Data science education with mBlock
As a STEAM teacher, you are probably seeking out new methods to get your students into science, technology, art, math, or engineering (STEAM) subject. Whether it’s AI for kids, Arduino programming, or creating games, we are all trying to find the best way to teach students the world around us.
Teaching STEAM is more than for a better career choice. It’s more for a way of problem-solving and logical thinking.
We see the booming of big data and data science these years. Although data science education is fairly new, it’s critical to teach students to think in a statistical and data-driven way early in their lives. Data science is everything now, even if just a buzzword. So, what actually is data science, and why do we need to teach data science to our students?
What is Data Science?
Data science is solving problems using data: identifying the problem, collecting the data, processing the data, analyzing the data, getting the result of the analysis. As our lives have been digitalized, big data has emerged and completely revolutionized the way we work and live. Every time we call, search, click… we add data to the data mountain. And the data becomes too much to handle using traditional technologies. The rise of big data sparks the rise of data science to support the needs of data application, as in to solve all kinds of problems. Big data is nothing until the data professionals who mine and turn data into actionable insights. Data science is all about using data to create as much impact as possible. The impact can be in lots of different forms, such as insights, data products, or product recommendations for a company. To do these things, then we need to use methods like building data models or data visualization.
Why do we need to teach data science?
Teaching computer programming can be a critical part of the K12 curriculum, which is a good way to teach computational thinking and also valuable for college education and even for careers. For students in high school, coding can be a career skill teaching in Career and Technical Education (CTE). The central problem with the CTE for coding education is that it’s challenging to keep up with the hottest languages for job hunting. As data science is reaching into every industry, the employment opportunities for data professionals could be widely broadened.
Compared with computer programming, data science is more discovery-oriented, which needs students to discover, communicate, and interpret data, while computer programming is usually directed-task-oriented. As data science often comes with lots of big data set, it also requires strong collaboration skills
“Don’t teach everyone how to code. Teach them how to identify and understand needs, as well as how to visually express logic. Teach them how technology works, so they can understand the realm of possibility and then envision game-changing innovations.”-- Gottfried Sehringer, marketing VP for a web development firm
Quote from “Should We Really Try to Teach Everyone to Code?”
What does mBlock have for data science education?
For data science education in grades K-12, mBlock has made a lot of effort for educators to bring data science into the classroom. As a block-based coding platform, mBlock allows teachers to teach computer programming from block-based coding to Python coding. (Python is very common for data crawling and data analysis.) Most importantly, mBlock is equipped with many extensions for different data science education purposes, including data collection and logging, data visualization, and data analysis.
Use mBlock for data collection and logging
- Climate Data Extension: enables you to collect real-time weather data of different cities, including temperature, humidity, and air quality data
- All kinds of sensors extensions: support 13 different types of sensors, including light, motion, temperature, humidity, etc. (See all the sensors >>)
- OCR (Optical character recognition): We integrated Microsoft Cognitive Services extensions allows you to convert voice into text that can be edited and recognize images as well. (Learn more about Cognitive Services extension>>)
- Text translation: enables you to translate texts into the target language. “Text translation” is especially useful for sentimental analysis.
Use mBlock for data analysis and data visualization
- Data Chart: With the “Data Chart” extension, you can visualize the data you collect from online and offline in real-time.
- Google Sheet: mBlockis integrated with Google Sheet. When you create a project in mBlock to read sensors data like temperature, mBlock allows you to log and store data to a Google Sheet spreadsheet.
- Microsoft Cognitive Services: “Cognitive Services” extension brings AI to you without requiring machine-learning expertise. You can create an interactive AI project such as emotion recognition, speech recognization, handwritten text recognization, etc..
- Teachable Machine: mBlock is integrated with Google’s Teachable Machine to make AI easier for students. “Teachable Machine” lets you train your own machine learning with dragging and dropping blocks.
At its core, the essential thing for data science education is to let students engage in data-driven and problem-solving thinking. It’s quite unrealistic to expect students (especially in elementary and middle grades) to learn all the aspects of data science. Hence as educators, we need to understand how we can use appropriate tools and techniques for each grade level.
As a coding platform designed for STEAM education, mBlock will continue to provide you with the best tool and techniques for STEAM education and data science education.
Author: The mBlock Team
（Cover image from https://www.datanami.com/ via Google search)