M Mohamed Thanzeer

During my academic journey, I developed a strong foundation in computer applications, including programming languages, data structures, and software engineering. I'm confident that my technical skills, combined with my enthusiasm for data analysis, make me an ideal candidate for this role

BCA graduate with a strong foundation in data and analytics, completed training at SLA Institute, Chennai. Skilled in SQL, Python, and Power BI with hands-on project experience. Capable of performing data cleaning, analysis, and creating insightful dashboards. A fast learner and analytical thinker, eager to solve real-world problems using data.

Python for Data Analysis & Prediction Data Cleaning with Pandas: Utilized Pandas to clean and preprocess data by handling missing values, removing duplicates, and correcting inconsistencies with functions like .dropna(), .fillna(), and .replace().

Statistical Analysis: Performed statistical analysis using NumPy and Pandas, calculating key metrics such as mean, median, mode, and standard deviation to analyze trends and summarize data.

Data Visualization with Matplotlib: Created a variety of visualizations, including line charts, bar graphs, and pie charts, while customizing plots with labels, colors, titles, and legends for better data interpretation.

Pandas for Data Manipulation: Leveraged DataFrames and Series for efficient data structuring and applied advanced operations like filtering, grouping, and merging to manipulate datasets effectively.

SQL for Data Management & Processing DDL/DML: Created and modified tables (DDL), and managed data (DML) with INSERT, UPDATE, DELETE.

Joins & Keys: Used joins to combine tables, and primary/foreign keys for data integrity.

Normalization: Applied 1NF, 2NF, 3NF to optimize database structure.

Stored Procedures & Triggers: Created stored procedures, used cursors for row operations, and set up triggers for automatic actions. Power BI for Business Intelligence & Reporting Power Query: Used Power Query for data extraction, cleaning, and transformation to prepare datasets for analysis.

DAX: Applied DAX formulas for calculated columns, measures, and aggregations to enhance data analysis and insights.

Data Transformation: Performed data shaping, merging, and pivoting to ensure data accuracy and readiness.

Forecasting & Trend Analysis: Implemented forecasting models and trend analysis to predict future data patterns and provide actionable insights.