Python Expert/Master Scribe Needed

I am in need of an adept Python expert who possesses exceptional writing skills. Their responsibilities would include, although not exclusive to: - I am seeking an expert in Computational Modelling within the domain of Social Sciences. Using SPARQL specifically, the focus of this project will involve querying and manipulating semantic data. While the type of databases or data sources is not yet defined, proficiency in working with RDF formats, Relational and NoSQL databases would be valuable. Ideal candidate should possess the following skills: - Proficient knowledge in Sparql. - Exceptional problem-solving ability. - Strong background in Social Sciences. - Experience working with different data sources. Knowledge in Image Analysis and Natural Language Processing is also a huge plus. The House of Commons SPARQL endpoint ([login to view URL]) provides access to structured data about Members of Parliament (MPs) and questions that have been asked in the House of Commons. Use this data together with data from Wikidata (where necessary) to answer the following questions, presenting your findings in the form of a report of up to 3000 words, based on data about questions asked by MPs between 1 January 2023 and 30 September 2023 inclusive: [login to view URL] what extent do Members of Parliament (MPs) tend to ask questions that directly reference their own constituency or a location in it? You should answer this question by identifying named entities that refer to places or identifiable geographical features (e.g. “Dartford Crossing”, “Reading Gaol”, etc.) in asked questions, and determining whether or not these are located in the MP’s constituency using data from Wikidata. You are free to choose any reasonable method in doing so – even if doing so will result in some false negatives – (e.g. relying on Wikidata property P131 “located in the administrative territorial entity”), however you should estimate how reliable you believe your chosen approach is. (N.B. it is not expected or required that your approach will result in 100% accuracy.) [login to view URL] applying LDA topic modeling and analyzing the results, what (if any) identifiable regional differences are there in the types of questions asked – e.g. do MPs representing, say, constituencies located in the North of England tend to ask more questions about certain topics than those in Southeast England? In answering this question, you should start by aggregating data into regions larger than an electoral district, such as those denoted by the property “region of England” [login to view URL] (For simplicity, you may treat Scotland and Northern Ireland as two separate regions without further subdivisions, or alternatively use any reasonable administrative subdivisions for these regions as you see fit). Discuss the assumptions and limitations of your approach and analysis. The following SPARQL query can be used as a starting point: SELECT * WHERE { ?question <[login to view URL]> ?qnum . ?person <[login to view URL]> ?question . ?question <[login to view URL]> ?text . ?question <[login to view URL]> ?date . FILTER (?date >= "2023-01-01+00:00"^^xsd:dateTime && ?date < "2023-10-01+00:00"^^xsd:dateTime) } This query returns the following data: ?question Entity representing a question ?person Entity representing the person who asked the question ?qnum Numerical identifier for this question ?text The text of the question More details will be provided on request!