Data Science - Arabic - Cornerstone Community College

Become a Data Scientist

In this course, students will learn how to use Python to collect, analyze and communicate large data sets with industry-standard tools. Students will also learn some of the challenges associated with working with datasets that are becoming ever larger and faster moving.

Our data science diploma courses will help students to master the following skills below:

Understand the characteristics of data management in different industries.
Identify the needs for data collection and create appropriate algorithms.
Understand the role of data management and processing in today’s business environment.
Develop and implement data models and algorithms.
Identify the best machine learning model for different datasets.
Understand the future of data science and the impact on new technologies.

Why is Data Science a Good Career?

Data Science jobs are some of the fastest-growing, most in-demand in technology. Since 2012, Data Scientist roles have increased by 650%, and this rise shows no sign of stopping. The U.S. Bureau of Labor Statistics predicts that the demand for data science skills will increase another 27.9% by 2026.

Average Junior Data Scientist Salaries in Canada: $74,713/ year

The professional route that students take will depend on their area of specialization, as well as their interests and comfort levels with various programming languages and frameworks.

Cornerstone will work with each student to discuss their strengths and experience in order to guide them to find a job in the Canadian data science industry.

Technological Requirements.

As a student, you need a personal computer or laptop for both online and in-person classes.

For remote classes, ensure you have reliable internet access; most home internet and WiFi services should work fine. For both online and in-person classes, make sure to get comfortable using your computer before classes start.

For certain courses, you’ll need specialized software. Your instructor will guide you on how to obtain it on the first day of class.

For more information, please access our Bring Your Own Device guide page.

This course will equip students with a comprehensive understanding of Data Science techniques, databases, and data visualization.

Gain a comprehensive overview of the Python language and its ecosystem. It covers topics such as testing and debugging code, Git and version control, and interactive data visualization using Python.

We will offer you an introduction to SQL and Relational Databases. Students will concentrate on setting up, aggregating, and grouping data.

Acquire an in-depth understanding of Big Data, including its definition and its profound impact on the industry.

This course will give students a basic understanding of data science analytics. You’ll learn about statistics, AB Testing, and how to tackle common data science challenges.

This topic offers students a comprehensive introduction to Machine Learning techniques, covering data modeling, dataset preparation, linear regression, and model evaluation. By the end, students will have a solid grasp of the Machine Learning field and the ability to apply basic models for running their own algorithms

This is the chance for students to apply the skills acquired in the Data Science Program, enabling them to create data visualization tools for presenting their project findings.

Potential Job Roles

Data Analyst: Interprets and analyzes data to help improve business decisions, using statistical tools to identify trends and patterns.
Junior Data Scientist: Assists in the analysis and interpretation of complex data to support scientific research or business decisions, under the guidance of senior staff.
Business Intelligence Analyst: Utilizes data analytics to optimize organizational performance, developing actionable insights through reports and dashboards.
Data Engineer (Entry Level): Focuses on building and maintaining data architecture, including databases and large-scale processing systems for efficient data handling.
Machine Learning Engineer (Entry Level): Develops and tests machine learning models and systems that learn from data to improve performance over time.
Data Visualization Specialist: Creates visual representations of data sets to make complex data more accessible and understandable for decision-making.

Admission

Enrollment Requirements:

Applicants must possess a high school diploma or its equivalent; alternatively, they must be at least 19 years old

English Proficiency Requirements:

TOEFL iBT: 79+
DUOLINGO: 120+
IELTS – 6.5+
Qualification for ESL level advanced on the CICCC placement test

International Admission Documents

Online application form
A copy of your passport
High school diploma for candidates under 19 years old
Valid documentation for IELTS/TOEFL/TOEIC results (if applicable)

Domestic Admission Documents

Online application form, completed with a $150 payment
Admissions interview (in-person, online, or by phone)
A completed and signed enrollment agreement
Proof of secondary school graduation or equivalent
A copy of government-issued identification

** Students are required to have their own personal computer

FAQ

To be eligible, applicants must have either a high school diploma or equivalent, or be at least 19 years old and have successfully completed the upper-intermediate level of the ESL program at Cornerstone College. For additional information on language proficiency requirements, please refer to the admissions section of the course details.

Python: A powerful programming language known for its versatility in data analysis, machine learning, and visualization tasks.
SQL: The standard language for database management, enabling effective querying and manipulation of relational databases.

The program implements machine learning, which allows automating the process of analyzing data and making predictions, aiding Data Science specialists in decision-making and problem-solving skills. Studying AI is essential for its demand and the competitive advantage it offers, with versatile and valuable skills that broaden career prospects.

AI is expected to automate routine tasks, create new job roles, augment human work and drive the need for reskilling and upskilling programs. Industries would begin looking for qualified professionals in these areas to increase their impact in the market.

After completing the course, graduates will be equipped with the skills and knowledge needed to excel in various job roles such as:

Data Analyst
Junior Data Scientist
Business Intelligence Analyst
Data Engineer (Entry Level) – May require further knowledge for a higher level
Machine Learning Engineer (Entry Level) – May require further knowledge for a higher level
Data Visualization Specialist – Focused on visual design topics

An average Junior Data Scientist in Canada typically earns $74,713/year on average.

Data Science roles extend beyond the tech sector. Graduates can find opportunities in these industries:

Technology: Tech companies need data analytics for product development, customer insights, and user experiences.
Finance: Financial institutions use Data Science for risk assessment, fraud detection, and algorithmic trading.
Healthcare: Hospitals and research labs utilize Data Science for patient diagnosis, drug discovery, and medical research.
Retail/E-commerce: Retailers and online platforms leverage Data Science for customer segmentation, demand forecasting, and pricing optimization.
Telecommunications: Telecom companies apply Data Science for network optimization and customer churn prediction.

Manufacturing: Manufacturing firms utilize Data Science for process optimization, predictive maintenance, and supply chain management.

Data Science isn’t just for technological industries. It all depends on the skills and aspects gained during the course and the career path students want to follow. Some of the main aspects learned during the course that could be implemented in positions in Data Science related to other industries are:

Technical skills: Learn programming, data analysis, machine learning, and visualization.
Domain knowledge: Understand industries like finance, healthcare, and retail.
Real-world projects: Work on projects simulating industry challenges.
Industry partnerships: Gain experience through internships and projects with companies.

Career guidance: Get help with resumes, interviews, and job placement.

Data Science, Machine Learning, and Computer Science are interconnected but focus on different aspects of technology and analysis. Data Science involves uncovering insights from data, while Machine Learning uses algorithms to enable computers to learn from and make decisions based on data. Both are grounded in Computer Science, the study of how computers operate and how to solve problems using them.