In the beginning, the case was just data for a company that did not indicate any useful information that would help decision-makers. In this case, I had to ask questions that could help extract and explore information that would help decision-makers improve and evaluate performance. But before that, I did some operations in the data to help me to analyze it accurately: 1- Understand the data. 2- Clean the data “By power query”. 3- insert some calculation and columns like “COGS” cost of goods sold by power query. 4- Modeling the data and adding some measures and other columns to help me in analysis. Then I asked these questions: To Enhance Customer Loyalty What is the most used ship mode by our customer? Who are our top 5 customers in terms of sales and order frequency? To monitor our strength and weak points Which segment of clients generates the most sales? Which city has the most sales value? Which state generates the most sales value? Performance measurement What are the top performing product categories in terms of sales and profit? What is the most profitable product that we sell? What is the lowest profitable product that we sell? Customer Experience On Average how long does it take the orders to reach our clients? Based on each Shipping Mode

Then started extracting her summaries and answers from the pivot tables and designing the data graphics in a dashboard for easy communication and reading of the information as well. And after completing these operations, I made some calculations related to the KPI to calculate the extent to which sales officials achieved and the extent to which they achieved the target.

This interactive sales dashboard is designed in Excel for B2C type of Businesses like Dmart, Walmart, Amazon, Shops & Supermarkets, etc. using Slicers, Pivot Tables & Pivot Chart.

Dashboard Overview

1. Sales dashboard ==> basically, it is designed for the B2C type of business. like Dmart, Walmart, Amazon, Shops & supermarkets, etc.
2. Slices ==> slices are used to drill down the data, on the basis of yearly, monthly, by sales type, and by mode of payment.
3. Total Sales/Total Profits ==> here is, the total sales, total profit, and profit percentage these all are combined into a monthly format and we can hide or unhide it to view it as individually or comparative.
4. Product Visual ==> the visual indicates product-wise sales for the selected period. Only 10 products are visualized at a glance, and you can scroll up & down to view other products in the list.
5. Daily Sales ==> It shows day-wise sales. (Area Chart)
6. Sales Type/Payment Mode ==> It shows sales percentage contribution based on the type of selling and mode of payment.
7. Top Product & Category ==> this is for the top-selling product and product category.
8. Category ==> the final one is the category-wise sales contribution.

Datasheets Overview

1. The dataset has the master data sheet or you can call it a catalog. It is added in the table form.
2. The first column is the product ID the list of items in this column is unique.
3. Then we have the product column instead of these two columns, we can manage with only one also but I kept it separate because sometimes product names can be the same, but some parameters will be different, like price, supplier, etc.
4. The next column is the category column, which is the product category. like cosmetics, foods, drinks, electronics, etc.
5. Then we have 4th column which is the unit of measure (UOM) you can update it also, based on the products you have.
6. And the last two columns are buying price and selling price, which means unit purchasing price and unit selling price.

Input Sheet

The first column is the date of Selling. The second column is the product ID. The third column is quantity. The fourth column is sales types, like direct selling, are purchased by a wholesaler or ordered online. The fifth column is a mode of payment, which is online or in cash. You can update these two as per requirements. The last one is a discount percentage. if you want to offer any discount, you can add it here.

Analysis Sheet: where all backend calculations are performed.

So, basically these are the four sheets mentioned above with different tasks.

However, a sales dashboard enables organizations to visualize their real-time sales data and boost productivity.

A dashboard is a very useful tool that brings together all the data in the forms of charts, graphs, statistics and many more visualizations which lead to data-driven and decision making.

Questions & Answers

1. What percentage of profit ratio of sales are displayed in the year 2021 and year 2022? ==> Total profit ratio of sales in the year 2021 is 19% with large sales of PRODUCT42, whereas profit ratio of sales for 2022 is 22% with large sales of PRODUCT30.
2. Which is the top product that have large number of sales in year 2021-2022? ==> The top product in the year 2021 is PRODUCT42 with the total sales of $12,798 whereas in the year 2022 the top product is PRODUCT30 with the total sales of $13,888.
3. In Area Chart which product is highly sold on 28th April 2022? ==> The large number of sales on 28th April 2022 is for PRODUCT14 with a 24% of profit ratio.
4. What is the sales type and payment mode present? ==> The sale type and payment modes show the sales percentage contribution based on the type of selling and mode of payment. Here, the sale types are Direct Sales with 52%, Online Sales with 33% and Wholesaler with 15%. Also, the payment modes are Online mode and Cash equally distributed with 50%.
5. In which month the direct sales are highest in the year 2022? ==> The highest direct sales can be easily identified which is designed by monthly format and it’s the November month where direct sales are highest with 28% as compared with other months.
6. Which payment mode is highly received in the year 2021 and year 2022? ==> The payments received in the year 2021 are the cash payments with 52% as compared with online transactions which are 48%. Also, the cash payment highly received is in the month of March, July and October with direct sales of 42%, Online with 45% and wholesaler with 13% with large sales of PRODUCT24. ==> The payments received in the year 2022 are the Online payments with 52% as compared with cash payments which are 48%. Also, the online payment highly received is in the month of Jan, Sept and December with direct sales of 45%, Online with 37% and whole...

Abstract

The Survey on Interest Rate Controls 2020 was conducted as a World Bank Group study on interest rate controls (IRCs) in lending and deposit markets around the world. The study aims to identify the different types of formal (or de jure) controls, the countries that apply then, how they implement them, and the reasons for doing so. The objective of the study is to advance knowledge on this topic by providing an evidence base for investigating the impact of IRCs on economic outcomes.

The survey investigates present IRCs in each surveyed country, the reasons why they have been applied, the framework and resources associated with their application and the details as to their level and functioning. The focus is on legal forms of control (i.e. codified into law) as opposed to de facto controls. The new database on interest rate controls, a popular form of financial repression is based on a survey of 108 countries, representing 88 percent of global gross domestic product. The interest rate controls presented in this dataset were in effect in 2019.

Geographic coverage

Global Survey, covering 108 countries, representing 88 percent of global GDP.

Analysis unit

Regulation at the national level.

Universe

Banking supervisors and Local Banking Associations.

Kind of data

Sample survey data [ssd]

Mode of data collection

Mail Questionnaire [mail]

Research instrument

Bank supervisors and banking associations were provided with a standard excel file with five parts. The survey was structured in five parts, each placed in a different excel sheet. Part A: Introduction. Countries with no IRCs in place were asked to only answer this sheet and leave the rest blank. Part B: Presented the definitions of controls, institutions, products and additional aspects that will be covered in the survey. Part C: Introduced a set of qualitative questions to describe the IRCs in place. Part D: Displayed a set of tables to quantitatively describe the IRCs in place. Part E: Laid out the final set of questions, covering sanctions and control mechanisms that support the IRCs' enforcement. The questionnaire is provided in the Documentation section in pdf and excel.

The dataset of ground truth measurements synchronizing with the airborne WiDAS mission was obtained in the Linze station foci experimental area on May 30, 2008. WiDAS, composed of four CCD cameras, one mid-infrared thermal imager (AGEMA 550), and one infrared thermal imager (S60), can acquire CCD, MIR and TIR band data. The simultaneous ground data included: (1) soil moisture (0-5cm) measured nine times by the cutting ring method (50cm^3) along LY07 and LY08 quadrates, and once by the cutting ring method and once by ML2X Soil Moisture Tachometer in the six points of Wulidun farmland quadrates. The preprocessed soil volumetric moisture data were archived as Excel files. (2) surface radiative temperature measured by two handheld infrared thermometer (5# and 6# from Cold and Arid Regions Environmental and Engineering Research Institute which were both calibrated) in the LY07 and LY08 quadrates (98 sample points and repeated three times) and the Wulidun farmland quadrates (various points and repeated three times). Data were archived as Excel files. (3) spectrum of maize, soil and soil with known moisture measured by ASD Spectroradiometer (350～2 500 nm) from BNU,and the 40% reference board in Wulidun farmland quadrate and the desert transit zone strips. Raw spectral data were archived as binary files, which were recorded daily in detail, and pre-processed data on reflectance were archived as Excel files. (4) maize BRDF measured by ASD Spectroradiometer (350～2 500 nm) from BNU, the 40% reference board, two observation platforms of BNU make and one of Institute of Remote Sensing Applications make in Wulidun farmland quadrate and the desert transit zone strips. Raw spectral data were archived as binary files , which were recorded daily in detail, and pre-processed data on reflectance and transmittivity (read by ViewSpecPro) were archived as text files (.txt). (5) LAI of maize, poplar and the desert scrub measured by the fisheye camera (CANON EOS40D with a lens of EF15/28), shooting straight downwards, with exceptions of higher plants, which were shot upwards in Wulidun farmland quadrate I, the desert transit zone and the poplar forest. Data included original photos (.JPG) and those processed by can_eye5.0 (in excel). (6) LAI measured by the ruler and the set square in D and H quadrates of the Wulidun farmland. Part of the samples were also measured by LI-3100 and compared with those by manual work for further correction. Data were archived as Excel files. See the metadata record “WATER: Dataset of setting of the sampling plots and stripes in the Linze station foci experimental area” for more information of the quadrate locations.

Matlab scripts, source C-code, mex compiled C-code, and figure data points for the paper entitled “Semi-Analytical Analytical Modelling of Linear Mode Coupling in Few -Mode Fibers”.

Folders: 0_differential_equations_solver Matlab scripts based on the Symbolic Math Toolbox for the derivation of a semi-analytical solution to the differential equations describing linear mode coupling in few-mode fibres. Scripts available for 3, 4, 5 and 6 modes.

1_C_code_for_high_precision_solution_of_polynomials C-code for the numerical evaluation of the 6-modes semi-analytical solutions obtained in 0_differential_equations_solver. Two versions: “highPrecRootFind_6M_doubleIO” uses always the same seed for the root finding section; “highPrecRootFind_6M_doubleIO_rand” uses a randomized seed for the root finding section.

2_crosstalk_vs_radial_displacement Script for plotting typical fibre coupling coefficients and plotting of the crosstalk introduced by a single fibre displacement as a function of the radial displacement and averaged in the azimuth coordinate.

3_solutions_precision Script for the evaluation of the precision of the semi-analytical solutions proposed against Runge-Kutta-Fehlberg Method (RKF45) numerical solutions.

98_poly_solvers_mex_files_compiled_for_R2014b_64bit Compiled mex C-code at 1_C_code_for_high_precision_solution_of_polynomials. Compiled for Mex Matlab R2014b 64bit.

99_fibre_parameters Typical fibre parameters used in this dataset.

100_figures_data_poins Excel files containing the data points in the figures presented in the paper.

State estimates for these years are no longer available due to methodological concerns with combining 2019 and 2020 data. We apologize for any inconvenience or confusion this may causeBecause of the COVID-19 pandemic, most respondents answered the survey via the web in Quarter 4 of 2020, even though all responses in Quarter 1 were from in-person interviews. It is known that people may respond to the survey differently while taking it online, thus introducing what is called a mode effect.When the state estimates were released, it was assumed that the mode effect was similar for different groups of people. However, later analyses have shown that this assumption should not be made. Because of these analyses, along with concerns about the rapid societal changes in 2020, it was determined that averages across the two years could be misleading.For more detail on this decision, see the 2019-2020state data page.

Title: Data on undergraduate students' creativity under HyFlex flipped classroom modeDescription: This dataset contains the quantitative data collected for a study investigating the effect of the HyFlex flipped classroom on students' creativity and gender differences.Methodology: Data were collected through a paper-based questionnaire in China between 28th April 2025 and 6th June 2025. The instrument measured students' creativity by using the CPSS.Data Contents: The dataset consists of one Excel file. It includes 301 rows (each representing one anonymous respondent) and 21 columns (each representing a variable). Key variables include ParticipantID, Group, Gender, and Time. Total_Raw (total score of orignal data), Total_100 (The percentage score generated by the formula)Data Processing: The raw data were cleaned by removing incomplete responses and calculating composite scores for scales as described in the first sheet of Excel (README).Usage Notes: A comprehensive codebook (README) is provided within this dataset, which defines all variables, values, and scoring procedures.Note for Peer Review: This dataset is under restricted access for the purpose of double-blind peer review. It will be made fully public upon acceptance of the associated manuscript.