Data Science
Through mastering fundamental data science skills and applying data analysis and machine learning techniques, unlock the power of data, develop practical applications, and achieve data-driven decision-making in daily life.
Age: 14 - 16
S3+
Course Description
The Data Science course is designed for students who have completed the RoboCode Python and Web Programming courses. It progresses from foundational knowledge to advanced applications, aiming to equip students with comprehensive data science skills.
The course covers database design, data analysis, visualization, and machine learning applications. By studying relational database, data analysis, and algorithm design, students will be able to develop machine learning models for real-world applications and perform in-depth data analysis and forecasting.
This course establishes a foundation for students to enter the field of computer science, focusing on enhancing relevant programming skills and advanced computational thinking, while also developing problem-solving abilities to prepare for future careers in technology.
What Will Your Kids Learn?
- Familiarize with Structured Query Language (SQL) syntax and database operations, and learn to develop and manage relational database systems
- Master the application techniques of data science, learning to transform complex data into easily understandable visual information
- Understand and apply machine learning algorithms for data classification and result prediction
RoboCode Uniqueness
Fully Exploring Data Science Applications from Database Construction to Web Application Development
The course teaches students to design and manage databases, use SQL for data querying and management, and integrate these skills with web development to apply data in real-world scenarios.
Analysing and Visualizing Data to Enhance Problem Solving Skills
The course emphasizes the application of data analysis, teaching students to use statistical and visualization techniques to transform analytical results into easily understandable visual information, and apply this knowledge to solve real-world problems.
Developing Machine Learning Algorithms to Uncover Value in Data
The course focuses on teaching students to build machine learning algorithms from the ground up, guiding them in selecting appropriate algorithms for different scenarios. Students will learn to extract key value from complex data, thereby developing their independent thinking and problem-solving skills.
Our Curriculum
Level 1 - In-Depth Study of Database Concepts and Using SQL to Build Real-World Applications
Through MySQL, students will explore the operations of relational databases, mastering the syntax and application techniques of SQL. The course covers performing operations such as adding, querying, updating, and deleting data in a database, and using databases to store data in system development.
- Learn SQL syntax for accurate data querying and modification
- Master the development of Relational Database Management Systems (RDBMS) and complete real-world database design
- Apply knowledge of web development to build real-world web systems utilizing databases
Level 2 - Advanced Data Analysis and Visualization
By studying data processing and visualization techniques, students will acquire essential skills ranging from statistical analysis to image processing. This level focuses on effectively handling, analyzing, and integrating large datasets to extract and present key information.
- Master statistical concepts and learn to use appropriate chart types to display data analysis results
- Gain a deep understanding of image processing techniques, extract key information from images, and convert it into formats commonly used in machine learning
- Learn data cleaning and data format recognition to comprehensively master the standard processes of big data analysis
Level 3 - Advanced Data Structures and Search Algorithms in Game Development
This level focuses on learning advanced data structures and search algorithms, equipping students with techniques to solve complex problems and integrating this knowledge into game development to introduce innovative elements.
- Study advanced data structures and the methods for organizing and processing data
- Master various key search algorithms to enhance the efficiency and accuracy of large-scale data processing
- Apply search algorithms in game development to create more variations and challenges, enriching the gaming experience
Level 4 - Introduction to Machine Learning — Supervised Machine Learning Algorithm
Explore the core concepts of supervised machine learning, learn to develop and optimize models for accurate predictions, and use techniques such as normalization and cross validation to enhance the overall accuracy of the models.
- Understand the fundamentals of supervised machine learning, including concepts such as Naive Bayes Theorem and K-Nearest Neighbors (KNN) algorithm
- Develop and train various supervised machine learning models to achieve predictive capabilities
- Learn to apply common strategies such as normalization and cross validation to improve the accuracy and stability of models
Level 5 - Advanced Machine Learning — Unsupervised Machine Learning Algorithm
Explore the applications of unsupervised machine learning, from model training to practical data processing. Accurately select appropriate machine learning algorithms and master the skills needed to build classification systems and recommendation systems.
- Understand the fundamentals of unsupervised machine learning, including concepts such as K-Means Theorem and Random Forest Algorithm
- Learn to build the fundamental application of unsupervised machine learning models
- Learn to apply common strategies to select the most appropriate machine learning algorithms and build effective recommendation systems
Suitable for
14 - 16 year-old
Every Lesson
90 Minutes
Every Level
12 Lessons
(18 Hours in Total)
Class Size
1:4
(Maximum)
Course Fee
$6840 per Level
Kickstart Your Child's Journey to Innovation and Discovery
Frequently Asked Questions
Do students need to bring their own laptops to classes?
We encourage students to bring their own laptops to class, and the instructor will assist with installing the necessary software. If students do not have a laptop, we can also provide one for them.
Can parents get back students' completed works after class?
Sure, students can save their works to personal storage spaces such as Google Drive or USB flash drives.
Can students practice at home?
Yes, students can access, modify, and run their files on any computer that supports the relevant software.
Are there any additional fees apart from tuition for the course?
No, only tuition is required; there are no extra charges.
What if students need to take leave?
Parents should notify our staffs in advance. We will then arrange makeup class.
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