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.
The Data Science Bootcamp encompasses various areas of data analysis:
statistics, business intelligence (BI), scientific methods, artificial intelligence (AI), and data analytics, with the goal of extracting added value from the data.
Real Time Group College's Data Science online course is comprehensive and practical, built upon the extensive experience of the development and training center in artificial intelligence work alongside software development.
The objective of studying Data Science is to produce AI professionals who can be relevant to the job market. The ML & Data Science Bootcamp at the college, which is also a software house, combines professional knowledge, practical experience, logical and analytical thinking, and of course, creativity.
Data Science studies include:
- During the Data Science Bootcamp, we will work with common tools such as the libraries NumPy, SciPy, Pandas, and SciKit learn.
- The college emphasizes up-to-date and evolving syllabi, in line with the technological advancements and shifts in the technology industry.
- All the lessons are recorded, allowing you to watch the recordings at any time and practice the material conveniently.
- The college's instructors assist students in bridging knowledge gaps through private lessons that aim to reinforce each student's theoretical foundation.
- A final project with the Development department: Work on real projects with the company's Development department as part of the Machine Learning Team — gain practical work experience that can be listed on your resume and offer a significant advantage over other candidates.
- The Placement department will help you with resume writing and preparation for personal job interviews.
What Is Data Science?
In Data Science, data is prepared for analysis, collected, and transformed for advanced data analysis. Analytical and data science applications can then review the results to uncover patterns and enable managers to derive meaningful insights about their organization or business initiatives.
The Internet revolution has led to us receiving enormous amounts of information and data every day from a variety of digital sources and applications with every click and interaction online. This volume of data has added value that can lead to conclusions and new business insights, hence the need for its processing, analysis, and monitoring.
In addition, as Data Science focuses on extracting meaningful insights and knowledge from large datasets, these insights can be used in the field of AI to build intelligent systems and algorithms. In Machine Learning, we use statistical and computational techniques and models to make predictions, decisions, and automate tasks.
As a result, the field of Data Science has become highly sought after in recent years among tech companies and various product and technology-oriented businesses. Data Science holds significant importance in the field of AI and Machine Learning and is used by various businesses and organizations to generate added value and business insights from data analysis.
The Role of a Data Scientist
A professional data scientist knows how to analyze data and extract insights from it.
A data scientist filters out relevant information from all the data they are exposed to and is capable of drawing conclusions, trends, and patterns from it.
Due to the need for data processing and analysis in technology companies, the role of a data scientist has become one of the most challenging in the 21st century. A data scientist needs to integrate a wide range of analytical skills and practices to analyze data gathered from the organization, the internet, smartphones, customers, sensors, and other sources of knowledge in order to derive practical insights.
The diverse skills that individuals in the field of Data Science bring to organizations and tech companies provide them with a more comprehensive perspective on the organization's needs and enable effective teamwork.
The definition of the profession of a data scientist has also evolved significantly and undergone changes over the years. The profession started with individuals skilled in algorithms. Later, there was a need for engineers with background in mathematics, analytical skills, programming abilities, and knowledge of statistics.
Today, there is a growing demand for data analysts with analytical and mathematical skills, experience, and familiarity with programming languages such as Python and other software tools for data analysis that have emerged over time.
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 Certification 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 Certification 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 Certification 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.
Who is the Data Science course suitable for?
- College/university graduates who are interested in specializing in Data Science.
- Hardware/software/computer science engineers lacking experience in this field who are interested in professional retraining.
- Individuals with relevant previous background and experience including Python programming and working with the Linux operating system.
- Programmers and computer professionals who want to expand their skillset and delve into the DATA world can take the Data Science Bootcamp.
Certifications and Credentials
Requirements for eligibility for the certificate:
- 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
Upon successful completion of the Data Science program, you will be awarded a Data Science Certificate by RTG. This certificate will attest to your high level of knowledge and professionalism.
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.
Salary Info Data Science
The estimated total pay for an Entry Level Data Scientist is $112,786 per year in the United States area, with an average salary of $102,486 per year.
Source: glassdoor.com