Data Analysis Lab

Paper Code: 
24GBCA202B
Credits: 
03
Periods/week: 
06
Max. Marks: 
100.00
Objective: 

The course will enable the students to

  1. Understand the role of statistics in data analysis.
  2. Apply statistical techniques to research data for analyzing and interpreting data carefully.

 

Course Outcomes: 

Course

Learning Outcome

 (at course  level)

Learning and teaching strategies

Assessment Strategies

Course

 Code

Course

Title

24GBCA

202B

Data Analysis Lab

 (Practical)

CO97. Implement statistical computations on different data sets using spreadsheets.

CO98. Analyse the role of different functions in data analysis.

CO99. Compute and interpret the coefficient of correlation for bivariate data.

CO100. Create charts and to visualize numeric data.

CO101. Perform sensitivity analysis on data for decision making related to a specific problem.

CO102.Contribute effectively in course-  specific interaction.

Approach in teaching:

Interactive Lectures,

Discussion, Reading assignments, Demonstration,

 

Learning activities for the students:

Self-learning assignments, Seminar presentation, Giving tasks.

Class test, Semester end examinations, Quiz, Assignments, Presentation, Individual and group projects

 

 

Unit I: 

Note: Students should be given hands-on experiences to use appropriate software packages for selected statistical analysis.

  • Introduction: Reading data into spreadsheets using various formats, referencing, Custom & Conditional Formatting.
  • Functions to Organize Data: 
  • Mathematical and statistical Functions: Sum, Count, CountBlank, Max, Min, Ceiling, Floor, Trunc, Abs, Fact, Int, Log, Mod, Power, Round, Exp, Countif & Countifs, Sumif & Sumifs, Averageif  & Averageifs 
  • Logical Functions: If, And, Or, Nested If, Lookup Functions: Hlookup & Vlookup.
  • Text Functions: Len, Left, Right, Mid, Find, Replace, Upper, Lower, Proper, Trim, Substitute, Concatenate
  • Date & Time Functions: Now, Date, Time, Day, Month, Year, Hour, Minute, Second, Today, Network Days.
  • Sorting & Filtering
  • Pivot Tables, and Charts: line, column, bar, pie chart, scatter plot.
  • What if analysis: Data tables, Scenario, Goal seek, Sub-totals.
  • Mean, Mode, Median, Correlation, Regression, Stdev Function
  • Descriptive Statistics: Frequency distributions, mean, median, mode, standard deviation, sample variance, range.

 

ESSENTIAL READINGS: 

1.  Winston,” Microsoft Excel 2013: Data Analysis and Business Modeling”, PHI

2. Denise Etheridge, “Excel Data Analysis”, Wiley Publication, Third Edition 

REFERENCES: 

SUGGESTED READINGS: 

1.    Hector Guerrero, “Excel Data Analysis - Modeling and Simulation”, Springer

2.    Chandan Sengupta, “Financial Analysis and Modeling using Excel and VBA”, Wiley.

e -RESOURCES:

1.    https://www.w3schools.com/EXCEL/index.php

2.    https://support.microsoft.com/en-us/office/excel-video-training-9bc05390-e94c-46af-a5b3-d7c22f6990bb

3.    https://www.tutorialspoint.com/advanced_excel/index.htm

4.    https://www.coursera.org/learn/excel-advanced

JOURNALS:

1.  Journal of Information Display, https://www.tandfonline.com/journals/tjid20

EPJ Data Science, https://epjdatascience.springeropen.com/

Academic Year: 
2024-25
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