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Data Science Course

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Start Date: TBD
Academic Timeline: 335 hours | 13 months
Campus: Live online learning model
Meetings Per Week: 2-3
Rating: 4.8

 

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:

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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

 

Why Study Data Science at Embedded Academy

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project development
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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.

Data Science Curriculum

Full Track
Standard Track
Self-Designed Track

Data Science Track: From Novice to Expert

Launch your career in Data Science with our most comprehensive educational track. This program is meticulously designed to provide you with an all-encompassing understanding of the field, ensuring you achieve the highest level of expertise.
Key Features:

  • Holistic Learning: Engage with all modules, gaining a thorough and well-rounded proficiency in Data Science .
  • Beginner-Friendly: Tailored for newcomers with little to no prior experience, providing a supportive environment for those taking their first steps into this dynamic field.
  • In-Depth Immersion: Dive deep into the subject matter over a 9 to 12-month period, allowing ample time for concept absorption and practical application.
  • Extensive Curriculum: Benefit from an impressive 335 academic hours of instruction, covering a wide array of essential topics and cutting-edge techniques.

This track is your gateway to becoming a proficient Data Science expert, equipping you with the knowledge, skills, and hands-on experience needed to excel in this rapidly evolving and in-demand area of technology.

Machine Learning & Data ScienceCourse duration
Machine Learning Fundamentals Course25 academic hours

This course presents the theory and fundamentals of Machine Learning, as well as recommended working methods for ML.

Scientific Python Course30 academic hours

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 Course50 academic hours

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 Course50 academic hours

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 CoursesCourse duration
Python Course90 academic hours

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 Course30 academic hours

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 Course35 academic hours

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) Course25 academic hours

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.

Tuition: $14800.00 $10800.00

Data Science Specialist Track

Elevate your software development expertise with our focused Data Science program. This track is carefully crafted for professionals with prior experience in software and application development, aiming to sharpen your skills in this specialized domain.
Key Features:

  • Targeted Curriculum: Concentrate solely on modules directly relevant to Data Science , ensuring efficient and applicable learning.
  • Accelerated Learning: Designed for experienced developers, this condensed track spans 6 to 7 months, allowing you to augment your existing skill set quickly.
  • Intensive Study: Engage in 155 academic hours of specialized instruction, delving deep into the intricacies of Data Science .

This track offers a streamlined path to mastering Data Science , ideal for seasoned developers looking to expand their expertise or pivot into this high-demand field. Gain the specific knowledge and skills needed to excel in Data Science development and stand out in the competitive tech industry.

Machine Learning & Data ScienceCourse duration
Machine Learning Fundamentals Course25 academic hours

This course presents the theory and fundamentals of Machine Learning, as well as recommended working methods for ML.

Scientific Python Course30 academic hours

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 Course50 academic hours

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 Course50 academic hours

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.

Tuition: $6800.00

Customizable Data Science Expertise Track

Tailor your tech education with our flexible, modular program. This track allows you to focus on specific areas of interest, creating a personalized learning journey.
Key Features:

  • Customized Learning: Select only the courses and content you need.
  • Experience-Based: Ideal for students with prior field experience.
  • Flexible Duration: Complete in 1 to 3 months, based on your course selection.
Machine Learning & Data ScienceCourse duration
Machine Learning Fundamentals Course25 academic hours

This course presents the theory and fundamentals of Machine Learning, as well as recommended working methods for ML.

Scientific Python Course30 academic hours

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 Course50 academic hours

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 Course50 academic hours

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 CoursesCourse duration
Python Course90 academic hours

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 Course30 academic hours

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 Course35 academic hours

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) Course25 academic hours

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.

Head of the department
teacher-image-Alex-Shoihat

Meet your instructor:

Alex Shoihat

Head of Machine Learning

Alex holds a B.Sc. in Information Systems and an M.A. in Electrical and Electronic Engineering.

As a Machine Learning Engineer at Embedded Academy, Alex specializes in the field of artificial intelligence, applying over 13 years of experience in project development, management, and transitioning from development to production in various domains such as Linux Embedded.

Throughout his career, Alex developed his expertise working with the integration of Machine Learning and Deep Learning in the Computer Vision and Data Analysis field.

Data Science Certification

Certification

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

Career Advisory

As part of the professional experience each graduate undergoes we help in integrating them into the high-tech world through our job search assistance, personalized resume building, and placement in a technology company.

1

Curriculum customization

We'll help you choose and structure the right program most suitable for your needs and career aspirations.

Curriculum customization

2

Tech Training

Students who start with no prior knowledge of the field will receive the fundamentals required to complete the course.

Tech Training

3

Final Project

Working on your course project and internship with experienced developers.

Final Project

4

Internship

Get real-world experience adapted to industry standards and requirements with our team of industry-leading engineers.

Internship

5

Job Interview

Graduates get resume-building assistance, interview simulations and career guidance.

Job Interview

What do our graduates say?

teacher-image-Lina-Tomkov

Lina Tomkov
Automation course graduate

Automation Engineer, Mohican Lab

I was invited to work at the academy’s engineering department as an intern and then joined the team as an Automation engineer. I got to apply a lot of scenarios I experienced during the classes and found the hands on practice I needed
teacher-image-Dima-Solomianik

Dima Solomianik
Real Time Embedded course graduate

RT Embedded Engineer, HP

After completing the course I moved onto the job interview preparation process in which I got valuable insights about what employers search for when seeking a talent to take on the role confident in the skills and experience I gained as a student. I got 3 high tech companies getting back to me to proceed to the next level
teacher-image-Alex-K.

Alex K.
Real Time Embedded course graduate

Linux Kernel Developer

The Embedded systems course was very detailed and relevant as I got to work on actual client projects that show industry challenges. I now work as an embedded engineer and the skills I gained are directly applicable to my daily work.
teacher-image-Mia-S.

Mia S.
Cybersecurity course graduate

Incident Response Specialist

The Cybersecurity program was good and also up to date, new tools and threats I read about were given as current examples. The additional interview preparation helped me get into a the dev team I’m in now.
teacher-image-Sarah-L.

Sarah L.
Machine Learning course graduate

AI/ML developer

The Machine Learning course was super practical, from hardcore algorithms to the ethics of AI. Thanks to the professional connections I met during school got me several job offers to check right after I graduate
teacher-image-Tom-R.

Tom R.
Full Stack course graduate

Full Stack developer

I basically came as a coding newbie and found the fullstack development course structured. The project-based learning approach was particularly effective. The career support post-graduation was exceptional. I'm now employed as a junior full-stack developer at a health-tech startup, building meaningful applications.
teacher-image-James-H.

James H.
DevOps course graduate

DevOps developer, Quali

Switching to DevOps seemed stressing at the time but the course was well built to take you from start to bottom.
teacher-image-David-M.

David M.
Embedded System course graduate

Embedded System engineer

Didn’t realy know what I wanted to focus on when applied but my career advisor laid the options down so we could see what’s a good fit for me professionally. I got into the embedded system program and feel good with this path.
teacher-image-Emma-W.

Emma W.
IT course graduate

Linux Administrator, HP

The Linux course really upped my game. The labs were great, I got to see work processes you don’t get only in school.
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FAQ

What are the work stages of a data scientist?

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.

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What is the difference between machine learning and deep learning?

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.

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What does a data scientist know?

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.

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