Students will understand the three major concepts of object-oriented programming: encapsulation, inheritance, and polymorphism, and will be able to interpret and develop programs using these concepts. This will enable them to take advantage of libraries and state-of-the-art frameworks with object-oriented programming languages.
Python is a popular programming language widely used to develop large-scale industrial applications. (i) fundamentals of Python (architecture, syntax, variable declaration, and compiling), (ii) Object Oriented Concepts in Python, (iii) python documentation, (iv) introduction to mini-world and Entity-Relation schema, (v) introduction to DBMS architecture and Structured Query Language (vi) Data frames, and (vii) data processing using data frames.
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Big data analytics (BDA) represents a complex process of analyzing voluminous data to uncover useful information -- such as hidden patterns, correlations, market trends and customer preferences -- that can help organizations make informed business decisions.
In Integrated Exercise for Software, 3 to 5 students work in teams to learn practical software development by developing a software that solves a given problem (project theme). Project themes will be decided at beginning of the course, development is performed incrementally in phases of 3 weeks each, and results are reported at an interim review and a final review session.
Briefly, this venture experience workshop covers the following topics: 1. Understanding the process employed by JARTIC to collect traffic congestion data 2. Identifying different entities and their relations in the congestion data 3. Developing Python libraries for ETL (Extract, Transformation, and Load) process. 4. Designing imputation techniques to fill the missing data 5. Studying the existing pattern mining techniques and exploring the new models to discover traffic congestion patterns.
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Big data is the term for a collection of the data sets that it becomes difficult to process using on-hand database management tools or traditional data processing applications. Data science is a novel term that is often used interchangeably with competitive intelligence or business analytics, and it seeks to use all available and relevant data to effectively tell a story that can be easily understood by non-practitioners
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This course aims to empower students with state-of-the-art knowledge discovery methods, tools, and techniques in both data science and cloud computing. The integration of these two fields is crucial for unlocking valuable insights from big data stored in globally-spanning cloud databases, with a goal of contributing to socio-economic development
Pattern mining is an important knowledge discovery technique in big data analytics. It involves identifying all regularities that exist in a database. Several algorithms were described in the literature to find user interest-based patterns that exist in a database.
This course is a combination of advanced lectures and exercises according to practical data analysis and tool-development in lunar and planetary explorations based on the antecedent course "Fundamental Data Analysis in Lunar and Planetary Explorations".
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Themes will be proposed from faculty members each Academic Year and factory will be held accordingly. Poster Session of Creative Factory Seminar by the registered students will be held in late September and the evaluation of each factory will be made.
