Data Science And
Analytics Diploma
The Data Science and Analytics Diploma aims to equip students with the skills necessary for working with data through programming and querying languages. It will enable them to clean, query, and analyze large datasets efficiently. Students will gain a solid understanding of machine learning principles and common modeling techniques, as well as develop expertise in both numerical and categorical models to drive business objectives. The diploma also provides valuable insights into some of the most widely used big data tools.
Throughout the diploma, you’ll explore a wide range of topics, including statistical analysis, data manipulation, predictive modeling, and data ethics. Guided by experienced instructors, you’ll become proficient in programming languages such as Python and R, and learn how to extract meaningful insights from large datasets.
This program has been approved by the Private Training Institutions Regulatory Unit (PTIRU) of the Ministry of Post-Secondary Education and Future Skills
Program Hours: Program Weeks: Program Months: | Delivery Method: Campus:
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Domestic Fees Tuition: Application Fee: Administrative Fee: Textbooks Fee: Other Fee: | International Fees Tuition: Application Fee: Administrative Fee: Textbooks Fee: Other Fee: |
* For full tuition breakdown please contact us
46-Week Data Science and Analytics Program
100 hours – Relational Database Design
This course introduces the fundamentals of relational databases, including database structures, design, and management. Students will learn how to create and manipulate databases, focusing on efficient data storage and retrieval.
100 hours – Fundamentals of Analytics
Students will explore essential concepts in data analytics, including data collection, data cleaning, and introductory statistical techniques. This course provides the foundation for understanding how to analyze large datasets to derive insights for business decisions.
100 hours – Predictive and Prescriptive Analytics
In this course, students will learn predictive analytics techniques for forecasting future trends and prescriptive analytics for data-driven decision making. Topics include linear regression, classification models, and optimization methods to support business objectives.
100 hours – Neural Networks, Deep Learning and Big Data
This advanced course covers the foundations of neural networks, deep learning, and big data technologies. Students will gain hands-on experience with neural network architectures and machine learning models used for large-scale data analysis.
100 hours – Introduction to Python Programming
Designed for beginners, this course introduces Python programming with a focus on syntax, data structures, and basic programming techniques. Students will learn how to write code that prepares them for data analysis and machine learning applications.
100 hours – Intermediate Python Programming Techniques
Building on foundational Python knowledge, this course dives into more complex programming concepts, including data manipulation, data wrangling, and key Python packages like Pandas and NumPy, which are essential for data science.
100 hours – Advanced Python Programming Techniques
This advanced Python course introduces students to sophisticated programming techniques, including object-oriented programming, error handling, and performance optimization, preparing students for more complex data science tasks.
100 hours – Python for Data Science
Focusing on Python’s role in data science, this course covers data analysis packages, including Matplotlib, Seaborn, and Scikit-Learn. Students will learn to visualize data, build predictive models, and apply machine learning techniques.
100 hours – Introduction to Project Management
Students will gain an understanding of project management principles, methodologies, and tools to manage data-related projects. This course includes planning, scheduling, and project execution to ensure effective project completion.
20 hours – Career and Employment Skills
This course prepares students for the job market by developing essential skills, including resume writing, interview techniques, and job search strategies, helping students enter the workforce confidently.
1. Is the program focused on specific applications of data science?
The program offers a comprehensive coverage of data science, including topics like statistical analysis, machine learning algorithms, data visualization, and business intelligence. You’ll gain a well-rounded understanding of how data science is applied across various industries and disciplines.
2. How hands-on is the program? Will I be working with real datasets?
Absolutely! The program includes hands-on projects that involve working with real-world datasets. You’ll analyze data, build predictive models, and create data visualizations using industry-standard tools, giving you practical experience relevant to the field.
3. How does the program stay updated with the latest data science trends?
The program is continuously updated to reflect the latest trends and technologies in data science. You’ll be learning about cutting-edge tools, techniques, and best practices that are shaping the current data-driven landscape.
4. Can I continue my education after completing this program?
Certainly! Completing this program will provide a solid foundation for further education in data science, computer science, or related fields. It’s also an excellent starting point for building a professional portfolio and launching a career in data science and analytics.
Upon successful completion of this program, students will demonstrate the ability to:
Understand the concept of relational databases.
Create and manipulate databases.
Understand the syntax of various programming and querying languages.
Understand the packages used for data analysis and machine learning.
Work, manipulate, and model data using programming languages.
Use various tools to build data visualizations.
Use machine-learning techniques to model and build data.
Exposure to deep learning and advanced machine learning models.
Use statistical knowledge to test the accuracy of data models.
Use and implement common big data tools.
Use and implement neural networks.
- Students are required to be 19 years of age prior to the start of the program or possess a high school diploma (or equivalent), and provide evidence of one of the following English proficiency requirements:
- Completion of grades 9-11, including English 10 with a grade of ‘C’ or higher from a country where English is one of the principal languages, or
- Completion of 2 years of full-time post-secondary education at an accredited institution where English is the language of instruction, or
- Provide verified results for one of the English language proficiency tests listed below. Test results must be dated no more than two years before the start date of the program:
- International English Language Testing System (IELTS) Academic – Minimum overall score of 5.5.
- Test of English as a Foreign Language (TOEFL) IBT – Minimum overall score of 46.
- Canadian Academic English Language Assessment (CAEL) – Minimum overall score of 40.
- Canadian English Language Proficiency Index Program (CELPIP) Listening 6, Speaking 6, Reading 5, and Writing 5.
- Duolingo English Test (DET) – Minimum overall score of 95.
- Pearson Test of English (PTE) Academic – Minimum overall score of 43.
- Cambridge English Qualifications: B2 First exam (FCE) – Minimum overall score of 160 or “C”.
- Cambridge Linguaskill – Minimum overall B2 level.
- LANGUAGECERT Academic – Minimum overall B2 level.
- The Michigan English Test (MET) – Minimum overall B2 level.
- iTEP Academic – Minimum overall score of 3.5.
- EIKEN – Minimum placement of Grade Pre-1.
Upon successful completion of this program, students can expect to work as:
- Data Analyst
- Junior Data Scientist
- Database Analyst
- Database Designer
- Data Visualization Analyst
Programs
