Barclays India Women’s Hackathon 2019

957 Registered Allowed team size: 1 - 3

This campaign is over.

idea phase
starts on:
Aug 31, 2019, 07:30 AM
ends on:
Oct 06, 2019, 01:25 PM
starts on:
Oct 24, 2019, 10:30 PM
ends on:
Nov 17, 2019, 12:25 PM


Hacking is building things that you always wanted to have but no one has built it yet. It’s to come up with an amazing idea and work tirelessly on it. It is to fail, fail again and fail better. Trying out new things and learn while doing it. Driving innovation, adding value and building real-time collaboration. In the end, it’s to be the best solution provider.

With that spirit, Barclays India is conducting a hackathon – "Barclays India Women's Hackathon'19" – and invites all women developers and women hackathon enthusiasts to engage their minds in this year’s hackathon challenge. If a code problem is a gateway to hundreds of possibilities and you have the passion to eat, sleep, and fight the problem then send in your nomination to us. A challenge awaits you.


  • Women Developers across PAN India having 3+ years of experience.

Terms & Conditions:

  • Barclays Women Employees with 3+ years of industrial experience can also participate in the hackathon.

Note:-Travel reimbursements and stay will be provided by Barclays for the teams who will be invited for the offline hackathon in Pune.*


How do you need to participate?

  • Click on Register and enter your personal details
  • Once you register, click on "Submit Idea" button on the dashboard
  • Click on the "build" or "Join" a team
  • Once you have formed the team, click on "+Add new idea"
  • Write the Title of your idea and Select theme for which you want to submit the idea
  • You can submit multiple ideas from different or the same themes
  • Follow this doc and submit your answers in the Description box
  • You can submit a PPT/PDF describing your idea and the approach in the upload File attachment (optional)
  • Save as Draft if you want to make changes to the idea at later stages
  • Publish idea once your idea is ready to be submitted
Sentiment Analysis

The problem statement it to find the negative sentiment about Barclays on Social Media Platforms such as Facebook, Twitter, etc.

Business Outcome:

  • Identifying and targeting the dissatisfied customers to ensure the non-proliferation of the negative sentiment and avoid customer attrition.
  • Identifying the dissatisfaction related to a product or a specific feature

Technologies required:

  • Python/PySpark
  • Knowledge about NLP and ML

Which Account should I use to consume social media feeds? Use your own Developer account to create webhooks for twitter, facebook etc to listen to the feeds.

Which library to use? Use any 3rd party Library or Cloud services for sentiment analysis and classification.

Human Capital Attrition

Technology to be used – Machine Learning / Data Science - Python/Java/SCALA

The problem statement is to build a predictive model for attrition which will help the organization in better resource planning and improve the employee experience.

The data elements that can be considered for building the predictive model can include the following:

  • Performance ratings – Outstanding / Strong / Needs improvement
  • Educational background
  • Time in title
  • Company tenure
  • Industry experience
  • Role
  • Area of expertise/skills
  • Career progression
  • Compensation placement – alphanumeric
  • Market demand (will need to be sourced from external agencies)
  • Talent pool (will need to be sourced from external sources)

Server Reboot

The problem statement is to build a feature/code/tool/portal, where UNIX servers can be registered for reboot and it should record following points:-

  • Basic initial checks (cpu, memory, file system size, network links, network routes, vas status, some basic/ecosystem processes)
  • Reboot Status
  • Post reboot checks in similar format
  • Should include kdump/console logs
  • Read the schedule time and register the server

This can be achieved on any cloud platform, as this feature is not available on AWS natively. The report should compare pre and post boot status and report in below format.

  • Web Screen should have options to upload 2 server list files with similar data attributes as above. (PS: above columns are sample, real columns will be similar to above)
  • There should be an Option to call the Web service to check the status of the server or servers.
  • Preferred Technologies usage: Python for operation logic and Angular JS for Web application Working code output required.
  • Solution is usable by business: Yes

Additional Requirement Tips:

  • How do we display the stats of an individual server? Display the stats in tabular and graphical format on click of individual server in the above table.
  • What commands do I use to get the metrics stats? Use Unix Commands or you can consider using Cloudwatch commands if using AWS cloud
  • Where do I store the server list and its stats collected? Store the data in AWS Cloud s3/NoSQL/RDS
  • What are data archival requirements? Data Older than 6 months should be auto-archived and should be made available within 24 hrs if requested.
  • Can I set up and configure the environment manually? You can start the solution development manually, however, It is good to have automation including CI/CD and Infrastructure as a Code through Terraform/Chef or Cloudformation.
  • Who will bear the cost of the services used in Cloud? Only services available in Free-tier to be used and resources needs be terminated as and when you are done or by end of the day. DO NOT leave your resources running. Barclays will not reimburse any cost incurred and recommend you to use only free-tier services.
  • Architecture of the solution design: Your web application should be highly available, scalable, cost effective, High performance and Fault tolerant. Use Cloud server-less managed services wherever applicable

Reconciliation system
  1. Restful Web API to be written for Probability-based algorithm to be applied for potential data-attribute value matches as a match criterion. e.g. where these sample Banking trade data attributes (columns) :
    • trade_value_date,
    • legal_entity
    • Trade Volume
    • Currency_name
    • transaction_value Should match potentially across 2 different datasets.
  2. Above fuzzy matching logic linking “text” to “similar text” should also be applied to combine datasets with uncooperative columns (i.e. columns that are not obvious but have same underlying meaning).

  3. Web Screen should have options to upload 2 data files with similar data attributes as above. (PS: above columns are a sample, real columns will be similar to above)
  4. There should be an option to call the Web service to prepare the matching criteria that will then suggest matches on screen.
  5. Prepare a self-learning Dictionary where User can be allowed to add examples like below to Dictionary:
    • Manual Growth of Dictionary: “International Business machines” should be used in future as “IBM” also after User accepts first time.
    • Automatic growth of Dictionary: as per sample outputs above.
    • Dictionary should maintain growth in database for persistence, not in memory.
  6. Technologies to be used: Python or Java for machine learning and Angular JS for Web application Working code output required. Solution is usable by business: Yes


Main Prizes
Apple iPad (3)

Each team member of the winning team receives an Apple iPad.

Amazon Echo (3)

Each team member of the runner-up team receives an Amazon Echo.

Fitbit (3)

Each team member of the second-runner up team receives a Fitbit.

Special Prizes
Conditional Job opportunities (10)

Social Share

View All Notifications