The District Data Support Person                                                                                    2018 – Present
Responsibilities                        
1- Monthly Cadres DDM Cards TSAs. UCSP, PTPs/STPs, RSPs, and Nomads
2- verify clean errors, then add the data to Soft and Hard before sending it to the Provincial Office within 15 days. 
3- Clearing files according to surveillance, identifying incomplete files, and sharing them with the DC
4- Updating online monthly and weekly trackers.
5- On a daily basis, I collect daily activities from my WHO staff, analyze them, and share the results with my officer
6- On a daily basis, I work with the PTP-STPS-EID teams to clean data from 52 to 70 points using multiple methods. To save time and ensure accuracy, I continuously update the data quickly using Power Automate
7- Pre-campaign, intra-campaign, and post-campaign, my job is to collect data for as many indicators or tally sheet analyses as possible, including HR Validation, ICM clusters analysis, and staff presence analysis (TPO-UCPOs-UCSPs-TTM-RSPs). It is my responsibility to ensure all staff are present and data is collected during the campaign            
Accomplishments
8- Received an award from the DC and District Coordinator for my work with HTA in 2023.
                               
Temporary Team Monitor          
Responsibilities                            JAN-2015
1- During the pre-campaign, intra-campaign, and post-campaign phases, I manage various indicators including team training, team monitoring, UPEC meetings, MP validation, field validation, and intra-campaign
Monitoring
 

2) Temporary Team Monitor                                                                                                                                        Jan-2015

During the pre-campaign, intra-campaign, and post-campaign phases, I manage various indicators including team training, team monitoring, UPEC meetings, MP validation, field validation, and intra-campaign Monitoring

 Tools Used: Python, SQL, Excel                                                                            feb2015

Developed a Python script to analyze a retail store's sales data, extracting insights on sales trends, customer demographics, and product performance.
Utilized Pandas for data manipulation and Matplotlib for visualizing sales trends over time.
Integrated findings with Excel for detailed reports and presentations, leading to a 10% increase in targeted marketing efficiency.