Hello I'm
Niloy Biswas
I'm a Data Science Enthusiast
- niloy.swe@gmail.com
- +880 1756 770501
- Nikunja-2, Dhaka, Bangladesh
Hello, I write code that makes the company’s data flow continuous. Which help the decision-makers take more efficient decision in a more convenient way by looking at interactive Dashboards. Also, I find trends and insights from data that helps to improve business critical matrices like reducing the CAC as well as Improving the ROI. And currently, I’m working on 10 minute school as a Business intelligence Analyst. Previously I worked at Sheba Platform Limited as a Data Analyst. I completed my graduation in 2021 in software engineering from Daffodil International University. I completed some extensive research papers related to machine learning and computer vision. I am always eager to learn more. Currently, I'm looking for an opportunity to enhance my research expertise in Data Science field.
Looking into a vast amount of business data and analyze them for finding better business insights. And using Python, MySQL, Google BigQuery for analyze the data.
Analyze Dataset and make prediction about certain unknowns in the future. Forecast about the upcoming trends and help the business owners to make strategic decisions.
Analyze data from multiple databases, identify patterns, and visualize them for stakeholders using tools like Google Data Studio, Google BigQuery, and Metabase.
Image Processing Using Python and OpenCV. Using Deep Learning algorithms like CNN, R-CNN, YOLO, VGG16 etc. Worked on Traffic, Plants Disease, Dental and others dataset.
Tools & Technology used - Python, Google BigQuery, SQL, Google Data Studio, Airbyte
I am an instructor for the Data Analytics Bootcamp, where I teach SQL to aspiring data professionals. My sessions cover a comprehensive range of topics including:
I am dedicated to transforming education and empowering individuals to reach their full potential. My goal is to ensure that participants not only understand the theoretical aspects of SQL but also gain hands-on experience to tackle real-world data challenges.
Looking forward to guiding the next generation of data professionals!
Data Science, Analytics & Insights (DSAI) R&D team.
Tools & Technology used - Python, AWS, SQL, Metabase, Amplitude, Firebase, Play Console, Grafana
Biswas, N. et al. x(2023) “Machine learning-based model to predict heart disease in early stage employing different feature selection techniques,” BioMed Research International, 2023, pp. 1–15.
Available at: https://doi.org/10.1155/2023/6864343.
Safe Saying is a Blog where you can post and read other bloger post. Here we use Machine Learning algorithm which will identify spam comment on our site and delete those. The purpose of this project is to find out the comments of those spam users and keep our social media spam free. In this work, different techniques used for classification of spam comments with those of normal user comments to improve the classification.
Home page of the Blog, Where you nca find all the post.
Personal Profile section.
This is an web based messaging application using PHP and MySQL. We build a Messagnig Application. Where we develop features like - SignUp, Login, Personal Profile Update and Delete, Sending Message to other People, Show Online Status, Add new people as Friend and So on.
Sign up page of the Blog, Create a new account.
Sending Message between two preson..
This is an web based Sorting Algoritgm Visualizar. Used Python for developing the algorithms. then visualize it using HTML and CSS. Implemented the following algorithms -
Visualize how sorting algorithms works.
Heart disease analysis Using different model and visualize. In the dataset we have some patient clinical report, We have to predict they have heart disease or not?
Prediction Results of different algorithms.
The BigQuery dataset titled "Chicago Taxi Trip" comprises a vast collection of data pertaining to taxi rides taken within the city of Chicago, Illinois.
Encompassing a timeframe of January 1, 2013, to April 23, 2023, this dataset encompasses over 200 million taxi trips. It encompasses crucial details
including the pickup and drop-off locations, distances traveled, fares, payment methods, and timestamps.
This dataset is conveniently hosted on Google Cloud's BigQuery platform, empowering users to efficiently query and analyze extensive datasets using SQL.
It is freely accessible and available for utilization across diverse domains such as transportation analysis, urban planning, and machine learning applications.
The project involved creating a dashboard using Looker Studio and Google BigQuery to visualize key summary points and insights from the dataset.
The integration of Looker Studio's data visualization capabilities and Google BigQuery's querying and analysis functionalities enabled the creation of interactive
visualizations, such as charts, graphs, and maps, to provide a comprehensive view of the data. This empowered stakeholders to make data-driven decisions, identify trends,
and derive actionable insights. Overall, the project successfully utilized Looker Studio and Google BigQuery to create a powerful dashboard for efficient data analysis and visualization.
In our day-to-day corporate life, Linkedin became an essential part of communication. We connect with various professionals throughout our career progression.
Hence, Linkedin does not give any built-in visualization for understanding our network.
You can easily Downlaod your Linkedin connection information as CSV and make beautiful visualization from it. Which will help you to find many potential insights.
Monitoring BigQuery usage and costs is an essential aspect of managing data on Google Cloud Platform (GCP). BigQuery is a fully-managed, serverless data warehouse that can scale to
petabyte-levels, making it easy to incur significant costs if you’re not careful. Fortunately, GCP provides several tools and services to help you monitor and optimize your BigQuery
usage and costs. In this post, we’ll explore how to monitor BigQuery usage and costs using the Information Schema JOBS table and Visualize the information in Looker Studio for further Monitoring.
Lets embarked on a journey of visual storytelling.
💡 Discoveries on this global exploration:
Bangladesh - With a population exceeding 160 million, the nation pulsates with vitality. Explore the intricacies of this densely populated haven, each region telling a unique story through the sheer density of its inhabitants.
Dhaka - As the bustling capital of Bangladesh, with a population of more than 44 million people, the city exudes life. Immerse yourself in the complexities of this densely populated metropolis, where each district tells a unique tale due to the sheer density of its residents.
Chittagong Division - Nestled in the scenic landscapes of Bangladesh, with a population exceeding 5.5 million, Chittagong Division resonates with diverse energy. Situated near the breathtaking Cox's Bazar, home to the world's longest sea beach, explore the vibrant tapestry of this densely populated region.
With a population of over 1.3 billion people, India pulsates with dynamic diversity. Explore the complexities of this densely inhabited refuge, where each state tells its own story via the sheer density of its population.
Sri Lanka's with a population over 22 million people, is vividly brought to life in our 3D Population Density Map. The map highlights key areas like Colombo, with its dense urban population, and contrasts them with the more sparsely populated rural regions, offering a comprehensive view of demographic distribution across the island.
Nepal's population, exceeding 29 million, is dynamically illustrated in our 3D Population Density Map. This map accentuates major areas such as Kathmandu, showcasing its densely populated urban landscape, and juxtaposes it with the less populated, remote mountainous and rural areas, providing a detailed perspective on the population spread throughout the country.
Bhutan, with a population of about 750K, is vividly captured in our 3D Population Density Map. The map showcases the stark contrast between densely populated regions and the expansive, sparsely inhabited Himalayan areas, offering a clear and comprehensive view of how the population is distributed across this tranquil mountain nation.
Myanmar, with a population exceeding 54 million, is effectively illustrated in our 3D Population Density Map. This map highlights the dense urban areas contrasted with less populated rural regions, providing an extensive view of the diverse population distribution throughout the country.
Developed an interactive dashboard using Looker Studio to analyze sales, returns, and customer data from Orders, Returns, and Users datasets.
The project focused on key performance indicators such as average monthly sales per region, total profit per customer, product profitability,
return rates, and customer segmentation using Recency, Frequency, and Monetary (RFM) metrics.
By visualizing data through charts and KPIs, the dashboard provides actionable insights into sales performance, customer profitability,
product efficiency, and order fulfillment. This includes identifying top-performing regions and products, analyzing customer purchase patterns,
assessing return rates, and suggesting strategies for targeted marketing and reducing returns.
Developed an interactive CRM Sales Performance Dashboard in Looker Studio to visualize and analyze sales trends over time. The dashboard provided insights into total sales, average sales value, sales orders, and performance by product, sector, and sales manager. Key metrics such as average days to close, orders won, and sector-wise sales distribution were highlighted to optimize sales strategies and improve decision-making for different product lines and managers. The dashboard facilitated tracking sales growth, identifying high-performing sectors, and evaluating manager efficiency.
📊 Explore my latest dashboard for an in-depth analysis of Dhaka's air quality, currently holding the rank as the most polluted city in the globe 🌏.
Historically recognized as one of the most pollution-burdened cities, Dhaka confronts severe environmental hurdles. This detailed analysis brings to
light the severity of the circumstances, with a startling peak AQI of 598 in 2023 😷.
Here are some key takeaways from the data:
▶ The dashboard tracks the AQI from 2020 to the present, highlighting times when air quality reached to 'Very Unhealthy' and 'Hazardous' levels 🔴.
▶ Dhaka endured 133 'Unhealthy' days, a distressing 82 'Very Unhealthy' days, 18 'Hazardous' days, and notably, not a single 'Good' air quality day 🚫🌿, emphasizing the persistent air quality crisis.
▶ A seasonal breakdown shows a winter surge in pollutants and a monsoon phase that offers a brief window of cleaner air🌦️.
▶ The data also sheds light on nighttime pollution peaks, influenced by specific atmospheric conditions and continuous emissions. 🌃💨.
Comparing Dhaka's AQI on a global scale underscores the critical need for immediate action.
