Freelance Data Analytics in New Delhi, Mayur vihar phase 3

Core Skills & Expertise Data Analysis & Interpretation – Extracting actionable insights from complex datasets. SQL & Database Management – Efficiently handling data storage, retrieval, and query optimization. Data Visualization – Presenting insights using Power BI, Tableau, and Excel Dashboards. Machine Learning & Predictive Modeling – Utilizing statistical techniques for forecasting trends. Business Intelligence (BI) Tools – Crafting interactive reports for stakeholder decision-making. Over the years, I have worked on multiple projects related to customer behavior analysis, financial modeling, trend forecasting, and process automation. By leveraging historical data, I assist businesses in improving their marketing strategies, operational efficiency, and overall profitability. Technical Proficiency SQL, Excel, Power BI, Tableau ETL Processes, Data Warehousing, Big Data Analysis Statistical Modeling, Regression Analysis, Time Series Forecasting

Motivated and detail-oriented Computer Science and Engineering graduate with hands-on experience in data analysis, Power BI dashboard development, Python, and Excel. Skilled in typing, data entry, data management, and maintaining accurate records with strong attention to detail. Proficient in transforming raw data into meaningful insights through interactive dashboards and reports. Strong communication, organizational, and problem-solving abilities, with a keen interest in analytical and management roles. A quick learner who adapts well to new technologies and is committed to delivering accurate, high-quality work both independently and as part of a team.

Higher Qualification: Master's Degree in Statistics and Computer Science from Pondicherry Central University with an aggregate of 87% Work Experience: Role: Data Analyst Company: Artificial Penetration Software Solutions, Hyderabad Duration: January 2019 – December 2021 Conduct Data Mining, Data Modelling, Statistical Analysis, Business Intelligence gathering, trending, and Forecasting. Data analytics supports decisions for high-priority, enterprise initiatives involving IT/product development, customer service improvement, organizational realignment, and process reengineering. Used quantitative data gathered to develop an understanding of customer behaviour, demographics, and lifecycle. Presented data that helped guide decisions of the Organization. Perform qualitative and quantitative analysis to support day-to-day decision making and support reporting and analytics, such as KPIs, Financial reports, and create interactive dashboards for better analysis using Power BI or Tableau Used Machine Learning and Statistical Modelling techniques to develop and evaluate algorithms to improve performance, quality, data management, accuracy and making predictions Project : Title: Magic Straw Restaurant Sales Analysis and Revenue Prediction Role: Data Analyst Technologies: Excel, Power BI, Python, Machine Learning Description: The Basic idea of Magic Straw restaurants analysis is to get a fair idea about the factors affecting the establishment of different types of restaurants at different places in Hyderabad and aggregate rating and Revenue of each restaurant. This dataset contains details (including ingredients), images and reviews of 241 ice cream flavours & COMBO thick shakes across the city. There are a total of 21,674 reviews, with each review containing star ratings and text. Responsibilities / Accomplishments: • Data Mining and Modelling: Collected, cleansed, and provided modelling and analyses of structured and • unstructured data used for major business initiatives. Outcomes: — A 15% reduction in transportation costs, resulting in annual savings. — Improved demand forecasting that reduced backorders to retail partners by 17%. — Completed Revenue Analysis and BI research that helped boost NW region sales by 15%. Dashboards: Created visually impactful reports /dashboards in Power BI for data reporting and data analysis by using pivot tables and VLOOKUP. Extracted, interpreted, and analysed data to identify key metrics and transform raw data into meaningful, actionable information. eCommerce: Designed and built Machine Learning analysis models on large data sets that helped increase online sales (up to 15% per product) and lowered the Orders-Cancellation rate by 23%.

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