Anirudha Chowdhury

Data Analyst | Power BI & Python Developer

About Me

Hi! I'm Anirudha Chowdhury and i am Data Analyst and a Power Bi & Python DeveloperI am a dynamic individual specializing in programming and data analytics, with expertise in tools like Python,SQL, Power Bi and TableauMy experience spans developing interactive dashboards, web applications, and conducting insightful data analyses to drive strategic decisions.I stay updated on emerging technologies and thrive on solving complex challenges.

Skills

Python | R | SQL | Tableau | PowerBI | Excel

  • Data Visualization 3+ Projects

  • Data Analytics 2+ Projects

  • Report Making 2+ Projects

  • Software Development 2+ Projects

Featured Projects

SQL | PowerBI | Excel | Sharepoint Walmart Sales Analysis

Created a multi page Power BI Dashboard to pull and visualize various region and product metrics

SQL | Tableau | Excel Japanese Provincial Housing Market analysis

Created a detailed Tableau Story Board to display visualizations and analysis

Professional Certifications

My growing list of proprietary Certifications

Google Data Analytics Certificate | Issued 2024

Thank You

Thank You for taking the time to visit my Portfolio.If you'd like to chat about me joining your team, feel free to email me using the Email below.[email protected]

SQL | PowerBI | Excel | Sharepoint Walmart Sales Analysis

First Page

In 2022 , I created a multipage Power BI Dashboard to analyze the walmart companies various region and product metrics to make better business decisions and increase efficiency

Research Questions, I was interested in answering :

  1. Which was the best selling product category during various seasons ?

  2. Which was the best selling region and state filtered by various time periods ?

  3. Which region had the highest average order values to decide which product types should be focused on for marketing ?

  4. Which product is the highest selling in a region to make accurate inventory decisions for effiecient operations in each region ?

  5. Which product segment and category is the highest revenue earner to decide supplier orders and customer marketing ?

My Analysis Workflow :

  1. Pulling the Data from Kaggle, cleaning it using excel and power BI transform, and preparing it for analysis.

  2. Creating Measures, filters and additional columns for Dataset Analysis

  3. Creating Visualizations from measures and columns, then placing them in the dashboard

  4. Creating various KPI's using DAX and placing them around in the dashboard to transmit key information at appropriate points.

  5. Placing slicers to filter all Dashboard visualizations and KPI's, making sure all tables are linked so all pages are filtered at the same time.

  6. Formating the Dashboard and creating an overall theme.

Second Page

Key Takeaways :

  1. The best selling product category in every region every holiday season was technology being the most ordered for a period of 5 years.

  2. The total sales has been increasing steadily every year, Showing a overall healthy company.

  3. There is a positive trend towards opting for first class shipping as more percentage of customers have been opting for it showing a customer base ready for more premium products and services.

  4. The southern region has the highest average order value consistently.

The takeaways allows the company to make better inventory decisions to make sure demand does not outrun supply of the correct product at the correct place. Furthermore, this helps in efficiently utilize marketing budgets to plan the best and most accurate campaigns to market the right products to the right customer base in the correct region.

Third Page

The Dashboard Changes dynamically according to the various filters selected via the slicers :

See it change Dynamically according to the filters

Thank You

Thank you for taking the time to check out this project.

SQL | Tableau | Excel Japanese Provincial Housing Market analysis

A Visualization comparing the Average trade price between the japanese province of Nigata and Toyama with various Key points stated pertaining to the analysis.

Created a 12 page Tableau story board with various visualizations and key points added to each visualization to give the user a detailed explanation of what each chart means comparing various Japanese provinces and analysing how the 2008 housing crisis affected each province. Using data from over a period of 13 years from 2006 upto 2019.

This Research Storyboard answers the following questions :

  1. How Did the 2008 Financial Crisis affect the Japanese Housing market ?

  2. Which Japanese provinces had recovered the most, which were still still recovering, which had exceeded precious values and which were performing worse than before the event ?

My Research Storyboard workflow steps :

  1. I first acquired my Dataset using kaggle. The Data i used for my analysis consisted of 47 CSV files where each file represented one of the Japanese provinces.

  2. Each CSV provided information about various properties in a particular province, this included information like the trade price, area, property type, year built and various other information related to each property in that particular province.

One of the CSV Files showing the data for properties in the Toyama Province

3. After i acquired the data and selected what i needed for my analysis. I cleaned the data removing any null values, empty fields, formating the datasets and removing the data i did not require for my analysis.4. I then imported all the required data into Tableau for my analysis and visualizations.

This is the Data source information window for one of the analysis's comparing the average trade price between the provinces of Tokyo and Kanagava. This window shows which files are connected to this visualization and its various attributes.

5. After i acquired the data and selected what i needed for my analysis. I cleaned the data removing any null values, empty fields, formating the datasets and removing the data i did not require for my analysis.6. I then imported all the required data into Tableau for my analysis and visualizations.

A snippet of data values from the Toyama Province after the cleaning procedure has been finished.

7. After importing the data into Tableau, i created various visualizations comparing the various provinces. I mostly used line charts for most of my visualizations, using a bar and line chart for only one visualization

The Bar and Line Chart i created comparing the trade price per year for the Fukushima and Ibaraki Province. In the Visualization, the Ibaraki province is represented by the bar chart and the Fukushima province is represented by the line chart.

A Line Chart comparing the unit price for the Chiba Province and the Saitama Province over the years.

Another Line Chart comparing the Trade Values of the Tokyo province and the Kanagawa Province over the years.

8.After creating the various Visualizations, i created a storyboard using the various visualizations and provided in depth insights for each of the visualizations for a detailed explanation.

The Title page for the Storyboard introducing the research question

One of the story pages in the StoryBoard, showcasing a line chart comparing the trade value between the Hokkaido and the Aomori Province.

Another one of the story pages in the Storyboard, showcasing a line chart comparing the Trade value between Gunma and Togichi Province over the years.

The Conclusion page of the storyboard presenting all of our findings in a consolidated manner for the shareholders to refer to in future easily.

A link to Download a copy of the complete Tableau file in available below.

Thank You

Thank you for taking the time to check out this project.

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