Hello!

I'm Mohd Fardeen



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Name: Mohd Fardeen

Job Role: Data Analyst

Address: Kaushambi, Uttar Pradesh, India

Skills

Excel 95%
SQL 95%
PYTHON 85%
Data Visualization 90%
Statistical Analysis 85%
Machine Learning 80%

About

About Me

I'm a highly motivated and detail-oriented fresher Data Analyst eager to transform raw data into actionable insights. With a strong foundation in statistical analysis, data visualization, and database management, I'm adept at the entire data lifecycle, from cleaning and processing to reporting. I have practical experience utilizing Python (Pandas, NumPy, Matplotlib) and SQL to solve complex problems, and I'm proficient with Power BI for creating impactful dashboards. I thrive on challenging problems and am committed to continuous learning to deliver data-driven solutions. My portfolio showcases my ability to apply these skills in diverse projects.

  • Profile: Data Science & Analytics
  • Education: Bachelor of Computer Application(2019-2022), Master of Application(2025-2027)
  • Language: English, Hindi
  • BI Tools: Microsoft Power BI
  • Other Skills: Cloud, Excel, & Jupyter Notebook
  • Interest: Traveling, MixedMartalArt

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Resume

Resume

A Fresher Data Analyst dedicated to transforming data into strategic business value. I bring a solid understanding of data science methodologies, comprehensive training in statistical analysis, and the practical ability to execute projects using tools like Excel, PowerBI, Python (Pandas, NumPy) and relational databases.

Education


2019-2022

Bachelor of Computer Application

United institute of management(fugs)
2025-2027

Master of Computer Application

Amity University

Download CV

Projects

Projects

Below are the sample Data Analytics projects on Excel, SQL, Python, Power BI & ML.

Walmart's Retail insight optmization

Analyzed sales growth, customer segmentation, and product performance using advanced MySQL queries. I identified top-performing branches, profitable product lines, and anomalies in sales transactions to improve strategies. I segmented customers into spending tiers, identified repeat buyers, and analyzed payment preferences by city. I delivered actionable insights through SQL-driven analysis, enhancing decision- making with clear visualizations and reports.

Electric Vehcils Market size analysis

I have analyzed datasets related to electric vehicles EVs, focusing on uncovering meaningful insights and providing actionable recommendations. I filtered EVs based on budget and range criteria, grouped them by manufacturer, and calculated average battery capacities. To identify patterns, I visualized the relationship between battery capacity and range and detected outliers in energy consumption. Additionally, I developed an EV recommendation system in Python that allows users to input their budget, desired range, and battery capacity to find the best matches. Using hypothesis testing, I compared the engine power of Tesla and Audi vehicles, drawing insights that informed strategic recommendations.

Power BI Sales dashboard Project

Transformed and cleaned airline datasets in Power Query, standardizing the data for visualization and analysis. I created DAX measures to analyze passenger bookings, ticket statuses, and flight performance metrics. I built interactive dashboards with compact visuals for flight operations and customer insights. Additionally, I configured relationships, slicers, and drill-throughs, enabling dynamic analysis and real- time reporting via Power BI Service..


KPMG Data Analysis with Excel

Cleaned and standardized customer, transaction, and demographic data, ensuring consistency and accuracy for analysis. I conducted segmentation by wealth, gender, and industry, identifying key customer groups and behavioral trends. I analyzed sales trends, product performance, and customer purchase patterns, providing actionable insights into revenue drivers. Additionally, I estimated potential revenue and calculated customer lifetime value CLV) to highlight high-value customer segments

Smart pricing and retention: Data Driven solutions for Airbnb and Telecom Industry

Predicting Airbnb Listing Prices- Collected and prepared data from Airbnb listings, engineered new features, and implemented regression techniques to develop a predictive model for estimating property prices. Also, utilized python libraries Pandas, NumPy, Scikit-learn) for data manipulation, feature engineering, and model evaluation. Customer Churn Prediction in Telecom Industry- Built a classification model to predict customer churn using machine learning algorithms like Logistic Regression, Decision Trees, and Random Forests. and conducted exploratory data analysis EDA, and evaluated model performance with accuracy, precision, and recall metrics.".

Certification and More projects on LinkedIn

I love to solve business problems & uncover hidden data stories


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Contact

Contact Me

Below are the details to reach out to me!

Address

Kaushambi,Uttar Pradesh India

Contact Number

+91 9696382272

Email Address

mohammadfardeen689@gmail.com

Download Resume

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