Data Analysis — DU B.A. (V.S.) Insurance Management Previous Year Question Papers

B.A. (VS) Insurance Management DISCIPLINE SPECIFIC ELECTIVE COURSE (DSE- 6.4) Data Analysis CREDIT DISTRIBUTION, ELIGIBILITY AND PRE-REQUISITES OF THE COURSE Lectures Tutorial Practical Eligibility criteria Pre-requisite of the course 4 3 1 - Pass in class Learning Objectives: Data analysis is a process of application of statistical tools on huge data sets by using software like R or Excel functions and formulas. It helps in reducing voluminous datasets into tables, charts, graphs and even in a set of few summary number which are comprehensible and enables analysis, interpretation and forecasting. Data Analysis is essential as it helps policymakers to design effective policies and businesses to understand their customers better, improves sales, reduces costs, and develop better problem-solving strategies. Learning Outcomes: After completion of the course, learners will be able to: Mismatch of LO, PE and course content Recognize trends in data and detect outliers; Calculate measures of central tendency, measures of dispersion and coefficient of correlation in R and Excel Organize data into summary tables, multi-dimensional tables, frequency tables etc. and visualize data in form of charts, bar graphs, histograms, scatter plots etc.; Develop and test hypothesis to make policy decisions; Run simple and multiple linear regression to identify relationship between two or more variables to improve understanding of data and provide forecast. Unit 1: Understanding different data sets; meaning of big data; structuring, summarizing and visualizing data. 10 hours Unit 2: Descriptive statistics- measures of central tendency, measures of dispersion, coefficient of correlation. 10 hours Unit 3: Hypothesis testing and statistical inference- develop and test hypothesis to analyze a single population or to analyze difference between two populations. 10 hours Unit 4: Single variable linear regression model; multiple variable linear regression model and forecasting. 1. Choose a data set or generate one with sample data and create visual representations using tools like Microsoft Excel, SPSS, Tableau, libraries. 2. Data Analysis and Summary: Collect a dataset or use a provided dataset with numerical values. Calculate measures of central tendency, such as mean, median, and mode, to determine the typical or central value of the dataset. Calculate measures of dispersion, such as range, variance, and standard deviation, to assess the spread or variability of the dataset. 3. Gather two sets of data that are expected to have a relationship or connection (e.g., height and weight, test scores and study time). Calculate the coefficient of correlation, such as Pearson's correlation coefficient, to quantify the strength and direction of the relationship between the variables. 4. Hypothesis Testing with Sample Data: Formulate a research question or hypothesis related to a specific topic of interest. Collect a sample dataset that is relevant to the research question.