About
Data Analyst with a strong background in collecting, cleaning, and analyzing data to generate meaningful insights for business growth. Proficient in SQL for data extraction and Python (Pandas, NumPy) for complex data analysis. Experienced in performing exploratory data analysis (EDA) and creating interactive dashboards and visualizations using Power BI and Google Sheets. Proven ability to translate business requirements into accurate recommendations and support stakeholders in 100% remote work environments.
Skills & Expertise (6)
Work Experience
Data analyst intern
labmantix
7-2025 - Present
Monitored and reported on data quality by validating datasets from multiple sources to ensure high accuracy for internal teams. Collaborated extensively with cross-functional teams to design automated reporting systems that support business intelligence. Scripted SQL and Python routines to automate ETL processes and complex data manipulation. Successfully reduced data preparation time for machine learning workflows by 20% through automation efforts. Translated technical methodologies into clear business insights, utilizing detailed video reports for non-technical stakeholders. Maintained data integrity by implementing rigorous validation checks across various data pipelines. Supported analytics initiatives by designing systems that streamlined data flow and accessibility
Data Analyst Intern
Labmantix
7-2025 - 1-2026
Collect, clean, and analyze data from multiple sources to execute 11 industry-standard projects, ensuring data accuracy for internal teams. Create dashboards and visual reports using Power BI and Streamlit to monitor operational KPIs and support stakeholders with insight-driven analysis. Script SQL and Python routines to automate ETL processes and data manipulation, reducing preparation time for machine learning workflows by 20%. Translate technical methodologies into clear business insights through detailed video reports for non-technical stakeholders.
Data Scientist Intern| data analyst intern
Saiket System
3-2025 - 6-2026
Executed a focused, project-based internship, delivering complex data science solutions within a strict 7-12 day timeline. Performed cohort analysis on over 7,000 customer records based on tenure and contract type. Identified high-risk churn groups through rigorous data examination and segmentation. Applied advanced statistical analysis and data modeling techniques, specifically utilizing XGBoost and Random Forest algorithms. Benchmarked predictive models, achieving a documented 75.8% accuracy rate. Segmented the user base to generate data-driven insights that directly supported customer retention strategies. Delivered actionable recommendations based on modeling results to optimize contract type offerings Apr 2025 – May 2025 Completed a project-based engagement, successfully delivering on all objectives within a 7-12 day timeframe. Maintained strong attention to detail by scrubbing and standardizing raw data to ensure quality. Achieved 100% accuracy in final reporting by implementing strict data cleaning protocols. Analyzed 9,551 rows of consumer data to identify specific preferences in the restaurant industry. Identified operational trends within the dataset to provide business-relevant context. Managed multiple tasks simultaneously, successfully completing 11 distinct data cleaning and visualization objectives. Produced visualizations that highlighted key consumer insights derived from the cleaned datasets. Excelerate Global | Data Analyst Intern (Internship/Contract) Mar 2025 – Apr 2025 Team Leadership: Served as the Team Leader for a cohort of 4 interns, coordinating daily tasks and workflows. Management Reporting: Acted as the primary point of contact, reporting project progress and insights directly to the reporting manager. Evaluated critical KPIs such as Cost Per Click (CPC), Click-Through Rate (CTR), and ROI. Identified that 20% of campaigns were underperforming and recommended budget reallocation to optimize spend. Utilized advanced Excel and Google Sheets functions to process over 150 data points. Visualized marketing trends across various channels to provide clear strategic direction. Managed strict project timelines to ensure 100% on-time delivery of the final project deliverables.
Education
BE
2019 - 2022 · India
Bachelor of Engineering (B.E.)
2019 - 2022 · India
Certifications
IBM data sciense professional ceritificate
IBM · 2025
Python: The most popular language for data science. You will learn it from scratch. Data Science Methodology: How to approach a problem like a scientist (Business Understanding -> Data Understanding -> Modeling -> Evaluation). SQL: Querying databases using Python. Machine Learning: Intro to regression, classification, and clustering (using Scikit-Learn). Tools: You get hands-on experience with Jupyter Notebooks and the IBM Watson Studio cloud platform. Curriculum Structure (10 Courses): What is Data Science? Tools for Data Science Data Science Methodology Python for Data Science, AI & Development Python Project for Data Science Databases and SQL for Data Science with Python Data Analysis with Python Data Visualization with Python Machine Learning with Python Applied Data Science Capstone
Google Data Analytics Professional Certificate
Google · 2025
Spreadsheets: Advanced Excel/Google Sheets functions for data cleaning and initial analysis. SQL: You will learn to write queries to pull data from big databases (BigQuery). Data Visualization: Extensive training on Tableau to build dashboards. R Programming: You will learn the R language for statistical analysis (instead of Python). Capstone Project: The final course requires you to complete a case study which you can put on your portfolio. Curriculum Structure (8 Courses): Foundations: Data, Data, Everywhere Ask Questions to Make Data-Driven Decisions Prepare Data for Exploration Process Data from Dirty to Clean Analyze Data to Answer Questions Share Data Through the Art of Visualization Data Analysis with R Programming Google Data Analytics Capstone: Complete a Case Study
data analyst
PW skill · 2024
Month 1: The Foundations (Excel & Python) You start with the basics to build a strong logical foundation. MS Excel: Focus: Mastering data manipulation without code. Topics: Advanced formulas (XLOOKUP, INDEX-MATCH), Pivot Tables, Data Validation, and Conditional Formatting. Python Programming (Basics to Intermediate): Focus: Learning the language of Data Science. Topics: Variables, Loops, Functions, Data Structures (Lists, Dictionaries, Sets), and Exception Handling. Key Skill Unlocked: You can write scripts to automate basic tasks and manipulate spreadsheets like a pro. Month 2: Mathematics & Databases (SQL) Now you learn how to talk to databases and the math behind the "magic" of AI. Statistics (Descriptive & Inferential): Focus: Understanding data distribution and making predictions. Topics: Mean/Median/Mode, Standard Deviation, Probability Distributions (Normal, Binomial), Hypothesis Testing (p-value, t-test). SQL (Structured Query Language): Focus: Pulling data from relational databases. Topics: SELECT, GROUP BY, JOINS (Inner, Left, Right), Window Functions, and Subqueries. Key Skill Unlocked: You can extract data from company databases and statistically validate your findings. Month 3: Data Analysis & NoSQL (Pandas + MongoDB) This month bridges the gap between raw data and insights. Python for Data Analysis: Libraries: NumPy (Math) and Pandas (Data Manipulation). Topics: Cleaning dirty data, handling missing values, and reshaping datasets (Data Wrangling). MongoDB (NoSQL Database): Focus: Handling unstructured data (like JSON documents) which is common in modern web apps. Topics: CRUD operations (Create, Read, Update, Delete) and aggregating data in MongoDB Atlas. Key Skill Unlocked: You can clean messy real-world data and store/retrieve it from modern non-relational databases. Month 4: Visualization & Storytelling (Power BI + Matplotlib) You learn to present your findings to stakeholders. Data Visualization (Python): Libraries: Matplotlib and Seaborn. Topics: Creating scatter plots, histograms, and heatmaps to find patterns in data. Power BI: Focus: Business Intelligence Dashboarding. Topics: Connecting to data sources, DAX (Data Analysis Expressions), and building interactive dashboards for business reports. Key Skill Unlocked: You can turn complex code results into beautiful, interactive charts that managers can understand. Month 5: Machine Learning (The Core) This is the heart of the course where you build predictive models. Supervised Learning: Topics: Linear Regression (Predicting prices), Logistic Regression (Classification), Decision Trees, and Random Forests. Unsupervised Learning: Topics: K-Means Clustering (Customer segmentation). Feature Engineering: How to select the right variables to make your model smarter. Key Skill Unlocked: You can predict future trends (like sales or stock prices) based on past data. Month 6: Deep Learning & Generative AI You move to advanced AI topics (neural networks). Deep Learning: Frameworks: TensorFlow or PyTorch. Topics: Artificial Neural Networks (ANN) and CNNs (for Image Recognition). NLP (Natural Language Processing): Processing text data (Sentiment Analysis). Generative AI: Introduction to LLMs (Large Language Models) and how tools like ChatGPT work. Key Skill Unlocked: You can build AI that recognizes images or understands human language. Month 7: Deployment & Cloud (AWS) A model is useless if it stays on your laptop. You learn to put it on the internet. AWS (Amazon Web Services): Focus: Cloud Computing for Data Science. Topics: EC2 (Virtual Servers), S3 (Storage), and Elastic Beanstalk (Deploying apps). Deployment: Tools: Flask or Streamlit (to create a web interface for your model). Action: Taking your Machine Learning model and hosting it on AWS so anyone can use it via a link. Key Skill Unlocked: You become an "End-to-End" Data Scientist who can build, visualize, and deploy a full application.
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