Code to Change data challenge (Hackathon weeks)

The Code to Change is aimed at reducing skills gap and associated gender divide. They inspire and teach women essential skills to succeed in the job market. Their core objective is to contribute towards the achievement of gender equality (UN Sustainable Development Goal 5) and economic empowerment of women via digital inclusion.

Challenge :

Identify the skills that are needed by women in various regions [ limit scope to countries/ cities] and build an interactive tool that can give insights about various skills based on selected country.

What it does

The tool can be used to assess the digital skill levels of women per country as well as the users' digital skill levels. The user answers a fixed number of questions, taken from Eurostat's "Individuals' level of digital skills" questionnaire. The tool relies on a calculation to return the digital skills level based on the answers and this calculation is also taken from Eurostat's questionnaire.

The scope of the tool includes women between ages 16 through 74 and based in the countries in which Code to Change operates. It also provides a generic recommendation of the courses the user could take based on their digital skill level.

In the future, the tool could be expanded to recommend specific Code to Change courses based on the users' digital skill level. It could also be expanded to allow Code to Change to evaluate the demand for certain courses.

How we built it

We have used the Eurostat "Individuals' level of digital skills" dataset (

This dataset provides a summary of each respondent's level of digital skills and is organized into different groups (females, males, etc.).

Digital skills indicators are composite indicators which are based on selected activities related to internet or software use performed by individuals aged 16-74 in four specific areas (information, communication, problem solving, software skills). It is assumed that individuals having performed certain activities have the corresponding skills. Therefore the indicators can be considered as proxy of the digital competences and skills of individuals.

We have filtered the dataset so that we focus on females (grouped by age ranges: 16 - 19, 20 - 24, 25 - 54, 55 - 74) in the countries (Bulgaria, Czech Republic, Germany, Greece, Italy, The Netherlands). Although we have available data between 2015 and 2019, we chose the data from 2019 so that we captured the most relevant information given that digital skills development occurs quickly. To calculate a respondent's digital skill level, Eurostat applied a formula based on the respondent's answers (see below). We built the streamlit app using these same questions and formula.

See this link for more detailed information on the Eurostat calculations:

Challenges we ran into

The biggest challenge we ran into was finding an open source dataset that would allow us to address the challenge. Because the dataset we did find didn't allow us to do everything we had initially sought to develop, we had to accept making adjustments to our initial planning.

Accomplishments that we're proud of

We are very proud of the cooperation between the team, we worked quite well together. We're also proud that we have developed a tool that can be used by an organisation to help reduce the gap between women and men in the workforce.

What we learned

We learned about patience, trial and error and being persistent. We also got better at using Python.

What's next for Code2Change


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