Highly motivated and analytical professional ready to embark on an exciting career as a data analyst

ANKIT RAJ MISHRA  • Peerlist

Data Analytics professional at Schenider Electric having 2+ years of experience with Excel,SQL, Python,Tableau

Data Analyst, , SQL , Python , Power Bi, Microsoft Excel

This Pizza Place Sales Analysis project focuses on identifying key factors behind a decline in sales and offering actionable, data-driven strategies to improve performance and customer satisfaction. By analyzing sales data from January to December 2015, we uncovered crucial insights into customer preferences, peak ordering times, popular pizza choices, and pricing trends.The project explores the restaurant's operational metrics using Excel functions like Pivot Tables, COUNT, and AVERAGE, helping the business identify underperforming menu items, optimize promotions, and adjust pricing strategies. Our analysis revealed key insights, such as peak sales periods, customer behavior, and the impact of promotions on sales. Based on these findings, we proposed solutions like weekday deals, seasonal pizza offerings, and improved staff training and loyalty programs to drive customer engagement and boost sales.The 5Ps framework—Product, Pricing, Promotion, Place, and People—guided our recommendations, ensuring a well-rounded approach to revamping the restaurant’s operations and driving sustainable growth.

Pizza Place Sales Analysis

You are a data analyst at Airtel, a leading telecommunications company. Recently, the company has noticed an increase in the number of customers discontinuing their services. This trend is alarming to the management as it could significantly impact the company's revenue and market share. Your task is to dive deep into the customer data to identify patterns and insights that could help understand the factors leading to customer churn. Your analysis will play a crucial role in developing strategies to improve customer retention.

Telecommunications ChurnQuest

Data Visualizer| Tableau|SQL|EXCEL| Data Analysis|PowerBI

Case Study of Swiggy Work insight of Swiggy app in PPT

Swiggy  Case study Document and PPT

Intermediate Joiner
Data Analytics

You have been provided with a dataset containing information about various Indian food cuisines. The dataset includes the dish's name, diet type, course name, description, cuisine name, ratings, similar dishes, ingredients, preparation time, cooking time, total time, servings, and recipe instructions. The goal of this case study is to clean and analyze the data to draw meaningful insights that can help improve data quality and support better decision-making.Objective :<ol><li>Data Cleaning: Apply best practices for data cleaning to ensure the dataset is accurate, consistent, and usable.</li><li>Data Analysis: Conduct exploratory data analysis (EDA) to uncover trends and patterns in the data.</li><li>Reporting: Prepare a report summarizing your findings, methodologies, and recommendations for future data collection.</li></ol>

Indian Cuisine Food Case Study

<h3 class="rich-heading">Background:</h3>You are a data analyst at Amazon, which has an extensive network of warehouses across various regions. The company is facing challenges in ensuring efficient warehouse operations, resulting in frequent stockouts, delays in deliveries, and increased operational costs. The management has tasked you with analyzing the data from these warehouses to identify key issues and provide actionable insights to enhance warehouse efficiency.Problem StatementProblem Statement: Amazon is experiencing inefficiencies in its warehouse operations. These inefficiencies are affecting the supply chain, leading to stockouts, delivery delays, and increased costs. Your task is to analyze warehouse data to uncover the root causes of these issues and propose data-driven solutions to optimize warehouse performance.Why is it Important to Solve? Addressing these inefficiencies is crucial for improving customer satisfaction, reducing operational costs, and ensuring timely delivery of products. Efficient warehouse operations are critical for maintaining a competitive edge in the retail market.

FMCG Dashboard

<h4 class="rich-heading">Background</h4>You have been provided with a dataset containing information about various Indian food cuisines. The dataset includes the dish's name, diet type, course name, description, cuisine name, ratings, similar dishes, ingredients, preparation time, cooking time, total time, servings, and recipe instructions. The goal of this case study is to clean and analyze the data to draw meaningful insights that can help improve data quality and support better decision-making.

Best Practices for Data Cleaning in Data Analytics

<h3 class="rich-heading">Background:</h3>Adecco India is a medium-sized technology company specializing in software development. It has a diverse workforce spread across various departments, including engineering, marketing, sales, and customer support. Recently, Adecco India has observed an increase in employee turnover, especially among junior-level employees, which is concerning for the management. The company is committed to understanding the factors influencing employee attrition and job satisfaction to develop strategies for improving employee engagement and retention.Scenario:Adecco India is facing a high attrition rate, particularly in the sales department among junior-level employees. The management is worried about the potential impact on productivity and the costs associated with hiring and training new employees. As a data analyst, you are tasked with analyzing the HR data to uncover insights that can help in reducing the attrition rate. Your analysis will inform strategic decisions aimed at enhancing employee satisfaction and retention.

HR Analytics

Scenario: You are a data analyst at a leading restaurant review aggregator company, similar to Zomato. The company has collected extensive data on restaurants across various cities worldwide, aiming to enhance customer experience and support restaurants in improving their services. The data contains information on restaurant names, locations, cuisines offered, ratings, price ranges, and more.In recent months, the company has noticed a decline in user engagement and feedback on its platform, potentially threatening its position in the market. The executive team believes that by analyzing the restaurant data, the company can uncover insights to tailor the user experience more personally, promote high-quality restaurants, and possibly introduce a new feature that could revitalize user interest and participation.Problem StatementThe challenge is to analyze the dataset to identify trends, preferences, and areas of improvement that can enhance the overall user experience on the platform. Specifically, the goal is to understand how different factors such as cuisine, location, price range, and ratings influence user choices and satisfaction.

Zomato_Dashboard

Airbnb data for 250,000+ listings in 10 major cities, including information about hosts, pricing, location, and room type, along with over 5 million historical reviews.NOTE: Prices are in local currency<h4 class="rich-heading">Recommended Analysis</h4><ol><li>Can you spot any major differences in the Airbnb market between cities?</li><li>Which attributes have the biggest influence on price?</li><li>Are you able to identify any trends or seasonality in the review data?</li><li>Which city offers a better value for travel?</li></ol>

Airbnb Listings & Reviews Using Excel, Power BI & Tableau

<h3 class="rich-heading">Background:</h3>In this case study, trainees will explore a dataset that captures unemployment rates across different countries from 1991 to 2021. The dataset provides an excellent opportunity to analyze global economic trends, understand regional disparities, and assess the impact of major global events on employment.<h3 class="rich-heading">Problem Statement</h3>What is the problem?Analyze the global unemployment trends over three decades to identify patterns, anomalies, and the impact of major economic events (like the 2008 financial crisis and the 2020 COVID-19 pandemic).Why is it important to solve it?Understanding unemployment trends is crucial for policymakers, businesses, and international organizations to devise strategies for economic growth, workforce development, and crisis management.

Unemployment Trends Analysis

Introduction:The US Honey Production dataset provides an in-depth look at the honey production industry across various states from 1995 to 2020. This case study is designed for data science trainees with basic knowledge of Excel and data visualization tools, challenging them to analyze the data to build analytical and logical thinking skills.What is the problem?The honey production industry faces challenges such as declining bee populations, changing environmental conditions, and fluctuating market demand. Understanding trends, state-wise production capacities, and economic impacts is crucial for stakeholders to make informed decisions.Why is it important to solve it?Solving this problem can help in strategizing for sustainable honey production, improving the livelihood of beekeepers, and ensuring environmental balance.

US Honey Production Analysis Using Excel

<ul><li>What is the problem?</li></ul>Identifying the characteristics of successful Netflix original movies based on different attributes such as genre, runtime, IMDB score, and language.<ul><li>Why is it important to be solved?</li></ul>Understanding the features of successful movies can help Netflix in planning and producing more popular and well-received content, thereby attracting a larger audience and increasing its market share in the OTT platform industry.<ul><li>Data Dictionary</li></ul><ol><li>Title: String - The name of the Netflix original movie.</li><li>Genre: String - The category defining the thematic elements of the movie.</li><li>Premiere: Date - The release date of the movie on Netflix.</li><li>Runtime: Integer - The length of the movie in minutes.</li><li>IMDB Score: Float - The rating given to the movie on the IMDB platform.</li><li>Language: String - The language(s) in which the movie is available.</li></ol>

Success Code of Netflix Original Movies Using Power BI

<ul><li>What is the problem?</li></ul>How can a bank increase the success rate of its term deposit subscription through targeted marketing strategies based on the profiles of its clients?<ul><li>Why is it important to solve it?</li></ul>Subscriptions to term deposits represent a significant revenue stream for banks. By understanding the profiles of clients who are more likely to subscribe, banks can tailor their marketing strategies, reduce costs, and increase their profitability.

BankOpt: Targeted Marketing Analysis

<ul><li>What is the problem?</li></ul>Understanding and analyzing various aspects of the real estate market, particularly in the context of flats, includes pricing, location preferences, and property features.<ul><li>Why is it important to solve it?</li></ul>This analysis can provide insights into market trends, consumer preferences, and investment opportunities in the real estate sector. It is also crucial for developing data analysis skills in practical, real-world contexts.

Real Estate Insights

<ul><li>What is the problem?</li></ul>Identifying the characteristics of successful Netflix original movies based on different attributes such as genre, runtime, IMDB score, and language.<ul><li>Why is it important to be solved?</li></ul>Understanding the features of successful movies can help Netflix in planning and producing more popular and well-received content, thereby attracting a larger audience and increasing its market share in the OTT platform industry.

Success Code of Netflix Original Movies

This project contains the data record of all types of crime against women in India in the span of years 2001-2014. The main aim of working on this project is to reflect on the situation of women in our society and raise concern about this matter.

Crimes Against Women(2001-2014)

This project contains the real-world data record of hotel bookings of a city and a resort hotel containing details like bookings, cancellations, guest details, etc. from 2015 to 2017. The main aim of the project is to understand and visualize datasets from the hotel and customer point of view i.e.▪ reasons for booking cancellations across various parameters▪ best time to book a hotel▪ peak season

Hotel Bookings

Heart attacks are a leading cause of premature mortality globally, with India witnessing over 3 million deaths annually due to heart attacks and strokes. Numerous factors can impact heart health and efficiency. By gaining valuable insights and taking necessary precautions, we can effectively manage the challenges and complications associated with cardiac health.

Cardiac Patients Medical Reports Analysis

While information on films may be found on many well-known websites, Rotten Tomatoes is the best source for critic reviews. This website enables users to compare the ratings offered by frequent users (audience score) and the scores provided by critics (Tomato-meter) who are officially accredited members of various literary guilds or film critic groups.

Movie Ratings -Rotten Tomatoes Movie Analysis

Marketing campaign data of 2,240 customers of Maven Marketing, including customer profiles, product preferences, campaign successes/failures, and channel performance. In this study, we examined the variables that have a major impact on online purchases, the best performing product, the channel, and the most effective marketing strategy. We suggest specific solutions to improve the efficiency of web purchases and boost income based on our findings.