Introduction

Hello, my name is Shiksha Lahre, and I'm a final-year BTech student at IIT (ISM) Dhanbad, majoring in
Petroleum Engineering. I am from Chirimiri, Chhattisgarh. Over the years, I've developed a strong
foundation in both technical and analytical skills, and I'm particularly excited about applying them in
problem-solving scenarios. During my time at IIT, I've focused on understanding and applying data-
driven approaches.

I'm also proficient in tools like Power BI and SQL, which I used extensively during my internship at
Accenture and Innobyte Services to work on data analysis and visualization projects. These
experiences have honed my ability to analyze and interpret data effectively, which I believe is crucial
for making informed decisions in any business environment.

I'm excited about the opportunity at Google because I admire how the company uses technology to
solve real-world problems, and I believe my combination of technical expertise and problem-solving
skills would allow me to contribute to innovative projects.

Internship wale projects

ACCENTURE DATA ANALYSIS AND VISUALIZATION
During my internship at Accenture, I worked on a data analysis project for a client called Social Buzz, a
fast-growing social media platform. The business problem they faced was the challenge of managing
and scaling their vast amounts of data due to their rapid growth. Social Buzz needed help with
auditing their big data practices, making recommendations for a successful IPO, and analyzing content
categories to identify the top 5 most popular ones.

My role in this project primarily involved analyzing sample datasets to understand the popularity of
different content categories. To achieve this, I first identified which datasets would be necessary to
answer the client’s business question. I worked with 7 datasets, each containing unique information
such as content IDs, categories, reaction types, and scores. After that, I cleaned and merged these
datasets to prepare them for analysis. This step was critical in ensuring that the data was accurate and
structured for further insights.

Using Excel, I extracted and combined the relevant data, and then I created visualizations to highlight
the top 5 content categories based on their aggregate reaction scores. Popularity was quantified by
adding up the scores from the various reaction types, which allowed me to determine which content
categories resonated the most with users.

Throughout the process, I collaborated with a larger team where tasks were delegated based on our
expertise. I was responsible for the data analysis and visualization part of the project, ensuring that
my insights could be presented effectively in front of stakeholders.
INNOBYTE SERVICES (a medium enterprise company)
During my internship at Innobyte Services, I worked on an exploratory data analysis (EDA) project for
a retail sales dataset. The objective of the project was to derive insights into customer behavior,
product popularity, and overall sales trends. To complete this project, I used three main platforms:
Excel, SQL, and Google Data Studio.

In Excel, I focused on data cleaning, where I handled missing values, removed duplicates, and ensured
data consistency. This step was crucial to prepare the dataset for deeper analysis.

Next, in SQL, I performed descriptive statistics analysis, where I analyzed key metrics such as total
sales, average order values, and segmented customers based on purchasing behavior. SQL allowed me
to efficiently extract insights by running queries that highlighted top-selling products, high-value
customers, and performance over time.

Finally, I used Google Data Studio to create visualizations. I presented my findings through interactive
dashboards that displayed trends in sales, customer segments, and product performance, helping
stakeholders quickly grasp the key insights.

Projects on the resume

(In this project) I was able to uncover valuable insights into what people enjoy eating and the factors
that influence their dining decisions. This analysis also tells about how restaurants operate in a given
area.

Project Highlights:

In this particular project, I began by providing a thorough overview of the dataset, which included
various attributes related to food orders. Following that, I formulated several key questions to guide the
analysis, such as:

 What varieties of food are most popular among consumers?

 How do customers rate different dishes?

 How does the cost of food vary across different cuisines?

 Are there significant differences in delivery times between weekdays and weekends?

 What is the correlation between food costs and other factors?

 Does food preparation time impact delivery efficiency?

These questions not only helped me dive deeper into the data but also allowed me to draw meaningful
conclusions that reflect consumer behavior.

Insights: From 1900 data, around 700 ratings were missing, hence this has influenced the analysis to
some extend because people’s likeness here is judged by rating given, cost of the order, and the order
made by them.
also noticed that the most liked or ordered food is American, followed by Japanese, Italian and Mexican.
Now, this could be a sentimental thing, because this dataset is from a locality neywork based, so yes
many people like eating American food.

The Joy of Visualization:

One of the aspects I cherish most about analytics is the integration of data visualization. After all, who
truly enjoys sifting through raw numbers? Engaging visualizations, such as heatmaps, boxplots,
histograms, cat plots, and density distributions, bring the data to life and make the insights more
accessible and understandable. These colorful representations are the heart of any project, allowing
stakeholders to grasp complex patterns at a glance.

I thoroughly enjoy every step of the analytical process, from data exploration to visualization, as it offers
a comprehensive view of how data influences decision-making and behavior.

What kind of tasks do you like?

I like tackling a variety of problem statements through data analysis, particularly those that provide
valuable insights into human behavior and decision-making. Following are some problem statements I
like to solve through data:

1) How big is my data? (rows, columns, unique entries)

2) What variety of data do I have in my dataset? (numerical, categorical, lists, dictionaries)

3) Is my data complete to answer all the questions I have with the dataset? (preprocessing)

4) What are the variables in the data that are correlated, and how much?

5) What patterns or trends can I identify in the data? (sales growth, changes in customer preferences,
demographic segments, purchasing behavior)

6) How can I visualize the data to uncover insights? (histograms, scatter plots, heatmaps, etc.)

7) What are the key takeaways or actionable insights from my analysis? (recommendations, decision-
making, follow-up analyses)

8) How can I ensure the validity and reliability of my analysis? (cross-validation, hypothesis testing,
transparency)

PIZZA-SQL:
I worked on analyzing pizza sales for Flame and Crust, addressing the owner's need for actionable
insights due to their analyst's unavailability. Using SQL, I explored a structured database containing
tables for pizza details, orders, and sales. I developed queries to identify customer preferences, sales
trends, and revenue by pizza type and size. These findings enabled data-driven recommendations to
optimize customer satisfaction and sales growth. This project involved creating and interpreting an
Enhanced Entity-Relationship model, querying the database for insights, and summarizing outcomes in a
clear report for stakeholders.

 Identified the best-selling pizza types and sizes.

 Observed patterns in sales trends over specific time periods (e.g., peak sales hours or days).

 Determined revenue contributions by different pizza categories (e.g., vegetarian, meat).

 Calculated cumulative revenue based on pizza size and type.

 Noted popularity differences between small, medium, and large pizzas.

 Analyzed order volumes to identify busy periods for better resource allocation.

 Found inefficiencies in pizza inventory based on order patterns and quantities.

Recommandations

 Suggested promotions or discounts for less popular pizzas to balance inventory and demand.

 Proposed focusing on high-revenue items to increase profitability.

POWER BI-PWC
In this project, I created interactive Power BI dashboards to simulate executive-level reporting. One
key insight was revealing a 26.54% churn rate, particularly high among month-to-month contracts. I
presented actionable recommendations to address critical churn issues, focusing on targeted
retention strategies. Additionally, I analyzed HR data, which showed an overall 87.8% increase in
promotions from FY20 to FY21. However, I noticed a concerning 13.45% decrease in promotions for
female employees, particularly at the executive level. I highlighted this disparity and suggested
initiatives to improve diversity and inclusion within the organization.

HR QUESTIONS:
Why google?
I want to join Google because it has inspired my passion for technology since I first started learning
internet. My interest grew further when I took the Google Data Analytics course, where I was struck
by the confidence and clarity with which the instructors conveyed complex concepts. This experience
showed me how Google not only creates innovative technologies but also fosters a culture of
continuous learning and knowledge-sharing. I’m excited about the opportunity to contribute to
Google’s mission as a technical solution consultant, where I can work on cutting-edge technologies
and help solve global challenges while growing in a collaborative, impactful environment.

Why technical solution consultant if had worked as a

data analyst intern?
While my internship as a data analyst allowed me to build strong analytical and problem-solving skills,
I am drawn to the role of a technical solution consultant because it offers an opportunity to apply
these skills in a broader, more dynamic context. As a data analyst, I gained valuable experience
working with data to uncover insights, but as a consultant, I would be able to use these insights to
create practical, scalable solutions that drive business decisions and solve real-world problems. I’m
excited about the prospect of working closely with clients to understand their challenges and
leveraging my technical expertise to develop tailored solutions that meet their needs. This shift aligns
with my interest in combining technical analysis with strategic thinking, and I believe it would allow
me to grow and make a greater impact.

Why wanna be a data analyst?

“DATA IS THE NEW OIL”Some people have told me that data analysis isn’t a technical job, but I always
wonder—who is dealing with the complexities of the data then? Data analysts work behind the scenes
to take complex business problems and turn them into solvable mathematical problems. The insights
we generate allow management to make informed decisions. What excites me most is not just
visualizing the data, but also presenting meaningful insights to stakeholders and the management
team, showing how data can drive real business outcomes.

Strength as a data analyst?

That I believe in knowing my data first. (how big is my data, types of data, types of datatype, empty
data, does it need to get filled, or removed, how much data is removable), hence, I would say as a
data analyst, my biggest strength is knowing my data. Then comes the tech stack part. Data regardless
any platform is very important, but how I am going to play with the data depends on what tools I am
choosing. Hence, through out my preparation period, I have gained skills in SQL, python programming
of analysis, power bi, and excel. Hence, I would say these are my strength.

Why CGPA is less?

When I first started preparing for the GATE examination, I realized that I was more drawn to the
numerical and analytical aspects of the preparation. I found myself enjoying tasks like solving logical
reasoning problems and data interpretation more than the core engineering subjects. That’s when I
began to explore the world of data analytics, where these skills play a crucial role. As I focused more
on developing my data analysis skills, my attention to the petroleum engineering concepts became
less, which impacted my marks. However, this shift allowed me to discover my true passion for data,
and since then, I’ve actively pursued internships, projects, and learning opportunities to excel in this
field.

Why wanna be a data analyst in Zomato?

I want to be a Data Analyst because I believe data analysis plays a key role in making it easier for
customers to discover their favorite food with minimal effort. At Zomato, where millions of users rely
on the platform to explore restaurants and food options, data can significantly enhance the user
experience by making personalized recommendations, predicting preferences, and ensuring quick,
reliable delivery.
Zomato's ability to use data for personalized food suggestions, optimizing delivery times, and
improving restaurant ratings is something I find incredibly exciting. As a data analyst, I would love to
contribute to creating a seamless experience for users, where the platform intuitively knows what
they are craving, even before they do. By analyzing user behavior, trends, and feedback, I aim to help
Zomato make informed decisions that not only drive business growth but also ensure customers enjoy
their interactions with the app effortlessly.