Data processing and Analytics :
Data processing and analytics: As a part of Academic project in Data processing and Analytics Task was to design and implement databases for Real-World scenario and perform data analytics on given datasets.
- Developed and ER(Entity-Relation) model and Translate into an equivalent set of relations including the identification of Primary and foreign keys.
- Implemented the above entities in SQL, MongoDB and Neo4J by identifying the type of data structure (SQL - Structured Data, MongoDB - Unstructured data and Documents, Neo4J - Semi-structured, Graph datatype)
- Designed 5 real-world use cases for each Database Technology and implement those using queries.
- For part of analysis one dataset(out of 3) was used to perform tasks such as defining training and test set, application of two classification algorithms and comparing their performance, Apply Principal Component analysis(PCA) and how it affects the percentage of variance covered, Application of feature selection algorithms, Discussing the challenges and implication regarding the time required to build.
Learning Outcomes :
1.
Perform
and critically analyse data modelling.
2.
Understand
the underlying technology of various database systems.
3.
Gained critical understanding of Data analytics’ challenges.
4. Gained critical understanding of
the most significant pattern recognition algorithms for dealing with Data and
Big Data.
5. Be
able to interpret the results from Data and Big Data analytics’ algorithms and
use the appropriate methods for reporting the results.