Statistical tool in data analysis detailed syllabus for Food Technology (Food Tech) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the Food Tech students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.
For Food Technology 6th Sem scheme and its subjects, do visit Food Tech 6th Sem 2021 regulation scheme. For Professional Elective-V scheme and its subjects refer to Food Tech Professional Elective-V syllabus scheme. The detailed syllabus of statistical tool in data analysis is as follows.
Course Objectives:
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Unit I
INTRODUCTION TO SCIENCE OF STATISTICS 9
Fundamental Elements of Statistics, Qualitative and Quantitative Data Summaries, Statistical Inference, Stating Hypotheses, Test Statistics and p-Values, Evaluating Hypotheses, Equation of multiple linear regression, Interpretation of multiple linear regression, Cautions about Regression.
Unit II
DATA MANAGEMENT AND ANALYSIS 9
Quantitative analysis, descriptive statistics, inferential statistics: Uses and limitations Summation sign and its properties, Proportions, percentages, ratios, Measures of central tendency-mean, median, mode arithmetic mean and its uses, mid – range, geometric mean, weighted mean, measures of dispersion /variability- range, variance, standard deviation, standard error, coefficient of variation, Kurtosis, Sleekness (practical aspects of grouped data-frequency distribution, histogram, frequency polygons, percentiles, Data Management and Analysis, Frequency distributions, Measures of central tendency, measures of dispersion, variability).
Unit III
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Unit IV
DATA ANALYTICAL MODELS 9
Reducing Data Complexity (Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA)), Additional Linear Model Topics (Collinearity, Logistic, Hierarchical Linear Models (HLM)), Confirmatory Factor Analysis and Structural Equation Modeling (SEM).
Unit V
DATA ANALYTICS: SOFTWARES AND TOOLS 9
SQL, Tableau, QlikView, R language, Python, RapidMiner, OpenRefine, SAS.
Course Outcomes:
At the end of the course the students will be able to
- Collect, store, process and analyze data according to high standards
- conduct empirical research in food science and technology using modern analytic software tools
- develop and apply new research methods
- solve problems using best practices of data analysis using modern computational tools.
Text Books:
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For detailed syllabus of all the other subjects of Food Technology 6th Sem, visit Food Tech 6th Sem subject syllabuses for 2021 regulation.
For all Food Technology results, visit Anna University Food Tech all semester results direct link.