B.Sc. (Data Science) Research Methodology Syllabus - Mumbai University
This is the Fourth Year BSc Data Science Honours syllabus under NEP 2020, phased in one year at a time, from the academic year 2024-25. The University still sets the earlier Choice Based papers alongside it for ATKT candidates, so check which scheme your exam form names before you revise.
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Syllabus for Research Methodology
Module I: Introduction to Research Methodology for Data Sciences
- Understanding Research in Data Sciences: Definition and significance of research in Data Sciences, Types of research: Basic research, applied research, and practical research, Research approaches: Quantitative vs. Qualitative, The role of research in Data Sciences. Research Process and Ethics: Steps in the research process in Data Sciences, Research ethics and responsible conduct, Formulating research questions and objectives, Identifying research problems in Data Sciences, Ethical considerations in research, including data privacy and security. Research Design and Sampling for Data Sciences: Principles of research design in Data Sciences, Types of research design, Sampling techniques in Data Sciences, Choosing the appropriate research design for data-driven research, Ethical considerations in sampling and data collection, including informed consent. Data Collection and Management for Data Sciences Data Sources and Collection Methods: Data sources: Primary vs. secondary data, Data collection methods: Surveys, questionnaires, web scraping, and APIs, Data quality and data cleaning, Ethical considerations in data collection, including data privacy. Data preprocessing techniques: Cleaning, transformation, and normalization, Data integration and data fusion, Handling missing data and outliers Exploratory Data Analysis (EDA): Techniques for exploring and visualizing data, Descriptive statistics and data visualization, Identifying patterns and trends, Data Analysis and Modeling for Data Sciences Statistical Analysis for Data Sciences: Inferential statistics for hypothesis testing, Regression analysis and correlation, Machine learning concepts for predictive modeling. Machine Learning and Deep Learning: Introduction to machine learning and deep learning algorithms, Model selection and evaluation, Big Data Analytics: Understanding big data and its challenges, Tools and frameworks for big data processing (e.g., Hadoop, Spark), Analyzing and extracting insights from large datasets. Research Communication, Publishing, and Data Privacy Research Paper Writing and Presentation: Structure of a research paper, Writing the abstract, introduction, literature review, methodology, and results sections, Effective research paper presentations. Publishing Research in Data Sciences: Choosing the right conferences and journals, The peer-review process in Data Sciences, Preparing manuscripts for submission, Ethical considerations in publishing, including data privacy and security in publications. Ethical Considerations in Data Privacy and Security: Data privacy regulations and compliance, Ethical considerations in data anonymization and de-identification, Securing research data and protecting sensitive information. 10 Text Books 1. Research Methodology: Methods and Techniques, C.R. Kothari, New Age International 11 Reference Books 1. 12 Internal Continuous Assessment: 50% Semester End Examination: 50% Sem – VIII Sem – VIII
Text Books
- 1 Research Methodology: Methods and Techniques, C.R. Kothari, New Age International 1.
Reproduced from the University of Mumbai syllabus for B.Sc. (Data Science) under NEP 2020. Wording is as printed in that syllabus. Module numbering is as printed there too.
The complete syllabus
This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.
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NEP 2020 syllabus, in force from 2024-25
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Earlier Choice Based syllabus, still set for ATKT candidates
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