaces (APIs) and configuration management tools enable network automation and will have a solid understanding of networking concepts and skills.

Teaching Methods

	A combination of theory, practical and online quizzes and exams. A certificate of participation is given to people who have attended 80% of the training hours. At the end of this training (Cisco level 1, 2 & 3), the participant will be ready to take the Cisco CCNA certification exam. Participants who score above 75% on online final Cisco Exams in certain modules are eligible for a 60% % reduction on the price of the CCNA exam.

Required Text / Course Materials

Online Access to the Cisco Curriculum

Topics Covered in this Course

	OSPFv2 concepts: Single Area OSPFv2 and Multiarea OSPFv2 Network security concepts ACL Access Control Lists Concepts Configure ACLs for IPv4 NAT for IPv4 WAN Concepts VPN and IPsec concepts Qo’s Concepts Network management Network architecture and troubleshooting Network virtualization and Network automation

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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.)	$869.21
Schedule	Saturday & Sunday : 9 a.m.- 3:30: p.m. (30 minute lunch)
Dates	Saturday: September 11, 18, 25; October 2, 16 Sunday: September 12; October 3, 17 No class on October 9
Prerequisites	Comfortable in Linux environment, some programming experience is required (ideally JAVA )
Target Audience	Data base analysts and administrators, IT analysts and consultants, programmers and developers, computer engineers, computer network technicians
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 Single Node 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 Hive – Analysis and Data Warehousing tool in Hadoop Ecosystem

What is Hive? – Architecture & Components Overview
Hive history and its components
Comparison with Traditional Database
Hive Installation and Configuration on Google Cloud
Hive Meta-store & Engine Configuration.
Hive Data Types and Data Models
Hands on Hive – orderBy, clusterBY, distributeBy and so on
Data Manupulation with Hive
Hive SerDe
Hive – internal vs external tables
Different file formats – Avro, JSON, parquet, RC, ORC

Hive Case Study – 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 Case Study – Retail data analysis, Consumer Complaints analysis

Apache HBase – Introduction to NoSQL DataBase

Difference between RDBMS and NOSQL
HBASE architecture
HBase – Hands On

Integration of Hadoop Ecosystem

Integrate with Sqoop and Hive
Integrate with Hive and HBase

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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-01
Platform	Linux
Duration	45 hours
Gouvernement du Québec fee (taxes incl.)	$90
General Public fee (taxes incl.)	$814.89
Schedule	Saturday: 9 a.m. – 4 p.m. (30 minute lunch) Sunday: 9 a.m. – 12 p.m.
Dates	Saturday: October 23, 30 , November 6, 13, 20, 27 Sunday: November 7, 21
Prerequisites	Absolute prerequisite: Management and Processing of Big data – Level I
Target Audience	Database analysts and administrators , IT analysts and consultants, programmers and developers, computer engineers, computer network technicians
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 enable 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
Installation of Apache Spark in windows
Introduction to PySpark Shell and how to submit a PySpark Job
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

Case Study on Spark RDDs

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
Case Study:
Stock Market data analysis
Aviation data analysis
How to use complex Joins to make analysis

Additional Topics to be covered if time permits:

Integrate Spark with Cloud Services (Mostly GCP)
Spin up a Databricks cluster in GCP
How to read data from BigQuery to Spark DataFrames and perform data analysis

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Processing of Big Data with SPARK Details >

Course Title	Processing of Big Data with SPARK
Course Number	900-107-EQ-01
Platform	Linux
Duration	30 hours
Gouvernement du Québec fee (taxes incl.)	$60
General Public fee (taxes incl.)	$543.26
Schedule	Saturday & Sunday 9:00 a.m. – 3:30 p.m. (30 minute lunch)
Dates	December 4 , 5, 11, 12, 18
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?

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BIG DATA Reporting and Prediction Details >

Course Title	BIG DATA – Reporting and Prediction
Course number	900-059-EQ-01
Platform	MS teams, Tableau Public & 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 14, 16, 21, 23, 28, 30, October 5, 7, 12, 14, 19, 21, 26, 28, November 2
Instructor	Diego Perea – Ph.D.
Room	Online format
Schedule	Tuesday & Thursday : 6:30-9:30
Gouvernement du Québec fee (taxes incl.)	$90.00
General public fee (taxes incl.)	$814.89

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 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: 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) Details >

SQL Analysis Services (SSAS) Details >

SQL – Integration Services (SSIS) – Introduction Details >

SQL – Integration Services (SSIS) – Advanced Details >

SQL – Data Quality Services (DQS) Details >

PYTHON PROGRAMMING LANGUAGE

*Schedule subject to change

Python – Level 1 – section 1 Details >

Python - level 1 – section 2 Details >

Python – Level 2 – Section 1 Details >

Python – Level 2 – Section 2 Details >

Python – Level 3 Details >

DATA SCIENCE

*Schedule subject to change

Data Science – Level 1 – Section 1 Details >

Course Title	Data Science – Level 1 – Section 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	October 25, 27; November 1, 3, 8, 10, 15
Instructor	José Rafael Porras
Room	Online format
Schedule	Monday & Wednesday: 6 p.m. – 9:30 p.m. First class on October 25: 6:30 p.m. – 9:30 p.m.
Gouvernement du Québec fee	$48.00
General public fee	$433.50

Recommended textbook: Class Notes

NB: This is a non -credit course. 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. 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

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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	October 26, 28 ; November 2, 4, 9, 11, 16
Instructor	José Rafael Porras
Room	Online format
Schedule	Tuesday & Thursday: 6 p.m. – 9:30 p.m. First class on October 26: 6:30 p.m. – 9:30 p.m.
Gouvernement du Québec fee	$48.00
General public fee	$433.50

Recommended textbook: Class Notes

NB: This is a non -credit course. 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. 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.

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 23, 25, 30; December 2, 7, 9, 14, 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.

Predictive Analytic methods are at the heart of this new 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 Machine learning in GCP

SOFTWARE TO BE USED

For the course, we will mainly use Python, which is the industry standard for statistical learning that 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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No SQL- Mongo DB Details >

VIRTUALIZATION TECHNOLOGIES

*Schedule subject to change

Managing a Cloud Computing Project Details >

Course Title	Managing a Cloud Computing Project
Course Number	900-064-EQ-01
Platform	Windows
Duration	30 hours
Gouvernement du Québec fee (taxes incl.)	$60
General Public fee (taxes incl.)	$543.26
Schedule	Saturday & Sunday 9 a.m. –5 p.m. ( 30 minute lunch)
Dates	September 11, 12, 18, 19
Prerequisites	Basic understanding of networking concepts and the Internet
Target Audience	IT professionals
Instructor	Michael Hughes
Location	Online format

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

Recommended Textbook

Textbook provided by the teacher.

Course Description

Please note that this is a non-credit course.

This training course uses mainly Microsoft Technology to help you in the understanding and building of the following topics: understanding the cloud; enabling Microsoft cloud services, configuring Microsoft cloud services; and supporting cloud users.

Students who are beginning studies in cloud technology need a strong foundation in the basics before moving on to more advanced technology courses. The Google/Amazon platform will be used for specific presentations and for contrast.

Upon completion of this course, participants will be familiar with key cloud services and terminology, Microsoft cloud services, Microsoft Office 365 and be able to use, configure, or support parts of other key Microsoft technologies for cloud services. In addition to the theoretical expose, participants will have hands on practice with virtualization and nested virtualization.

Based on specific interest the student will have access to and in most cases be able to use such services as Teams, Yammer, Blog, Sway, Forms, Site web applications, Onedrive, Skype PowerApps, Planner Intune, BI , Staff Notes just to mention a few. In general the student will have access to and use most services provided with Office 365 Business.

Topics Covered in this Course

The Seven Business Models – evolution to Cloud computing
i. Software Economics
ii. Cloud Computing
iii. Internet of Things (IoT)
Cloud Computing is a business model and virtualization is a technology
When and how should Office 365 be used
Relationship between datacenters and cloud explained
Changes to computer thinking and IoT
Hands-on exercises are done using Office 365 and visualization software. (Each student has a JAC O365 account and will be provided with most software and documents as is available to the College.)
“What is Cloud Computing?”
· Defining Cloud Computing· Understanding Distributed Application Design· Understanding Resource Management Automation· Understanding Virtualized Computing Environments· Understanding High-Performance Computing Environments· Understanding Cloud Computing Technologies

This session introduces the basic characteristics of cloud computing

Chapter 2, “Cloud Models”

Planning Organizational Roles in the Cloud
Identifying Cloud Deployment Models
Including Future Cloud Models

This session introduces the four basic cloud deployment models – public, private, community and hybrid.

Chapter 3, “Service Models”

Categorizing cloud services
Examining Software as a Service
Examining Platform as a Service
Examining Infrastructure as a Service
Identifying Emerging Cloud Database Capabilities
Defining Everything as a Service

This session introduces the three basic cloud service models – Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS). It also introduces other as a service cloud models, such as Database as a Service (DBaaS) and Everything as a Service (XaaS).

Chapter 4, “Current Cloud Technologies”

Comparing Traditional Technologies and Cloud Alternatives
Accessing the Cloud
Leveraging Software as a Service
Developing within Platform as a Service
Implementing Infrastructure as a Service
Empowering Mobile Computing

This session gives examples of each of the three main cloud service models used.

Chapter 5, “cloud Business Value”

Identifying Business Driver for Cloud Computing
Examining the Business Impact

This session looks at how to evaluate the possible benefits of cloud computing to a business from an organizational and financial point of view.

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Microsoft Azure – Fundamental Concepts 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.)	$814.89
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	October 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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Amazon Web Services Details >

Container Based System Architecture: Docker Details >

Hyper-V Details >

MICROSOFT APPLICATIONS

*Schedule subject to change

VBA for Excel Details >

WINDOWS SERVER ADMINISTRATION

*Schedule subject to change

Windows Server 2019 Details >

Windows Operating System 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	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)	$320.28
Schedule	Monday & Wednesday: 6:00 p.m. – 10:00 p.m. Saturday: 9 a.m. – 2:30 p.m. (30-minute lunch)
Dates	December 6, 8, 11, 13, 15

Prerequisites	SQL Intermediate or working knowledge of relational databases (database design skills)
Target Audience	This course is intended for database professionals who need to fulfill a Business (BI) Intelligence Developer role. They will need to focus on hands-on work creating BI solutions including data cleansing by using 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*
This instructor led course describes how to use SQL Server Data Quality Services (DQS) to cleanse, match, standardize, and enrich data, so you can deliver trusted information for business intelligence, a data warehouse, and transaction processing workloads.

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.)	$271.73
Schedule	Saturday & Sunday : 9 a.m.-5 p.m. ( 30 minute lunch)
Dates	November 20, 21
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

*IMPORTANT MESSAGE*

Courses

CCNA CISCO

CCNA Cisco –level 1

CCNA Cisco –level 2

CCNA Cisco –level 3

BIG DATA

Management and Processing of Big Data–Level I

Management and Processing of Big Data – Level 2

Processing of Big Data with SPARK

BIG DATA Reporting and Prediction

DATABASES (SQL SERIES)

SQL – Introduction

SQL – Intermediate

SQL – Advanced

SQL – Reporting Services (SSRS)

SQL Analysis Services (SSAS)

SQL – Integration Services (SSIS) – Introduction

SQL – Integration Services (SSIS) – Advanced

SQL – Data Quality Services (DQS)

PYTHON PROGRAMMING LANGUAGE

Python – Level 1 – section 1

Python ­- level 1 – section 2

Python – Level 2 – Section 1

Python – Level 2 – Section 2

Python – Level 3

DATA SCIENCE

Data Science – Level 1 – Section 1

Data Science – Level 1 – Section 2

Data Science – Level 2

No SQL- Mongo DB

VIRTUALIZATION TECHNOLOGIES

Managing a Cloud Computing Project

Microsoft Azure – Fundamental Concepts

Amazon Web Services

Container Based System Architecture: Docker

Hyper-V

MICROSOFT APPLICATIONS

VBA for Excel

WINDOWS SERVER ADMINISTRATION

Windows Server 2019

Windows Operating System

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

Python - level 1 – section 2