Portfolio - Akshita Agarwal | Codebasics

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Akshita Agarwal

Data Analyst

EMAIL

[email protected]

PHONE

+91 7014689744

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About Me

As an aspiring data analyst with a deep-seated passion for uncovering insights that propel business success, I am Proficient in transforming raw data into clear, actionable strategies through advanced analytics and visualization techniques. I am skilled in identifying trends and patterns that drive innovation and optimize performance. Looking forward to contribute analytical expertise in a dynamic environment focused on achieving impactful results and continuous growth.

1

Excel Project

2

PowerBI Projects

1

Python Project

Key Skills

SQL

Data analyst

problem solving

data visualization

python

My Projects

My Experience

AtliQ Technologies - Data Analyst Intern(August 2024 – September 2024)

Developed reports and dashboards in Power BI; streamlined and analyzed large insurance datasets, improved project efficiency by 25%.

Extracted and queried data using SQL, delivering actionable insights that increased efficiency by 20%.

Accenture – Application Development Associate ( Jan 2021-Sep2021)

Developed and maintained data warehouse solutions customized to project requirements, encompassing design, implementation, and optimization of data models, ETL processes. Enhanced data flows to streamline information delivery, reducing data load times by 20% and improving overall system reliability. Collaborated with cross-functional teams to gather requirements and troubleshoot issues; ensured seamless system integration and enhanced data flow accuracy by 30% within the SAP BW environment. Optimized data models and ETL processes; improved data integration efficiency by 40% and reduced data retrieval time by 25%

Awards & Certificate

Excel: Mother of Business Intelligence

Get Job Ready: Power BI Data Analytics for All Levels 3.0

SQL Beginner to Advanced For Data Professionals

Microsoft Fabric Mini: For Data Analysts

Tata Group - Data Visualisation: Empowering Business with Effective Insights Job Simulation

Tata Group - Data Visualisation: Empowering Business with Effective Insights Job Simulation

Let's Work Together!

Feel free to get in touch with me. I am always open to discussing new projects, creative ideas or opportunities to be part of your visions.

EMAIL

[email protected]

Call Me

+91 7014689744

LinkedIn

https://www.linkedin.com/in/akshita-agrawal-60038717a/

GitHub

https://github.com/akshita9898

Excel Project

Excel-Sales and Finance Analytics

Domain/Function: Sales and finance

This project on P&L analysis for AtliQ Hardware FY 2022 focusses on optimizing finance and sales processes, employing Excel's advanced tools such as Pivot Tables and DAX with automated data cleaning using Power Query resulting in a 30% increase in report accuracy.

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Engagement

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on GitHUB

Excel-Sales and Finance Analytics

Customer Performance Report: Provides net sales performance of each customer across years

Excel-Sales and Finance Analytics

Market performance vs Target report across years: Enables stakeholders to assess the country's performance and compare it to set targets.

Project Details

In this project I have created an in-depth P&L analysis using Excel's advanced tools such as Pivot Tables and DAX ,delivered critical insights into revenue and expense categories, enhancing financial decision-making. I automated data cleaning using Power Query, which can rescue around 4 hrs. of repeated manual work. I deployed DAX-based calculation models, resulting in a 30% increase in report accuracy and providing users with more nuanced, flexible data insights.
Overall, this project aims to empower businesses with the necessary tools and insights to monitor, evaluate, and enhance their sales activities. By leveraging sales data analysis, businesses can unlock new opportunities, drive growth, and stay competitive in the dynamic market landscape.

PowerBI Project

Business Insights 360 Dashboard for Finance, Sales, Marketing and Supply Chain

Domain/Function: Business domain

AtliQ Hardwares is a consumer electronics company expanding rapidly but is not able to compete with other companies using data as most of their report still exists in Excel. My goal is to implement an advanced analytics solution using Power BI that will enable the company to get meaningful insights.

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Engagement

Business Insights 360 Dashboard for Finance, Sales, Marketing and Supply Chain

User-empathetic landing page as a part of dashboard design

Business Insights 360 Dashboard for Finance, Sales, Marketing and Supply Chain

Data modelling involving 10+ tables

Project Details

In this project, the goal is to be one report which could be used by stakeholders from sales, marketing, finance and executive team.
I learned the following things in this project-
1. Power Query (Basic and Advanced Operations)
2. Basic and complex DAX formulas
3. Data modelling involving 10+ tables
4. Choosing the right visuals and formatting
5. Dashboard designing principles
6. Using bookmarks
7. Deploying in Power BI service
8. Stakeholder Feedback Implementation
9. Sales, Marketing, Finance & Supply Chain metrics
Through robust ETL (Extract, Transform, Load) processes, I harmonized disparate data sets into a unified data model. I've used visualization techniques such as drill-downs, filters, and slicers which enhance usability and decision-making capabilities.

Live Interactive Dashboard

PowerBI Project

Blinkit Grocery - Sales and finance analysis

Domain/Function: Sales and finance

This project involves analyzing Blinkit’s(formerly known as Grofers) grocery data, which provided a comprehensive view of various business metrics and trends. Leveraged Power BI to create dynamic, interactive dashboards. Extracted data from excel and used Dax query to create various measures .

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Engagement

Blinkit Grocery - Sales and finance analysis

Dashboard showing various visualizations as per data provided.

Blinkit Grocery - Sales and finance analysis

Raw data later passed through ETL(Extract-Transform-Load)for making visualization for better analysis

Project Details

In this analysis, I concentrated on several key areas:
---> Examined sales performance across different outlet types to identify which outlets are performing the best and also year wise which was best as per revenue generated.
---> Analyzed sales data by region to understand geographical trends and variations.
---> Investigated the different types of markets (supermarket adn grocery) and their sales contributions.
---> Evaluated the types of items sold, with a particular focus on fat content to understand customer preferences and health trends.
---> Correlated average customer ratings with sales data to identify the impact of ratings on sales performance.
Visualizes total sales segmented by outlet type, items sold and in all tier 1, tier 2 and tier 3 states helping to pinpoint which types of outlets are generating the highest revenue. Showcases average sales figures ,Customer ratings, Item Distribution Analysis.

Live Interactive Dashboard

Python Project

Atliq Grands Hospitality Project

Domain/Function: Hospitaity

Tools Used- Python, Pandas, Seaborn, SQL, Power BI, Jupyter Notebook Analyzed 5 datasets (500K+ records) from Hyderabad, Bangalore, Delhi, and Mumbai, identifying a 12% YoY revenue decline and a 15% drop in occupancy rates. Used Pandas & SQL for data processing, Seaborn & Power B

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Engagement

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on GitHUB

Atliq Grands Hospitality Project

Atliq Grands Homepage

Atliq Grands Hospitality Project

Data-driven strategies to help Atliq Grands regain its competitive edge and accelerate growth in the hospitality

Project Details

Atliq Grands, a prominent hotel chain across Hyderabad, Bangalore, Delhi, and Mumbai, is facing challenges with declining revenue and market share. To reverse this trend, the business manager is seeking a Data Analyst to drive data-oriented management decisions.
In my project, I used Jupyter Notebook to process data from 5 different files, enabling a deep dive into the company's performance.
📊 Data Analysis with Pandas:
Loaded data using pd.read_csv() from multiple sources
Cleaned and transformed data with methods like dropna(), fillna(), and apply()
Identified unique values with unique() and grouped data using groupby()
Merged and joined datasets using merge() and concat()
📈 Data Visualization with Seaborn:
Created simple visualizations to highlight key trends and patterns using seaborn.heatmap(), seaborn.boxplot(), and seaborn.barplot()