The Agricultural Research Service of the US Department of Agriculture (USDA) in collaboration with other government agencies has a program to track changes in the sodium content of commercially processed and restaurant foods. This monitoring program includes these activities: Tracking sodium levels of ~125 popular foods, called "Sentinel Foods," by periodically sampling them at stores and restaurants around the country, followed by laboratory analyses. Tracking levels of "related" nutrients that could change when manufacturers reformulate their foods to reduce sodium; these related nutrients are potassium, total and saturated fat, total dietary fiber, and total sugar. Sharing the results of these monitoring activities to the public periodically in the Sodium Monitoring Dataset and USDA National Nutrient Database for Standard Reference and once every two years in the Food and Nutrient Database for Dietary Studies. The Sodium Monitoring Dataset is downloadable in Excel spreadsheet format. Resources in this dataset:Resource Title: Data Dictionary. File Name: SodiumMonitoringDataset_datadictionary.csvResource Description: Defines variables, descriptions, data types, character length, etc. for each of the spreadsheets in this Excel data file: Sentinel Foods - Baseline; Priority-2 Foods - Baseline; Sentinel Foods - Monitoring; Priority-2 Foods - Monitoring.Resource Title: Sodium Monitoring Dataset (MS Excel download). File Name: SodiumMonitoringDatasetUpdatedJuly2616.xlsxResource Description: Microsoft Excel : Sentinel Foods - Baseline; Priority-2 Foods - Baseline; Sentinel Foods - Monitoring; Priority Foods - Monitoring.

The present dataset was developed using the MODIS Normalized Difference Snow Index with a spatial resolution of 500 m as input for the SNOWMAP algorithm to detect lake ice from daily clear-sky observations. Furthermore, for cloud-cover conditions, lake ice was identified based on the spatial and temporal continuity of lake-ice data. On this basis, the daily lake-ice monitoring data of 2612 lakes of the Tibetan Plateau from 2002 to 2018 were calculated and classified. Moreover, a time-series analysis of lake ice coverage, which included lakes with surface area greater than 1 km2, was carried out to provide a clear list of lakes for which lake ice phenology can be estimated. The data set contains 5834 raster files, one vector file and 2612 Excel files (including 1134 time series with and without classification statistics). The raster file is named daily lake ice extent. The vector file contains such information as the number, name, location, surface area and classification number of the processed lake. The names of the excel files correspond to lake numbers. Each excel file contains four columns with the daily lake ice coverage information of its corresponding lake from July 2002 to June 2018. The attributes of each column are, successively, date, lake water coverage, lake ice coverage and cloud coverage. Users can first use the vector file to determine the number, location and classification number of a given lake, and then obtain the corresponding daily lake ice coverage data for a given year from the Excel file to use for the monitoring of lake-ice freeze-thaw and research on climate change.

HOW TO: - Hierarchy using the category, subcategory & product fields (columns “Product Category” “Product SubCategory”, & “Product Name”). - Group the values ​​of the column "Region" into 2 groups, alphabetically, based on the name of each region.

1. Display a table, which shows, for each value of the product hierarchy you created above, the total amount of sales ("Sales") and profitability ("Profit").
2. The same information as the previous point (2) in a bar chart illustration.
3. Display columns with the total sales amount ("Sales") for each value of the alphabetical grouping of the Region field you created. The color of each column should be derived from the corresponding total shipping cost (“Shipping Cost”). In the Tooltip of the illustration all numeric values ​​should have a currency format.
4. The same diagram as above (3), with the addition of a data filter at visual level filter that will display only the data subset related to sales with positive values ​​for the field "Profit".
5. The same diagram with the above point (3), with the addition of a data filter at visual level filter that will display only the subset of data related to sales with negative values ​​for the field "Profit".
6. Map showing the total amount of sales (size of each point), as well as the total profitability (color of each point). Change the dimensions of the image

PROJECT OBJECTIVE

We are a part of XYZ Co Pvt Ltd company who is in the business of organizing the sports events at international level. Countries nominate sportsmen from different departments and our team has been given the responsibility to systematize the membership roster and generate different reports as per business requirements.

Questions (KPIs)

TASK 1: STANDARDIZING THE DATASET

• Populate the FULLNAME consisting of the following fields ONLY, in the prescribed format: PREFIX FIRSTNAME LASTNAME.{Note: All UPPERCASE)
• Get the COUNTRY NAME to which these sportsmen belong to. Make use of LOCATION sheet to get the required data
• Populate the LANGUAGE_!poken by the sportsmen. Make use of LOCTION sheet to get the required data
• Generate the EMAIL ADDRESS for those members, who speak English, in the prescribed format :lastname.firstnamel@xyz .org {Note: All lowercase) and for all other members, format should be lastname.firstname@xyz.com (Note: All lowercase)
• Populate the SPORT LOCATION of the sport played by each player. Make use of SPORT sheet to get the required data

TASK 2: DATA FORMATING

• Display MEMBER IDas always 3 digit number {Note: 001,002 ...,D2D,..etc)
• Format the BIRTHDATE as dd mmm'yyyy (Prescribed format example: 09 May' 1986)
• Display the units for the WEIGHT column (Prescribed format example: 80 kg)
• Format the SALARY to show the data In thousands. If SALARY is less than 100,000 then display data with 2 decimal places else display data with one decimal place. In both cases units should be thousands (k) e.g. 87670 -> 87.67 k and 12 250 -> 123.2 k

TASK 3: SUMMARIZE DATA - PIVOT TABLE (Use SPORTSMEN worksheet after attempting TASK 1) • Create a PIVOT table in the worksheet ANALYSIS, starting at cell B3,with the following details:

• In COLUMNS; Group : GENDER.
• In ROWS; Group : COUNTRY (Note: use COUNTRY NAMES).
• In VALUES; calculate the count of candidates from each COUNTRY and GENDER type, Remove GRAND TOTALs.

TASK 4: SUMMARIZE DATA - EXCEL FUNCTIONS (Use SPORTSMEN worksheet after attempting TASK 1)

• Create a SUMMARY table in the worksheet ANALYSIS,starting at cell G4, with the following details:

• Starting from range RANGE H4; get the distinct GENDER. Use remove duplicates option and transpose the data.
• Starting from range RANGE GS; get the distinct COUNTRY (Note: use COUNTRY NAMES).
• In the cross table,get the count of candidates from each COUNTRY and GENDER type.

TASK 5: GENERATE REPORT - PIVOT TABLE (Use SPORTSMEN worksheet after attempting TASK 1)

• Create a PIVOT table report in the worksheet REPORT, starting at cell A3, with the following information:

• Change the report layout to TABULAR form.
• Remove expand and collapse buttons.
• Remove GRAND TOTALs.
• Allow user to filter the data by SPORT LOCATION.

Process

• Verify data for any missing values and anomalies, and sort out the same.
• Made sure data is consistent and clean with respect to data type, data format and values used.
• Created pivot tables according to the questions asked.

TimeSpec4LULC is archived in 30 different ZIP files owning the name of the 29 LULC classes (one class is divided into two files since it is too large). Within each ZIP file, there exists a set of seven CSV files, each one corresponding to one of the seven spectral bands. The naming of each file follows this structure: IdOfTheClass_NameOfTheClass_ModisBand.csv For example, for band 1 of the Barren Lands class, the filename is: 01_BarrenLands_MCD09A1b01.csv Inside each CSV file, rows represent the collected pixels for that class. The first 11 columns contain the following metadata: - “IdOfTheClass”: Id of the class. - “NameOfTheClass”: Name of the class. - “IdOfTheLevel0”: Id of the FAO-L0 (i.e., countries). - “IdOfTheLevel1”: Id of the FAO-L1 (i.e., departments, states, or provinces depending on the country). - “IdOfThePixel”: Id of the pixel. - “PurityOfThePixel”: Spatial and inter-annual consensus for this class across multiple land-cover products, i.e., Purity of the pixel. - “DataAvailability”: percentage of non-missing data per band throughout the time series. - “Index_GHM”: average of Global Human Modification index (gHM). - “Lat”: Latitude of the pixel center. - “Lon”: Longitude of the pixel center. - “.geo”: (Longitude, Latitude) of the pixel center with more precision. And, the last 223 columns contain the 223 monthly observations of the time series for one spectral band from 2002-07 to 2021-01. Along with the dataset, an Excel file named 'Countries_Departments_FAO-GAUL' containing the FAO-L0 and the FAO-L1 Ids and names (following the FAO-GAUL standards) is provided.

The Gazetteer of British Columbia is a spreadsheet of all official place names, including feature type, feature code, mapsheet, and latitude & longitude. The Gazetteer is extracted from the BC Geographical Names Information System (BCGNIS), the master database of British Columbia place names. The BC Gazetteer is available as a zipped SHP file, CSV or XLSX. See also BC Geographical Names (https://catalogue.data.gov.bc.ca/dataset/43805524-4add-4474-ad53-1a985930f352) dataset for other formats and download options. Some software (including MS Excel) may assume the wrong encoding when the .csv file is opened, and this can cause names with special characters to be presented incorrectly. With whatever software you use to open the .csv file, it is recommended to open the file in a way that explicitly acknowledges the UTF-8 character encoding. Alternatively, the Gazetteer is also available in .xlsx format which is recommended for MS Excel users because it will automatically recognize the correct character encoding.

SOURCES

It has 4 central portion, which shows Bangla text. One contains Only Bangla text(12179-unsuspicious, 7822-suspicious). Another one contains Bangla and English mixed(12725-unsuspicious, 7219-suspicious). Another one contains politically suspicious content(167-unsuspicious, 132-suspicious). Lastly, another contains the @name mentioned comment(6145-suspicious, 53855-unsuspicious). Finally, a CSV file contains all categorical Bangla Data. It contains 1,00,100+ data.

COLLECTION METHODOLOGY

Suspicious tweets- https://www.kaggle.com/datasets/syedabbasraza/suspicious-tweets Suspicious Tweets - https://www.kaggle.com/datasets/munkialbright/suspicious-tweets Suspicious Communication on Social Platforms - https://www.kaggle.com/datasets/syedabbasraza/suspicious-communication-on-social-platforms

Others are collected manually from Facebook comments. After collecting the Bangla comments, check dataset comment was understandable or not. Then step by step, each Excel file is converted into a datagram. then change the column name to the desired one('Detect' and 'Bangla Text'). I also drop some columns if needed. The files are saved in an Excel file because the CSV file can not contain Bangla text appropriately.

The 5 XLSX file are "suspicious_content(bangla)", "suspicious_content(bangla + english)", "suspicious_content(political)", "suspicious_content(including mention)" and "suspicious_content(all)". All the Excel files have only two columns, 'Detect' and 'Bangla Text'.

You will be able to see the dataset creation process in this link: https://www.kaggle.com/code/meherunnesashraboni/suspicious

suspicious

Bangla_Text

Detection

unsuspicious