UNP Junior

As per the World Economic Forum, by 2025, the world will generate 463 exabytes of data each day. Therefore, data handling and interpretation skills are going to be indispensable. Moreover, such skills empower in several ways, like making data-driven decisions instead of being guided by other’s opinions, present arguments confidently utilizing data, and definitely, such skills have a monetary value.

At UNP, after considerable deliberation and research, it’s apparent to us that the data analytic skills should start from secondary and high school students. We pondered on several points, including whether the secondary school, i.e. grades 9 and 10, is too early for learning data analytic skills, do students in those grades have sufficient mathematical foundation, is adding another topic going to increase academic pressure. After all the considerations, research and consulting several educators, we concluded that the onus is on us to design the curriculum with the critical objective to spark sufficient interest within students.

The outcome is UNP Jr. -I, and Jr.-II, for grades 9, 10 and 11, 12 respectively. We designed these curriculums keeping three things in mind – spark sufficient interest in data analytics, show varied data sources, and teach the essential software and foundational tools for data analytics. The curriculum is full of joint case studies, visualization, python programming and storytelling with data.

After completing UNP Jr. I, grade 9, 10 students will be able to:

Identify data sources based on the problem at hand
Data handling, management and cleaning of reasonably large data sets
Visualize data through multiple lenses and create dashboards
Program using python
Perform fundamental statistical analysis and generate meaningful insights

After completing UNP Jr. II, grade 11, 12 students will be able to:

Pull out data from open databases and IoT devices
Handle and manage data in cloud infrastructure
Create interactive dashboards and present stories using data
Program using python and create apps to deploy their work
Use fundamental machine learning and data mining techniques to find hidden patterns and perform predictive analytics

More details (Jr. I, Jr. II):

2 x 1.5 hrs virtual classroom with experts
12 weeks (~ 3 months)
Collaborative course project (a must for Jr. I and Jr. II)
Final presentation (must or Jr. II, optional for Jr. I)
Registration link - coming soon