Excel
Data Analysis & Dashboards
Hello, I'm
Lincon
Data Analyst
Hello, I'm
Data Analyst
Get To Know More
1+ years
Data Science and Analysis
B.Tech in Computer Science and Engineering
Higher Secondary Education (CBSE)
Secondary Education (ICSE)
Hi, I'm Lincon Dash, a passionate Data Analyst with a keen interest in sceince of data and a strong aptitude for problem-solving. I thrive on turning complex data into actionable insights that drive decision-making and business growth. My ability to dissect challenging problems and finding innovative solutions sets me apart, and I'm always eager to apply my skills to real-world projects. I'm constantly learning and growing in the field, and I'm excited to showcase my work and the impact it can make. Let's connect together and make efforts to contribute to our vast society of data.
Explore My
Data Analysis & Dashboards
Data Analysis & Storage
Pandas, Numpy, Matplotlib, etc.
Power BI , Tableau, Looker, etc.
Sklearn, Keras
Pytorch, Tensorflow
Flask, Django, Streamlit, etc.
C, C++, Python
Git & Github
GCP, AWS, etc.
Data Structures and Algorithms
Statistics, probability, calculus, algebra, etc.
Browse My Recent
Recently completed analyzing global layoffs using SQL, where I delved into extensive datasets to uncover trends and insights related to layoffs across different industries and regions. This helped me in gaining actionable insights, all through the power of SQL.
This project leverages advanced Excel features, pivot tables, and charts to create an interactive experience that allows users to explore and analyze various aspects of the Olympic Games (1896-2020), including medal counts, country performance, and athlete statistics.
This project showcases a comprehensive use of Power BI features, including DAX functions, to analyze loan data. Although the data is dummy, the report is structured around Bank of America, making it a realistic and professional demonstration of bank reports.
Browse My Recent
Movie Recommender System that uses content-based filtering to suggest movies based on previously watched movie. For this project, libraries like sklearn, pandas, and numpy were used. The system was deployed using Streamlit, allowing users to interact with the recommendations through a user-friendly web interface.
It utilized libraries like requests and bs4 to extract job data such as job titles, posting dates, industries, etc. The scraped data is cleaned then saved in CSV format within a data folder. This project automates the process of gathering job listings, making it easier to analyze and keep track of the latest opportunities.
Let's Connect Together
