Open Data Science for Smart Manufacturing

Open Data offers a tremendous opportunity in transformation of today’s manufacturing sector to smarter manufacturing. Smart Manufacturing initiatives include digitalising production processes and integrating IoT technologies for connecting machines to collect data for analysis and visualisation.

In this talk, an understanding of linkage between various industries within manufacturing sector through lens of Open Data Science will be illustrated. The data on manufacturing sector companies, company profiles, officers and financials will be scraped from UK Open Data API’s. The work I plan to showcase in ODSC is part of UK Made Smarter Project, where the work has been useful for major aerospace alliances to find out the champions and strugglers (SMEs) within manufacturing sector based on the open data gathered from multiple sources. The talk includes discussion on data extraction, data cleaning, data transformation - transforming raw financial information about companies to key metrics of interest - and further data analytics to create clusters of manufacturing companies into "Champions" and "Strugglers". The talk showcased examples of powerful R Shiny based dashboards of interest for suppliers, manufacturer and other key stakeholders in supply chain network.

Further analysis includes network analysis for industries, clustering and deploying the model as an API using Google Cloud Platform. The presenter will discuss about the necessity of 'Analytical Thinking' approach as an aid to handle complex big data projects and how to overcome challenges while working with real-life data science projects.

 
 

Outline/Structure of the Talk

Data Scraping from UK Open Data API's

Data Preprocessing and Integration

Transformation of key financials into meaningful metric to find Champions and Strugglers category of SMEs

'Analytical Thinking' for handling financial data

Network Analysis for Industries

Deployment on Google Cloud Platform

Learning Outcome

  • This talk will provide an overview on how Open Data can be leveraged for businesses using data science technologies.
  • The talk will emphasis on how to build scalable data science pipelines with ease, even if attendees have no prior experience in data science.
  • The attendees will understand the importance of all stages of data science projects like data extraction, data cleaning, data integration, feature engineering, data analysis and model deployment.

Target Audience

Data Science Enthusiasts, Data Engineers, Statisticians, Business Professionals interested in application of Data Science and Digital Manufacturing or Industries 4.0

Prerequisites for Attendees

  • Basic knowledge of Python
  • Basic knowledge of R
schedule Submitted 9 months ago

Public Feedback

comment Suggest improvements to the Speaker
  • Dr. Vikas Agrawal
    By Dr. Vikas Agrawal  ~  7 months ago
    reply Reply

    Dear Neha: I am wondering about two things:

    1. The specificity of manufacturing specific use cases here vs general applicability of using open data. How would your talk address manufacturing in particular?

    2. Has the work you plan to show been used to solve the problems in a specific manufacturing case. It appears that you are primarily showing a data integration use case with network inferencing. 

    Warm Regards,

    Vikas Agrawal

    • Dr. Neha Sehgal
      By Dr. Neha Sehgal  ~  7 months ago
      reply Reply

      Dear Dr. Vikas,

      Thanks for your email.

      The work I plan to showcase in ODSC is part of UK Made Smarter Project, where the work has been useful for major aerospace alliances to find out the champions and strugglers (SMEs) within manufacturing sector based on the open data gathered from multiple sources, integrated, analysed and visualised. It is not merely a data integration based case study, but it include data extraction, data cleaning, data transformation - transforming raw financial information about companies to key metrics of interest - and further data analytics and visualisations of interest for suppliers, manufacturer and other key stakeholders in supply chain network.

      Thanks,

      NS

      • Dr. Vikas Agrawal
        By Dr. Vikas Agrawal  ~  7 months ago
        reply Reply
        Dear Dr. Neha: I have the same questions as Nirav. We see that this dataset and the analysis applies to broader use cases beyond manufacturing and Industry 4.0 as it is financials data. Would you like to add a manufacturing and Industry 4.0 data set or use case to make the title of the talk consistent with the content? Or perhaps we could tilt the title of the talk closer to Open Data Integration and Analysis? Warm Regards, Vikas
        • Dr. Neha Sehgal
          By Dr. Neha Sehgal  ~  7 months ago
          reply Reply

          Dear Dr. Vikas,

          Thanks for your query. Well, I can understand that this work can be generalised to other sectors as it is financials and company profile data. In this regard, I can extend the talk to include how the information gathered from open data integrations and data analytics part helps specifically in finding manufacturing SMEs for digital productivity transformation study. Open Data gave information on profiles and help in providing key financials metrics but it doesn't give information on which company is an SME or not. I can include decision tree analysis for classifying Manufacturing SME or Non-SME later in the talk, and can discuss about how the SME's are engaged by UK Manufacturing Alliances for their digital productivity transformation.

          Hope this new information can shed light on specific manufacturing based use case study.

          Thank you,

          NS

  • Venkatraman J
    By Venkatraman J  ~  7 months ago
    reply Reply

    Hi Neha,

    Can you please attach a video of your previous conference speaking experience?.

  • Nirav Shah
    By Nirav Shah  ~  8 months ago
    reply Reply

    Hi Neha,

    Thank you for your submission. Smart Manufacturing and Industry 4.0 is very relevant, but can you please elaborate on how do you plan to show the correlation between open data in UK and then deploying it on GCP improves production processes? Most of manufacturing and production processes data is proprietary.

    • Dr. Neha Sehgal
      By Dr. Neha Sehgal  ~  8 months ago
      reply Reply

      Hi Nirav,

      Thank you for your comments. 

      Manufacturing sector is still at nascent stage, where SME's find it difficult to connect with suppliers or manufacturers not listed as per SIC codes. With explosion of Open Data available, this talk will discuss how manufacturing companies can benefit from UK open data related to UK companies. This talk will focus more on connecting industries (specifically manufacturing) through Open Data and data science capabilities.

      Well, I will not be using any production processes data from any manufacturing companies for the discussion. Smart manufacturing is not limited to smart factories or powder/printers data analytics but it also include integrating digital or big data platforms for better understanding about manufacturing sector. The task of gathering data from Open Data API, to clean it, to integrate it and analyse it require automation of task. For this purpose specifically, I intend to utilize Google Cloud Platform (GCP) for storing and transforming data or Airflow.

      Hope this clarifies what I intend to discuss during my talk at ODSC 2019.

      Thanks,

      NS

      • Nirav Shah
        By Nirav Shah  ~  8 months ago
        reply Reply

        Hi Neha

        Yes, it does , thank you for the clarification. Looking forward to it.

        Nirav

  • Anoop Kulkarni
    By Anoop Kulkarni  ~  8 months ago
    reply Reply

    Neha, thank you for your proposal. Sounds very interesting. When you mention web scraping UK databases, would this talk be more at a concept level or handson tutorial and/or demo level?

    Looking forward.. Can you suggest some time breakup for your talk?

    thanks

    ~anoop

    • Dr. Neha Sehgal
      By Dr. Neha Sehgal  ~  8 months ago
      reply Reply

      Hi Dr. Anoop,

      Thank you for your comments. I have planned this talk at a demo level. Time breakup will be as follows:

      1. Introduction & Data Scraping from UK Open Data Portal - 10 Mins

      2. Data Integration and Data preprocessing for Financials Data - Issues Handled (15 Mins)

      3. Further Data Analysis : Network Analysis or Others (5 Mins)

      4. Deployment on GCP or Airflow for Automation (10 Mins)

      5. Q&A (5 mins)

      Looking forward to meet you at ODSC 2019.

      Thanks,

      NS


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    There is quite a bit of journey that one needs to cover from building a model in Jupyter notebook to taking it to production.
    I would like to call it as the “last mile problem in ML” , this last mile could be a simple tread if we embrace some good ideas.

    This talk covers some of these opinionated ideas on how we can get around some of the pitfalls in deployment of ML models in production.

    We would go over the below questions in detail think about solutions for them.

    • How to fix the zombie models apocalypse, a state when nobody knows how the model was trained ?
    • In Science, experiments are found to be valid only if they are reproducible. Should this be the case in Datascience as well ?
    • Training the model in your local machine and waiting for an eternity to complete is no fun. What are some better ways of doing this ?
    • How do you package your machine learning code in a robust manner?
    • Does an ML project have the luxury of not following good Software Engineering principles?
  • Liked Amit  Baldwa
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    Amit Baldwa - PREDICTING AND BEATING THE STOCK MARKET WITH MACHINE LEARNING AND TECHNICAL ANALYSIS

    Amit  Baldwa
    Amit Baldwa
    Director
    Finastra Financial Software
    schedule 7 months ago
    Sold Out!
    45 Mins
    Demonstration
    Intermediate

    Machine learning provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

    Technical analysis shows in graphic form investor sentiment, both greed and fear. Technical analysis attempts to use past stock price and volume information to predict future price movements. Technical analysis of various indicators has been a time-tested strategy for seasoned traders and hedge funds, who have used these techniques to effective turn our profits in Securities Industry.

    Some researchers claim that stock prices conform to the theory of random walk, which is that the future path of the price of a stock is not more predictable than random numbers. However, Stock prices do not follow random walks.

    We will evaluate whether stock returns can be predicted based on historical information.

    Coupled with Machine Learning, we further try to decipher the correlation between the various indicators and identify the set of indicators which appropriately predict the value

  • Liked Deepthi Chand
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    Deepthi Chand / Shreya Agrawal - Samantar, an open assistive translation framework for Indic Languages

    45 Mins
    Case Study
    Beginner

    India is a land of many languages. There are 23 official and much more unofficial languages prevalently used in day-to-day conversations. Unfortunately, information dissemination to the low resource languages get difficult because of the geo-spatial distances. Popular translation platforms helped to fill this gap in major languages but their efficiency is challenged by the lack of availability of proper datasets and their generic nature. This problem is very evident when more domain information gets involved.

    We present Samantar, an open translation suggestion framework targeted at Indian languages. Samantar is built with open parallel corpora and opensource technologies. The translations can be tuned to suggest according to different target domains.

  • Liked Vidhya Veeraraghavan
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    Vidhya Veeraraghavan - Story Teller - Analytics in Banking & Financial Sector

    45 Mins
    Case Study
    Beginner

    As kids, we always enjoyed stories. Some scary, some holy, some imbibing moral values & some just for fun.

    Analytics is fun when you approach it with passion and curiosity. I know this because I have done this. With few case studies, I wish to illuminate your wits about Analytics and how it is being actively used in Banking and Financial Sector.

    Come join me for a fun ride.

  • Liked Shankar Somayajula
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    Shankar Somayajula - Revisiting Market Basket Analysis (MBA) with the help of SQL Pattern Matching