SALMANUL FARIS KP

Detail-oriented Data Analyst and Power BI Developer with 2.7 years of experience in building interactive dashboards and data models. Proficient in Power BI, DAX, SQL, and Python, with expertise in ETL processes and advanced analytics. Skilled at transforming data into actionable insights to drive business decisions and optimize reporting efficiency.

• Data Science Certification, ExcelR Solutions, Mar 2023
• MySQL 5.0 Developer, Mar 2023
• Machine Learning with Python, May 2023
• Machine Learning with Python - Level 1, May 2023
• Python Essentials 2, Aug 2023
• Foundation: Data, Data, Everywhere, Aug 2024
• Ask Questions to Make Data-Driven Decisions, Sep 204
• Prepare Data for Exploration, Sep 2024
• Process Data from Dirty to Clean, Sep 2024
• Share Data Through the Art of Visualization, Sep 2024
• Data Analysis with R Programming, Sep 2024
• Analyze Data to Answer Questions, Sep 2024
• Google Data Analytics Capstone: Complete a case study, Sep 2024
• Google Data Analytics Specialization, Sep 2024
• Data Analysis and Visualization with Power BI, Oct 2024
• Power BI - Business Intelligence for Beginners to Advance, Sep 2025
• The Practical Guide to SQL Server Reporting Services (SSRS), Oct 2025

• Participated in company-wide AI Hackathon — developed PMO Chatbot integrated with Microsoft Teams for automated timesheet entry and project tracking

• Named Top Performer of 2025 by Group CIO Mr. Anshu Sharma Raja, Arab Bank

Power BI Desktop, Power BI Report Builder, Power BI Service, SSRS, Tableau, SSAS (Tabular), SQL Server Management Studio (SSMS), Visual Studio, Informatica, Microsoft SQL Server, MySQL, Google Cloud Platform (GCP), Python (Pandas, NumPy, Scikit-learn, TensorFlow), DAX, SQL (MySQL), R, Advanced Excel, Exploratory Data Analysis (EDA), Statistical Analysis, Predictive Modeling, Hadoop, Jira

• Cognos to Power BI Report Migration and Development, Migrated financial reports from Cognos to Power BI, improving accuracy, efficiency, and usability., Developed and maintained complex SQL Server Management Studio (SSMS) databases, optimizing queries and stored procedures for efficient data retrieval and management in credit reporting systems., Designed and implemented SSAS (SQL Server Analysis Services) data models, establishing robust relationships between tables to support tabular analysis of credit data., Created interactive, real-time Banking Report dashboards using Power BI, providing stakeholders with actionable insights on key credit metrics and trends., Led a team of 4 experienced data analysts from external vendors, providing both business and technical knowledge transfer, offering project support, and ensuring progress through validation of dashboards and regular monitoring of deliverables., Successfully migrated over 300+ banking reports from IBM Cognos to Power BI, ensuring seamless transition, enhanced visualization, and improved data accessibility for business users.
• Power BI dashboard development for PMO, Enabled real-time project tracking and streamlined HR reporting through interactive dashboards and automation., Built dynamic Power BI dashboards for the PMO team to monitor project status, resource allocation, and performance metrics, directly addressing the top three bottlenecks in project tracking., Developed advanced DAX measures and calculated columns to enhance reporting accuracy and efficiency across project data., Designed an automated HR biometric attendance report using Power BI by integrating Zoho export data, reducing report preparation time from 1-2 hours to under 2 minutes and eliminating manual work., Conducted Power BI training sessions for the HR team, enabling independent use and adoption of automated reports., Published both PMO and HR reports to the Power BI Report Server, providing secure, real-time access for department heads and stakeholders.
• Hourly Power Consumption Forecasting (Time Series Analysis), Predicted power consumption for 30 days to aid resource planning and operational efficiency., Implemented predictive models using XGBoost, ARIMA, SARIMA, and SEM algorithms; deployed via Streamlit., Compared algorithm performance to ensure the highest forecasting accuracy.
• Myocardial Infraction Prediction, Predicted heart attack types using machine learning models for improved healthcare decisions., Built classification models (Logistic Regression, Random Forest, Gradient Boosting, etc.) to identify 8 heart attack types., Engineered and implemented data balancing techniques that enhanced model accuracy by 15%, enabling reliable predictions of heart attack types to support better healthcare decision-making processes.
• Financial Sentiment Analysis (NLP), Analyzed financial documents to classify sentiments as positive, negative, or neutral., Conducted text preprocessing (stop-word removal, tokenization) and EDA (unigram, bigram, trigram analysis)., Utilized TF-IDF and Word2Vec for vectorization; applied classification algorithms to identify sentiment trends.