Bringing Data Science Education to Secondary Schools in India for Zero Cost with Machine Learning

Bringing Data Science Education to Secondary Schools in India for Zero Cost with Machine Learning

Create a base of Indian secondary schools and their email domains to spread the importance of having data science in schools’ curriculum. In this 8-week challenge, you will join a collaborative team of 50 AI engineers.

During the duration of the project, you will be granted free DataCamp Donates access for 1 year. Participants must be committed towards finishing the project to keep the full year of DataCamp Donates access.

 

The context

Data science is needed now more than ever. Changes in the ways we live demand changes in the ways we learn. DataCamp for Classrooms allows teachers and students to get free, comprehensive access to over 380 courses on DataCamp on the most popular technologies, like, Python, SQL, and more. It also gives teachers all the tools to manage their students’ assignments and track their progress. Currently, DataCamp for Classrooms is available to university teachers and students worldwide as well as secondary school teachers and students in the US, UK, Belgium, and Poland.

Now, we want to bring DataCamp for Classrooms to secondary schools in India. To equip the next generation with the skills to thrive, DataCamp is partnering with Omdena to collaborate on a list of tech-ready secondary schools in India that we can give access to DataCamp for Classrooms, enabling teachers to integrate world-class data science education into their curriculum and bridge the country’s skill gap.

 

The problem

DataCamp wants to start integrating data science education into the secondary school system of India at zero cost to its teachers and students. As the world’s second-most populous country, India is poised to grow rapidly in innovations that stem from data science. But this potential is going unrealized because young people don’t have access to the data education resources. Bringing DataCamp for Classrooms to India will be an essential stepping stone to fostering a more data-literate world by educating the youth about the importance of data science and how it can be harnessed. This will lead to more young people being prepared for college, tomorrow’s job market, and generally becoming more autonomous in their decisions regarding data from their own lives. DataCamp believes that everyone, regardless of their background, deserves the education they need to thrive in our data-driven world.

The aim is to gather the names and unique email domains of Indian secondary schools through the use of web scraping and data entry in order to have a comprehensive database of schools whose teachers and students are eligible for DataCamp for Classrooms access. DataCamp provides the online learning platform that these students can use to start learning while being guided by their teachers who ensure their progress and development.

 

The project goals

  • Identify and gather all Indian secondary schools to spread the importance of having data science in schools’ curriculum.
  • Collaborate on creating a robust Google Sheet spreadsheet that contains essential information about the secondary schools in India by August 1, 2022. The spreadsheet should present, at minimum, these two essential data into two separate columns:
    • School name
    • Email domain
  • Additionally, the list could include: 
    • Main email contact
    • School website link
    • Address
    • City
    • Province
    • Postcode
    • Telephone number
  • Schools with their own unique email domains should be prioritized in their own list. Information on schools that don’t have their own email domain can be gathered on a separate sheet but are not a high priority.

 

What is DataCamp for Classrooms?

DataCamp for classrooms enables all higher education teachers and students in any part of the globe to receive free renewable 6-months DataCamp access. Additionally, all secondary education teachers and students in the US, UK, Belgium, and Poland can benefit from this free offer.

Find out more on how to receive 6 months of renewable access to your class here.

 

Why join? The uniqueness of Omdena AI Challenges

A collaborative experience you never had in your working life! For the next eight weeks, you will not only build AI solutions to make a real-world impact but also go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join a global and collaborative team of changemakers. Omdena AI Challenges are not a competition or hackathon but a real-world project that will take your experience of what is possible through collaboration to a new level.

 

Find more information on how an Omdena project works

 

Building Conscious AI for Sophia Robot

Building Conscious AI for Sophia Robot

This challenge requires experience in one or more of the following – familiarity with conscious AI research, consciousness, cognitive architecture, AI ethics, and advanced deep learning.

 

The problem

Humanity is facing a lot of challenges due to AI. We can already see AI algorithms suppressing people’s opinions and trying to control us. So to face future challenges, from bad actors (and AI), we have to build compassionate algorithms. Otherwise, we will make intelligent machines that kill, fight and suppress each other.

 

The project goals

The first step of building a compassionate AI is building a conscious AI. There is already much research done on conscious AI and this project will explore different existing approaches and implement the most promising ones. The project also aims to further the research and development with the potential to try findings on the Sophia Robot developed by Hanson Robotics.

 

The project mentor 

If accepted to this project you will join a diverse team of 50 engineers to address this challenge. David Hanson, founder, and Chief Executive Officer will act as a mentor to the team.

 

Why join? The uniqueness of Omdena AI Challenges

A collaborative experience you never had in your working life! For the next eight weeks, you will not only build AI solutions to make a real-world impact but also go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join a global and collaborative team of changemakers. Omdena AI Challenges are not a competition or hackathon but a real-world project that will take your experience of what is possible through collaboration to a new level.

 

Find more information on how an Omdena project works

 

Improving the Quality of Life for Seniors in Nursing Homes with IoT Monitoring System

Improving the Quality of Life for Seniors in Nursing Homes with IoT Monitoring System

Build a solution that helps detect urinary incontinence in nursing homes by leveraging machine learning and digital sensor data. In this 8-week challenge, you will join a collaborative team of 50 AI engineers from all around the world.

This challenge requires experience in Data Analysis and Machine Learning.

 

The problem

Without monitoring technology in nursing homes, the staff cannot determine the status of the seniors and whether they have been lying down with a wet diaper for hours. Consequently, this issue impacts their quality of life while placing them at significant risk for skin breakdown and life-threatening urinary tract infections. Impact-driven startup Driq Health´s non-contact sensor monitoring can solve this by allowing real-time notifications for timely diaper changes.

The project goals

The goal is to develop a more accurate wet vs. dry state determination by examining digital sensor data from passive RFID moisture sensing tags logged via IoT onto the cloud. In addition to moisture state code, the AI solution would have access to diaper temperature data as well as the signal strength of the sensor. Both signal strength and temperature of the diaper will change during an incontinence event making them a good target for the AI-enabled algorithm.

 

The Data

The partner for this project will provide the data.

 

Why join? The uniqueness of Omdena AI Challenges

A collaborative experience you never had in your working life! For the next eight weeks, you will not only build AI solutions to make a real-world impact but also go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join a global and collaborative team of changemakers. Omdena AI Challenges are not a competition or hackathon but a real-world project that will take your experience of what is possible through collaboration to a new level.

 

Find more information on how an Omdena project works

 

Credit Scoring for Making Food Affordable to the Millions of Underserved in Africa

Credit Scoring for Making Food Affordable to the Millions of Underserved in Africa

Develop a credit score algorithm that allows for the financial inclusion of unbanked Nigerians. In this 8-week challenge, you will join a collaborative team of 50 AI engineers from all around the world.

This challenge requires experience in Data Analysis and Machine Learning.

 

The problem

Nigerians need more food than is currently produced and imported into the country, unable to meet the food needs. Although agricultural productivity has increased, it has been outpaced by population growth, insecurity, and climate change. As food is expensive, scarce, and inaccessible, Nigeria has a food security problem.

The biggest challenge to the food security issue is population. The UN suggests there will be 440 million Nigerians by 2050, meaning over 200 million more mouths to feed. If food production does not increase at a similar pace, there will be more starving Nigerians. And right now, food scarcity is amplified by poverty. The average Nigerian household spends almost 75% of its income on food.

The project goals

A credit score will be delivered based on data. This credit score will continuously be updated as customers interact with the platform. A historical depiction of the scores will also be available with advice on how to improve the score.

 

The Data

Our partner will provide the data.

 

Why join? The uniqueness of Omdena AI Challenges

A collaborative experience you never had in your working life! For the next eight weeks, you will not only build AI solutions to make a real-world impact but also go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join a global and collaborative team of changemakers. Omdena AI Challenges are not a competition or hackathon but a real-world project that will take your experience of what is possible through collaboration to a new level.

 

Find more information on how an Omdena project works

 

Reducing the Risk of Misdiagnosis of Chest X-rays with Deep Learning

Reducing the Risk of Misdiagnosis of Chest X-rays with Deep Learning

Join a global team of 50 AI changemakers in this high-impact 2-month challenge to build AI solutions for reducing the risk of misdiagnosis in X-rays.

This challenge requires experience in Deep Learning and Machine Learning.

 

The Impact

It is not unusual to miss radiological abnormalities. This is common because of areas on the chest X-ray where lesions can easily be overlooked: behind the clavicle, heart, diaphragm, at the hilum, and pleural lesions. Such mistakes lead to a diagnostic delay and significant delay in time to initiation of treatment and palliation of symptoms. Dr CADx’s AI solution helps doctors minimize the incidence of missed cases by picking up subtle findings at an early stage.

The deep learning algorithms of Dr CADx analyze medical images for features that suggest the presence of diseases. The insights brought by the analysis help the doctors reduce the risk of missed diagnosis by up to 20%. 

Considering the initial target market in Africa, where each year, 45 million patients lack access to have a radiologist report on their medical image, Dr CADx’s solution has the potential to reduce the risk of misdiagnosis for at least 1.3 million patients annually (taking into account that at least 50% are chest X-rays and 30% are currently digital a proportion that is continuously growing).

 

The Problem

Radiology is central to modern medicine, with the diagnosis of diseases frequently dependent on medical imaging (X-rays, CT scans, ultrasound, MRI), which needs to be interpreted by an imaging specialist, the radiologist. However, studies show that radiologists have limited accuracy, with up to 30% error rates. Additionally, due to the shortage of radiologists worldwide, it can take up to 30 days for about 10% of cases to be reported by radiologists in better-resourced regions like the EU. Furthermore, only 60% of medical images are read by radiologists.

The situation is even direr in regions like Africa, where radiologists read just about 10% of medical images. As a result, an estimated 45 million African patients get a medical image taken but do not have access to a radiologist to report on them. Given that the miss rates are higher among non-radiologists, reaching up to 45%, patient outcomes are non-ideal.

As a result of the 4.6 billion medical images taken worldwide annually, at least 1.4 billion are misdiagnosed. Patient outcomes can be severe, resulting in paralysis or death, with statistics showing that misdiagnoses result in 1.5 million deaths globally. Financially, errors in interpreting medical scans cost the global healthcare sector over $115 billion every year.

 

The Project goals

DR CADx has a model for 14 chest findings that have been already developed but not yet validated in a clinical study. Based on the initial market feedback from prospects, the following steps for the product development are thus as follows: Finetune the algorithm for the 14 chest findings to include:

1. Adding COVID, TB screening, and rib fractures, thus expanding it to 17 findings

2. Include more training data with a focus on data from Africa and different imaging equipment to further reduce bias and improve accuracy across various population groups

3. Improve the localization of detected findings

4. (Optional, if there is enough time) include analysis of lateral X-rays in addition to PA and AP images

5. (Optional, if there is enough time) a quality checking model to reject images that do not have chest X-rays

 

Why join? The uniqueness of Omdena AI Challenges

A collaborative experience you never had in your working life! For the next eight weeks, you will not only build AI solutions to make a real-world impact but also go through an entire data science project lifecycle. This covers problem scoping, data collection, preparation, and modeling for deployment.

And the best part is that you will join a global and collaborative team of changemakers. Omdena AI Challenges are not a competition or hackathon but a real-world project that will take your experience of what is possible through collaboration to a new level.

 

Find more information on how an Omdena project works