Research AnalyticsSillabus

Fənn kodu

MM-0003

Kredit

6

Tələbənin iş yükü

150

Semestr

Payız semestri

Akademik il

2

Fənnin tipi

Məcburi

Təhsil forması

Əyani

Tədris dili

Azərbaycan

Auditoriyadaxili saatlar

30 hours – Lectures

Auditoriyadan kənar saatlar

80 hours – Independent study 45 hours – Preparation

This course introduces the fundamental concepts, principles, and methodologies of research analytics, focusing on data collection, processing, statistical analysis, interpretation, and evidence-based decision-making. Students explore research design, systematic literature reviews, sampling techniques, variable measurement, and quantitative and qualitative research methods. The course develops practical skills in data management and analysis using Python and Microsoft Excel, including data cleaning, descriptive statistics, data visualization, probability distributions, statistical inference, hypothesis testing, correlation analysis, A/B testing, regression analysis, and predictive modeling. Students also examine text analytics, model validation, research data ethics, informed consent, privacy protection, and academic integrity. Through case studies and project-based learning, students learn to select appropriate analytical methods, interpret research findings, prepare evidence-based analytical reports, and communicate results effectively in academic and professional contexts.

• mühazirə
• interaktiv müzakirə
• case study
• təqdimatlar
• komanda işi

1. 1

   CLO 1: Explain the fundamental concepts, principles, and stages of research analytics.

2. 2

   CLO 2: Apply appropriate data collection, sampling, and analytical methods to research problems

3. 3

   CLO 3: Analyze collected data using statistical and analytical techniques and identify relationships between variables.

4. 4

   CLO 4: Interpret analytical results, create appropriate visualizations, and prepare well-founded analytical reports.

5. 5

   CLO 5: Develop and present an analytical project individually or as part of a team to address a specific research problem.

1. 1.
   Conceptual and Methodological Foundations of Research Analytics Mühazirə

   This topic covers the nature, objectives, fundamental principles, and methodological approaches of research analytics. Students explore the stages of data collection, processing, analysis, and evidence-based interpretation in scientific research. The characteristics of quantitative and qualitative research methods, the selection of appropriate analytical approaches, and the principles of data-driven decision-making are examined. Students also develop the knowledge and skills required to select analytical methods aligned with research questions and evaluate the scientific validity of research findings.

2. 2.
   Research Problems and Systematic Literature ReviewMühazirə

   This topic addresses the identification of research problems, formulation of research questions, and systematic examination of existing scientific knowledge. Students learn how to search for scholarly sources, select relevant literature, assess its reliability, and conduct comparative analysis. The preparation of systematic literature reviews, development of search strategies, establishment of inclusion and exclusion criteria, and identification of research gaps are also covered. Students develop the ability to justify research problems and establish the relevance of a study based on scientific literature.

3. 3.
   Research Design and VariablesMühazirə

   This topic focuses on selecting an appropriate research design based on research objectives and questions, identifying variables, and determining how they should be measured. Experimental, observational, correlational, and other research designs are examined. Students study the identification of independent, dependent, control, and confounding variables; operational definitions; measurement scales; and hypothesis formulation. The topic also addresses internal and external validity, research reproducibility, and the importance of accounting for methodological limitations.

4. 4.
   Sampling Methods and Data Collection TechniquesMühazirə

   This topic covers the identification of the target population, the design of samples, and the methods used to collect research data. Probability sampling methods, including random, stratified, cluster, and systematic sampling, are examined alongside non-probability approaches such as purposive and convenience sampling. Students explore questionnaires, interviews, observations, experiments, and the use of existing databases. Sample size determination, sampling errors, bias, and factors affecting data quality are also analyzed.

5. 5.
   Analytical Environment: Data Management Using Python and ExcelMühazirə

   This topic aims to develop students’ skills in using Python and Microsoft Excel to manage, process, and analyze research data. In Excel, students learn to create tables, sort and filter data, use functions and conditional formatting, and work with pivot tables. In Python, the topic introduces basic programming operations, data reading, processing, and grouping with the Pandas library, and numerical computations using NumPy. Students also explore importing and exporting data in different formats, automating analytical tasks, and building reproducible analytical workflows.

6. 6.
   Data Cleaning and Preparation for Analysis Mühazirə

   This topic covers methods for assessing data quality and preparing datasets for analysis. Students learn to identify and handle missing values, duplicate records, inconsistent formats, and incorrectly entered data. Methods for detecting outliers, normalizing data, standardizing categories, and integrating data from multiple sources are examined. Emphasis is placed on preserving original data, documenting modifications, and verifying that datasets are suitable for analysis.

7. 7.
   Descriptive Statistics and Exploratory Data AnalysisMühazirə

   This topic introduces the summarization and preliminary assessment of data using descriptive statistical measures. Students learn to calculate the mean, median, mode, variance, standard deviation, minimum, and maximum values. Data distributions, variability, skewness, outliers, and initial patterns are explored. The use of histograms, frequency tables, and box plots to understand data characteristics is examined, along with the selection of appropriate methods for subsequent statistical analysis.

8. 8.
   Data Visualization and Graphical PresentationMühazirə

   This topic focuses on presenting data clearly, understandably, and scientifically through graphical and visual methods. Students examine the applications of bar charts, line charts, scatter plots, area charts, box plots, and histograms. The creation of visualizations using Python and Excel, selection of appropriate chart types, and correct formatting of axes, titles, scales, and legends are covered. Students also learn to avoid misleading representations and ensure that visualizations are readable and appropriate for their intended audience.

9. 9.
   Probability Theory, Distributions, and Statistical InferenceMühazirə

   This topic covers probability theory, random variables, and probability distributions as the theoretical foundations of statistical analysis. Conditional probability, independent events, expected value, and variance are examined. The characteristics and applications of binomial, normal, Poisson, and other probability distributions are explored, including their role in data modeling. Students also study the estimation of population parameters from samples, construction of confidence intervals, hypothesis testing, interpretation of p-values, and the distinction between statistical and practical significance.

10. 10.
    Group Comparisons, Statistical Relationships, and A/B TestingMühazirə

    This topic examines methods for identifying differences between groups, evaluating statistical relationships between variables, and analyzing experimental results. Students learn to compare means, select appropriate parametric and non-parametric tests, calculate correlation coefficients, and interpret findings. The design of A/B tests, formation of control and experimental groups, selection of key metrics, sample size planning, and assessment of statistical differences are covered. Multiple comparisons, bias, confounding factors, and the importance of correct interpretation are also discussed.

11. 11.
    Regression Analysis: Explanatory and Predictive ModelsMühazirə

    This topic focuses on modeling relationships between variables and applying regression methods to explain outcomes and make predictions. Students study simple and multiple linear regression, estimation of model coefficients, selection of explanatory variables, and interpretation of results. The coefficient of determination, residual analysis, multicollinearity, heteroscedasticity, and other regression assumptions are examined. The differences between explanatory and predictive modeling, model limitations, and the scientific justification of analytical conclusions are also addressed.

12. 12.
    Predictive Models and Model ValidationMühazirə

    This topic covers the development of predictive models, assessment of their accuracy, and evaluation of their reliability on unseen data. Students learn to create training, validation, and test datasets; apply cross-validation; select hyperparameters; and prevent overfitting. Evaluation metrics for regression and classification models, including MAE, RMSE, accuracy, precision, recall, and F1-score, are examined. The topic also addresses data leakage prevention, generalization performance, and the comparison of alternative models

13. 13.
    Text Data Analytics Mühazirə

    This topic examines methods for collecting, processing, and analyzing textual data. Students learn text cleaning, tokenization, word normalization, frequency analysis, and the transformation of text into structured data. Natural language processing (NLP), keyword extraction, topic modeling, text classification, and sentiment analysis are introduced. Students develop practical skills in analyzing text corpora using Python libraries, visualizing results, and deriving research insights from textual information.

14. 14.
    Research Data Ethics, Consent, and Legal Compliance Mühazirə

    This topic covers the ethical principles and legal requirements governing the collection, storage, processing, and use of research data. Students examine informed consent, personal data protection, anonymization, confidentiality, and access control. The appropriate use of data, secure storage, sharing with third parties, and honest reporting of research findings are also discussed. Additional attention is given to plagiarism, data fabrication, manipulation of results, and the academically responsible use of artificial intelligence tools.

15. 15.
    Interpretation of Analytical Results and Academic PresentationMühazirə

    This topic focuses on interpreting analytical findings based on scientific evidence, relating them to research questions, and presenting them in an academic format. Students learn to interpret statistical measures, charts, tables, and model outputs; draw well-supported conclusions; and identify research limitations. The preparation of scientific reports, research papers, presentations, and analytical briefs is covered, along with citation practices and communicating findings to different audiences. Students develop the ability to present results clearly, coherently, and objectively, construct evidence-based arguments, and identify directions for future research.

1. Saunders, M. N. K., Lewis, P., & Thornhill, A. (2023). Research Methods for Business Students (9th ed.). Pearson.
2. McKinney, W. (2022). Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter (3rd ed.). O’Reilly Media.
3. Diez, D. M., Çetinkaya-Rundel, M., & Barr, C. D. (2019). OpenIntro Statistics (4th ed.). OpenIntro.
4. James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An Introduction to Statistical Learning: With Applications in Python. Springer.
5. Braun, V., & Clarke, V. (2021). Thematic Analysis: A Practical Guide. SAGE.
6. Winston, W. L. (2022). Microsoft Excel Data Analysis and Business Modeling (Office 2021 and Microsoft 365) (7th ed.). Microsoft Press.