Anatomy of a data science project
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Slides for the presentation given at the Data Science Scotland Meetup (https://www.meetup.com/Scotland-Data-Science-Technology-Meetup/events/256269263/). This talk aimed to give some general advice, tips, and tricks about how to run a successful data science project. Hosted by: Incremental Group - https://www.linkedin.com/company/incremental-group/ MBN Solutions - https://www.linkedin.com/company/mbn-recruitment-solutions/ The Datalab - https://www.linkedin.com/company/the-data-lab-innovation-centre/
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OpenSistemas Corporate Presentation
Data technology can help companies predict outcomes through simulations, find unexpected relationships in large data sets, and monitor situations in real-time. OpenSistemas is a company that specializes in data management, analysis, storage and visualization using technologies like Apache Spark, machine learning, and cloud integration. They provide services in areas like data processing, analytics, visualization and cloud to help clients strengthen the strategic value of their information.
Digital Transformation: A Case for Modern Workplace
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Kai-Fu Lee predicted that AI would change the world more than anything in the history of humanity – even electricity. It would disrupt how we live and work, how we operate our businesses, the core products and services on offer and the way in which we build technology.
However, in 2024 the impact of AI can no longer be discussed in future tense. With Microsoft copilot now publicly available, the change is already upon us. There is no consultation period or ‘unsubscribe’ button.
Project management professionals are likely to be asked to manage AI projects - and we are expected to skilfully use AI in our daily work lives. While overwhelming, this is not the first time we’ve had to adapt.
Sarah helps her audience sharpen their cutting-edge skills by answering:
What do I need to know about AI right now?
If I’m asked to work on an AI project, what techniques do I need to be successful?
Where do I start my own learning journey to upskill and prepare?
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Capgemini Ron Tolido - the 3rd Platform and Insurance
1) The document discusses digital transformation in the insurance industry and outlines several frameworks for how insurance companies can progress in their digital capabilities and mastery.
2) It presents different "levels" of digital capability that insurance companies may fall into, from "beginners" to "digital masters", and suggests that most insurers currently rank as "conservatives".
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Where the Warehouse Ends: A New Age of Information Access
The document provides information about an upcoming webinar hosted by The Briefing Room. The webinar will feature David Besemer, CTO of Composite Software, who will discuss how Composite addresses the challenges of data integration and providing data for analytics. The webinar aims to explain how Composite's data virtualization platform can help analysts more easily access and work with data from various sources through self-service analytic sandboxes and data hubs. The webinar also hopes to demonstrate how Composite can help organizations gain business insights faster while reducing costs compared to traditional data integration and warehousing approaches.
How Data Virtualization Puts Enterprise Machine Learning Programs into Produc...
Watch full webinar here: https://bit.ly/3offv7G
Presented at AI Live APAC
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spend most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.
Watch this on-demand session to learn how companies can use data virtualization to:
- Create a logical architecture to make all enterprise data available for advanced analytics exercise
- Accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- Integrate popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc.
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ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
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Anatomy of a data science project
- 1. ‘ANATOMY OF A DATA SCIENCE PROJECT’ ADAM SROKA, SENIOR DATA SCIENTIST WELCOME TO THE DECEMBER SCOTLAND DATA SCIENCE & TECHNOLOGY MEETUP RICARDO ANTUNES, DATA SCIENTIST INCREMENTAL GROUP - with &
- 2. MBN ACADEMY ARE THE OFFICIAL PARTNERS OF THE DATA LAB MSC. PLACEMENT PROGRAMME 2018/19. IF YOU ARE PART OF THE SCOTTISH BUSINESS COMMUNITY AND INTERESTED IN BECOMING A POTENTIAL HOST ORGANISATION, PLEASE EMAIL ROB AT ACADEMY@MBNSOLUTIONS.COM
- 3. Anatomy of a Data Science Project Adam Sroka, Senior Data Scientist adam.sroka@incrementalgroup.co.uk @adzsroka
- 4. Why we exist Digital technology is changing everything Sustainable success comes from incremental improvements Our mission is to enable government and industry to digitally transform, one step at a time
- 5. THE DATA SCIENCE HIERARCHY OF NEEDS
- 6. THE DATA SCIENCE HIERARCHY OF NEEDS
- 8. What makes a good project? A few points to consider before you start
- 13. Ask yourself… 1. Will this be easy to deploy & use? 2. Will it be considerably better than existing solutions? 3. Can parts be automated, reproduced, and reused? 4. Is it easy to understand, explain, and test? 5. Is it technically interesting?
- 14. Data Making your raw materials go further
- 16. This is always the first step Build a reusable set of tools for measuring quality DataExplorer is great start Quality
- 17. Build pipelines
- 18. Models Mastering the tools of the trade
- 19. “Never use a long word where a short one will do.” George Orwell
- 20. It’s easy to get excited about the new big thing Sometimes seems like expressing intelligence has taken priority over delivering value Marginal gains at the cost of understanding and interoperability aren’t gains at all Complexity
- 21. What are other people doing? Are there similar problems to yours on Kaggle? Do you have any biases? Take a quick first pass with everything and review What works?
- 22. Tools A few things to make your life easier
- 23. Templates Figure out a template that works for you – then stick to it It makes moving between and sharing projects tolerable https://drivendata.github.io/co okiecutter-data-science/
- 24. For longer lasting projects, strongly consider build automation This will manage rebuilding what’s needed when you make a change Tools like Azure Pipelines, AWS CodeBuild, Luigi, or even Makefiles Build Tools
- 25. Containers Package your entire workspace into easily manageable containers Makes reproducibility and sharing simple Many cloud platforms allow automatic distribution of containers to clusters
- 27. Thanks! Adam Sroka, Senior Data Scientist adam.sroka@incrementalgroup.co.uk @adzsroka
Editor's Notes
- Ease of adoption – awful interfaces
- Is this an improvement https://medium.com/@EvanSinar/7-data-visualization-types-you-should-be-using-more-and-how-to-start-4015b5d4adf2
- https://aeon.co/essays/is-technology-making-the-world-indecipherable
- Reusability https://www.freeimageslive.co.uk/free_stock_image/building-concept-jpg
- https://gengo.ai/datasets/the-50-best-free-datasets-for-machine-learning/ https://digital.nhs.uk/ https://registry.opendata.aws/ https://data.europa.eu/euodp/en/data/ https://data.gov.uk/ https://www.data.gov/
- https://cran.r-project.org/web/packages/DataExplorer/vignettes/dataexplorer-intro.html
- https://icons8.com/icon/2297/gis