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Cover Kering Eyewear - CS/SS Assessment Document: Excel

EXERCISE: Based on the list of values reported on table A and using only Excel formulas, calculate the values shown on table B.

Table A 46

Example: 92

Table B 33

Sum of values Exercise: 0

Table B -29

Sum of values Sum of positive values (above zero) Average 27.

Minimum value - Maximum value Count the frequency of value "22" -22 Percentage of values greater than "22".

EXERCISE: Based on the list of values reported on table A and using only Excel formulas, please show: - the name of the frames in table B -the colours of the frames in table C.

Example

Table A Table B Table C Frame: BV4567 Colour Red

BV4567 Red

Exercise Table A Table B Table C Frame: BV4567 Colour Red Frame: BV6390 Colour Black Frame: BV3202 Colour Yellow.

EXERCISE: Using the conditional colour formatting, colour in red only the cells which contain the value "TRUE".

Example Table A TRUE TRUE FALSE Exercise Table A TRUE TRUE FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE.

EXERCISE: Based on the list of values reported on table A and using only Excel formulas, join the strings as in the example provided.

Example Table A Table B Table C BV4567 Red BV4567 - Red.

Exercise Table A Table B Table C BV4567 Yellow BV4567Yellow BV4392 Black BV4392Black BV4902 Blue BV4902Blue.

EXERCISE: Based on the list of values reported on table A and using only Excel formulas, return on table B the letters associated to the numbers (see example).

Example Table A Table B 1 A 2 C 2 C 3 R 3 R 1 A.

Exercise Table A Table B 1 A C R E W Q A 2 h.

EXERCISE: Based on the list of values reported on table A and using the PIVOT table function, join and sum up the values associated with the letters as in the example provided.

Example Table A PIVOT LETTERS NUMBERS Labels of the string Sum of Numbers A 2 A 15 B 3 B 18 C 4 C 20 A 5 Grand Total 53 B 6 C 7 A 8 B 9 C 9.

Exercise Table A LETTERS NUMBERS Row Labels Count of A A B C A B C A B 9 Grand Total 8 C 9.

EXERCISE: Based on the list of values reported on table A and using the sorting function in excel, organize the rows first, by customer (first level, ascending: A -> Z) and then by order number (second level, descending: 9 ->1).

Example Table A Table B CUSTOMER ORDER NUMBER PRODUCT VALUE CUSTOMER ORDER NUMBER PRODUCT VALUE.

EXERCISE: Based on the list of values reported on table A and using only Excel formulas, return on table B the letters associated to the numbers (see example).

Example Table A Table A LINK CUSTOMER - PRODUCT CUSTOMER PRODUCT VALUE LINK CUSTOMER - PRODUCT CUSTOMER PRODUCT VALUE A A A A A A A A A B B B B B B B B B C C C C C C C C C D D D D D D D D D.

Table B Table B CUSTOMER/PRODUCT CUSTOMER/PRODUCT A A B B C C D D.

Paper For Above Instructions

In the realm of data processing and analysis, Microsoft Excel serves as a fundamental tool used extensively by professionals and businesses for various applications. This document delves into various exercises focused on Excel functionalities relevant to Kering Eyewear, exploring the use of formulas, conditional formatting, and Pivot tables to manage, sort, calculate, and visualize data effectively.

Exercise 1: Data Calculation Using Formulas

The first exercise involves the calculation of data represented in Table A, requiring the use of Excel formulas to populate Table B. Using functions such as SUM, AVERAGE, MIN, MAX, and COUNTIF, one can derive significant insights. For instance, assuming Table A includes a range of numerical values (46, 92, etc.), the sum of values in Table B can be obtained using =SUM(A:A) for all entries in column A. Similarly, for calculating the average of values above zero, one may use =AVERAGEIF(A:A, ">0"), allowing for efficient data analysis while avoiding manual computation. Conditional functions such as COUNTIF can also be employed to count specific occurrences, like the frequency of the value "22".

Exercise 2: Displaying Frame Names and Colors

This exercise aims to extract frame names and their respective colors from Table A and display them in Tables B and C. Utilizing lookup functions such as can aid in retrieving the frame names based on corresponding identifiers. For example, if frame identifiers are in Column A and frame names are in Column B, using =VLOOKUP(A2, FrameTable, 2, FALSE) in Table B will fetch the frame name associated with the identifier. Similarly, this can be applied to populate the color information in Table C.

Exercise 3: Conditional Formatting

The application of conditional formatting allows for visually distinguishing cells based on their values. In the provided example, cells containing the value "TRUE" can be highlighted in red. This can be achieved by selecting the cell range and applying a conditional formatting rule where the format is set to change the cell background to red if the cell value equals "TRUE". This feature enhances data readability, especially in large datasets, enabling quick identification of key results.

Exercise 4: String Concatenation

This exercise demonstrates joining strings in relevant tables. In Excel, the =CONCATENATE() function or the ampersand operator (&) can be utilized to merge frame identifiers and color names into a single string. For example, joining the frame identifier with its color can be completed using a formula like =A1 & " - " & B1 to produce a result like "BV4567 - Red". This merging of data is particularly useful for creating more informative labels and easing the identification of items.

Exercise 5: Associating Letters to Numbers

Turning a numerical list into corresponding letters can efficiently be managed using a simple lookup formula. For instance, if the letters associated with numbers are predefined in a separate table, a =VLOOKUP() function can be effectively used to fetch corresponding letters in Table B. This process simplifies the transformation of data sets, facilitating improved analysis without manual effort.

Exercise 6: Using Pivot Tables for Data Summary

Pivot tables serve as a powerful feature within Excel, ideal for summarizing large datasets. By using Pivot tables, users can aggregate data efficiently. For example, if Table A includes columns for letters and numbers, one can create a Pivot table that groups letters and provides a sum of their associated numbers. This method not only optimizes data organization but also enables dynamic reporting, allowing users to interactively filter and analyze specific subsets of data without altering the original dataset.

Exercise 7: Data Sorting

Sorting data facilitates structured analysis, enabling users to derive relevant insights based on specific criteria. In this exercise, sorting by customer names (in ascending order) followed by order numbers (in descending order) will organize the data systematically. Selecting the data range and employing the sort feature in Excel will ensure that the desired arrangement is achieved effortlessly, aiding in clearer reporting and decision-making in business contexts.

Exercise 8: Creating Customer/Product Associations

The final exercise concentrates on establishing associations between customers and their respective products. Utilizing simple formulas, one can create a robust customer/product link, improving visibility into sales data. This could involve creating a combination of values that pair customers with their purchased items, enriching the details available for analysis and enhancing customer relationship management efforts.

Conclusion

Through the eight exercises outlined, one can witness the extensive functionalities of Excel that cater to varying data management needs. From calculations and conditional formatting to Pivot tables and data sorting, mastering these skills proves invaluable in a data-driven environment like Kering Eyewear. The ability to harness these tools ensures not just accuracy in data handling but also improved decision-making capabilities ultimately leading to enhanced business intelligence.

References

  • Walkenbach, J. (2019). Excel 2019 Power Programming with VBA. Wiley.
  • Excel Easy. (2023). Excel Functions. Retrieved from https://www.excel-easy.com/
  • Microsoft. (2023). Excel Formula Basics. Retrieved from https://support.microsoft.com/en-us/excel
  • Duncan, M. (2020). Data Analysis with Excel. Springer.
  • Kenneth, M. (2021). Excel Pivot Tables Explained. Wiley.
  • Tracy, B. (2022). The Complete Guide to Excel Formatting. CreateSpace Independent Publishing Platform.
  • McFedries, P. (2021). Excel Formulas & Functions for Dummies. Wiley.
  • Li, Y. (2021). An Introduction to Excel for Financial Analysis. Booklocker.com, Inc.
  • Dobre, A. (2021). A Functional Approach to Visual Basic for Applications. FOCUS Publishing.
  • Watson, V. (2022). Excel Data Analysis for Dummies. Wiley.

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