Pay your tuition in advance through self-payment or with the help of your employer.
Numerous employers provide financial assistance for coding programs, so it is advisable to inquire with your employer regarding tuition support.
Data Science Course Overview
Our Data Science online course is meticulously designed to propel you into a professional career in data science, one of the most in-demand fields in today's tech-driven economy. Focused on the core principles of data analysis, this course dives into statistical modelling, predictive analytics, machine learning, and data visualization techniques. You'll master essential tools and programming languages including Python and SQL, which are crucial for handling big data environments effectively, equipped with valuable insights.
The course curriculum is structured to bridge the gap between theoretical knowledge and practical expertise. Through collaborative projects and case studies, you will apply your skills to real-world data challenges, gaining the experience needed to solve complex problems. This hands-on approach not only enhances your understanding but also prepares you to enter the industry as a skilled data scientist, ready to contribute to and lead data-driven projects in any organization.
Skills You Will Gain Completing Our Data Science Training Program
- Expertise in Python and SQL for data manipulation and analysis.
- Proficiency in using machine learning algorithms to solve business problems.
- Ability to create impactful data visualizations and dashboards.
- Skills in big data technologies for handling large-scale data sets.
- Understanding of statistical models for predictive analytics.
As a Qualified Our Data Science Course Graduate, You’ll Be Prepared for Roles Such As:
- Data Scientist
- Data Analyst
- Machine Learning Engineer
- Business Intelligence Analyst
- Quantitative Analyst
Industries You’ll Be Able to Work in With Our Online Data Science Course
- Technology and Software
- Financial Services
- Healthcare
- Marketing and Sales
- Government and Public Sector
Alex Shoihat
Head of Machine Learning
Alex holds a bachelor's degree in Information Systems (B.Sc.) and a master's degree in Electrical and Electronics Engineering.
Alex is a Machine Learning Engineer at RT. He specializes in the AI field, with over 13 years of experience in project development, management, and transitioning from development to production in various domains such as Linux Embedded.
Alex has experience working with the integration of Machine Learning and Deep Learning in the Computer Vision and Data Analysis field.
Data Science Curriculum
The Data Science Course program is made up of a number of courses (modules).
We know that each of us arrives with a different background and level of knowledge. In order to tailor the content to best fit your needs, you can choose the track that is most suitable for you:
Data Science Course studies at the college are currently conducted online.
Comprehensive Track
This track includes all the courses in the program and is designed to make you an expert in the field.
- Designed for students with little to no experience.
- Requires 9 to 12 months to complete.
- 335 academic hours.
Standard Track
This track includes only the courses and content specifically designed for the Data Science Course program.
- Designed for students with prior knowledge in the high-tech industry.
- Requires 6 to 7 months to complete.
- 155 academic hours.
Self-Designed Track
This track allows you to select only the specific courses and content that you are interested in and wish to complete.
- Designed for students with experience in the field.
- Requires 1 to 3 months to complete, depending on the courses chosen by the student.
| Machine Learning & Data Science | |||||
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| Machine Learning Fundamentals | 25 academic hours |  Comprehensive Track |
 Standard Track |
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This course presents the theory and fundamentals of Machine Learning, as well as recommended working methods for ML. | |||||
| Scientific Python | 30 academic hours | Comprehensive Track |
Standard Track |
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The "Scientific Python" course at RTG College is part of an advanced learning program that equips students with the necessary skills for advanced analysis of scientific data. Throughout the course, students develop proficiency in the Python programming language and practice using libraries like NumPy, Pandas, and Matplotlib for data processing and analysis. The knowledge is conveyed through hands-on projects, allowing students to apply the knowledge and develop solution-focused abilities in a practical environment. At the end of the course, students conduct in-depth analysis and present their findings in a final project. The course provides you with the tools for breakthroughs in the complex world of data analysis. | |||||
| Machine Learning With Python | 50 academic hours | Comprehensive Track |
Standard Track |
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Python offers a wide range of libraries that can be used for Machine Learning (such as NumPy, SciPy, Matplotlib). In this course, you will learn how to implement the tools from these libraries. Using these tools, we will also implement models that were taught in previous courses. | |||||
| Deep Learning with Tensorflow | 50 academic hours | Comprehensive Track |
Standard Track |
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Deep Neural Networks draw inspiration from the way the human brain functions and represent the most advanced subset of Artificial Intelligence. In this course, you will learn how to develop and test Deep Learning models using the TensorFlow / Keras platforms for Machine Learning and Neural Networks projects. | |||||
| Software Programming Courses | |||||
| Python | 90 academic hours | Comprehensive Track |
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The focus is on Python 3, aiming to provide the knowledge and experience required for programming real-world applications in an object-oriented industry. You will learn how to develop software using Python. You will be taught techniques and appropriate tools to professionally develop high-level Python programs suitable for high-tech companies. This is a very practical course in which we will also be using circuit boards. Python is currently considered one of the most popular and sought-after programming languages in the IT industry. Its popularity and widespread use in various industry projects make Python one of the most demanded programming courses. The high demand and diverse employment opportunities make Python highly beneficial to specialize in for a rewarding professional career with multiple growth opportunities. | |||||
| SQL | 30 academic hours | Comprehensive Track |
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In this course, you will learn and practice SQL (Structured Query Language) and gain thorough familiarity with MySQL. The goal of the course is to learn how to communicate and perform various operations with the database. Most software operates with large amounts of data in the background. Nowadays, this data can be stored in different types of databases, like MySQL or Oracle in the backend. During software testing, some of this data needs to be verified, for example, to check if the relevant data is stored correctly in the databases. Therefore, knowledge of database basics and SQL queries is essential. In the course, we will cover topics such as SQL Formal Definitions, The Relational Model, SQL Key Notes, SQL Properties, SQL User Objective, Data Definition Language, and more. | |||||
| AWS | 35 academic hours | Comprehensive Track |
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The course is designed to help you gain a deep understanding of the architectural principles and services of Amazon Web Services (AWS). You will learn how to design and deploy AWS cloud applications using recommended best practices endorsed by Amazon. | |||||
| GIT (Version Control) | 25 academic hours | Comprehensive Track |
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Git is an open-source version control system that serves as a tool for managing code versions and the software development process. Its primary purpose is to help developers efficiently manage code and track changes in software files. In this course, you will learn the core features of Git, workflow techniques, and methods to undo changes or maintain multiple project versions. Additionally, you'll discover how to collaborate effectively with other teams and developers. Designed for programmers seeking the best and most suitable way to manage code development versions, the course covers essential workflow principles, core features, version control, collaboration, and more. | |||||
Admission Requirements
- Basic computer skills (operating Windows OS).
- Basic knowledge in mathematics and statistics (no need to be a mathematician, just the basics).
- No previous programming experience is required.
Certifications and Credentials
Students must complete the following to be eligible for Data Science certification:
- Participation in at least 80% of the course hours
- Submission of a final project / final exam with a score of 70 and above
- Obligation to submit course assignments, including exercises, homework, and projects
Why Study Data Science at RTG
Paying Tuition for Data Science
Some students turn to personal loans to fund their coding education.
A diverse range of personal lending choices is at your disposal for careful consideration and assessment. Should you opt for a personal loan, be sure to select the one that aligns most effectively with your individual circumstances and financial goals.
Your eligibility for various government aid and scholarship programs may vary depending on your location.
As such, it is recommended that you explore the options and inquire about any scholarships or financial aid opportunities available.
Top Companies Where Our Graduates Work
FAQ
1. Understand the main goal and intermediate objectives of the project.
2. Collect data from various sources.
3. Organize the data at your disposal: cleans, filters, and arranges.
4. Construct a model that theoretically should achieve the project's goals.
5. Train the system on the data portion and evaluate the learning results.
6. Validate the model to achieve the goals with unfamiliar data.
7. In case the system did not sufficiently achieve the goals, start again from the first stage.
1. Hardware Dependence: Deep learning requires more 'powerful' hardware to perform learning within a reasonable time. Machine learning is less dependent on hardware.
2. Learning Time: Deep learning requires a longer learning time compared to the learning time of machine learning.
3. Task Execution Time: After the learning process is completed, deep learning performs the required task much faster than machine learning.
4. A machine learning system operates based on a certain algorithm, whereas a deep learning system operates without one.
Data scientists have knowledge and experience working with:
1. Machine Learning algorithms, such as SVM (Support Vector Machine), Decision Trees, etc.
2. Various types of databases (relational and/or non-relational), like SQL, MongoDB, etc.
3. Different programming languages, such as Python, R, etc.
4. Messy, outdated, and missing data.
5. Complex and multifaceted functions.