MEAL officer learning log

Slicers in Power BI are a type of visual filter that allow users to interactively slice and dice data in a report or dashboard. Slicers provide a way to quickly filter data and focus on specific subsets of information, making it easier to analyze data and gain insights. To create a slicer for filtering dataset by several attributes you can follow next steps: For each column in our future filter, create own separate measure, like: Count BNF = COUNTA (' base '[ Beneficiaries ]) Create a one column table with all options you need to be able to filter. Each record will be show like an option in slicer visual. Create common view measure with SWITCH operator, like: DisplayMeasure = SWITCH ( SELECTEDVALUE ( Distributed [ Distributerd Item ]), " CASH for rent ", [ Count CFR ], " CASH for winterization ", [ Count CFW ], " NFI Kits ", [ Count NFI ], " WASH Kits ", [ Count WASH ], " Beneficiaries ", [ Count BNF ]) Place the new measur...

Step-by-step instruction Sign up at https://www.mapbox.com Go to account webpage and copy default public token or create now token. Add MapBox custom visual into the Power BI document page. Go to Build visual tab and place table columns into Location and Color fields. Go to Format visual -> Visual -> Viz Settings and paste the token into the Access Token field. Disable the Circle checkbox and enable the Choropleth checkbox . Go to MapBox Studio tilesets collection and copy needed map's Tileset ID (use ellipsis button). Open the map and copy the layer name. Copy the field name you need. Go to Format visual -> Visual -> Choropleth and change Data level value to Custom Tileset Paste the information from steps 6-8 into Vector Tile Url , Source Layer Name and Vector Property fields respectively. Done. Notice You have to upload your own maps after registration at MapBox. Map size can be decreased by coordinates values processing with accuracy decreasing (coordinate ...

Today's solved tasks: Made next iteration of particular data cleaning; Prepare aggregated data table for items distribution; Split collected data group by communities. Time used: 4h

Research of the contents of the Middle Name field has been performed. Processed over 5k records; 68% records fixed; Unique middle names amount decreased from 425 to 306; Created preliminary names and middle names reference list; Time used: 4h

Research of the contents of the Settlement field has been performed. Processed over 5k records; 75% records fixed; Unique names amount decreased from 368 to 282; 86 misnaming cases fixed; Completed bringing settlement names to a general appearance; Input typos fixed. Research of the contents of the Name field has been performed. 29 possible name collisions found; 3 records pass; 24 records fixed; 2 irreparable name collisions found. Time used: 3h

First step in geo-based data visualisation design. The Power BI GIS widget has been chosen; Found and checked geo-json files geoBoundaries-UKR-ADM3/ADM2 for districts and communities location; Analysed geo-json files content and geopoints accuracy; Investigated offline json edit tools; [planned] decreasing geopoints accuracy to reduce file size;  Checked district and community naming compatibility between DB and geo-json; Found and analysed naming collisions; [planned] Make changes to the Power BI database structure.  Time used: 5h

In this comprehensive video, we explore the modern data ecosystem and the role of data analytics. Starting from the data analyst role, we delve into the language for data professionals and understanding data repositories and big data platforms. We then move on to the process of gathering, wrangling, analyzing, and mining data, and communicating data analysis findings. The video also covers Excel basics for data analysis, including an introduction to spreadsheets for data analysis, getting started using spreadsheets, basics of data quality and privacy, cleaning data, and data analysis basics such as filtering and sorting data. We also explore using pivot tables. The section on data visualization and dashboards covers creating charts, advanced charts, and dashboards using spreadsheets, as well as using IBM Cognos Analytics for creating dashboards. Moving on to Python for data science, AI, and development, we cover types, expressions and variables, string operations, lists and tuples, d...