● Building data pipelines to gather global market data, commodity prices, weather conditions, and wave height data from external sources. ● Managing and optimizing cloud-based data storage for secure and fast data access. ● Setting up ETL pipelines to clean, integrate, and consolidate data, preparing it for analysis and visualization. ● Analysing real-time commodity price trends and demand to deliver relevant pricing and market predictions. ● Generating insights on commodity conditions and identifies optimization opportunities based on data trends and user behaviors. ● Developing and compelling data visualizations to facilitate user understanding of market conditions and potential product optimization.

● Execute hands-on stealth tech startup project as part of RevoU Data Analytics. ● Responsible to execute analysis such as feature adoption and prioritization analysis, customer segmentation, churn analysis, and KPI dashboard monitoring. ● Manage to run 1 self-data project in 2 weeks with satisfactory results from supervisor.

● Inputting climate data variables up to 12 hours such as; temperature, relative humidity (RH), air pressure, sunshine duration, radiation, and precipitation daily. ● Analyzing those climate data daily like highest, lowest, average, trends, and boxplot from those variables of climate data using Python. ● Creating bulletin or dissemination of analysis climate and forecasting about precipitation, drought level, water availability level, flood-prone areas, and average temperature monthly with an accuracy level of 80%. Projects: 1. US Regional Sales Data. (January 2023 – April 2023). Tools: Google Sheets, Python, Tableau. ● Problem discovery, We found A stagnant profit that happened in 2020 with a growth of +0.12% from 2019. ● We did EDA and from EDA We got 56 stores that caused stagnant profits in 2020, so We need store segmentation to boost our profit. ● Performed Regression to Understand the influence of using segmentation to increase profit and orders. An increase of 81-121 order quantity per month could boost profit up to $1.24M. ● Created an interactive dashboard using tableau. 2. Mutual Fund Company Customer Segmentation Analysis. (February 2023 – March 2023). Tool: Python. ● Cleaned data by checking the data type, checking values and typos, finding the outliers, treating missing & irrelevant values, and manipulating the data. ● Performed descriptive statistics for string and numeric variables. ● Defined the user and transaction trends. ● Created customer segmentation with elbow and silhouette methods. 3. TheLook eCommerce User Retention Analysis (Cohort Analysis). (February 2023). Tools: Google BigQuery, Google Sheets. ● Defined the primary key of the tables to connect them. ● Found tables that can be used to answer the business problem. ● Wrote query in Google BigQuery. ● Performed cohort analysis to find the user retention rate.