Python project-based interview questions for a data analyst role, along with tips and sample answers [Part-1]

1. Data Cleaning and Preprocessing
   - Question: Can you walk me through the data cleaning process you followed in a Python-based project?
   - Answer: In my project, I used Pandas for data manipulation. First, I handled missing values by imputing them with the median for numerical columns and the most frequent value for categorical columns using fillna(). I also removed outliers by setting a threshold based on the interquartile range (IQR). Additionally, I standardized numerical columns using StandardScaler from Scikit-learn and performed one-hot encoding for categorical variables using Pandas' get_dummies() function.
   - Tip: Mention specific functions you used, like dropna(), fillna(), apply(), or replace(), and explain your rationale for selecting each method.

2. Exploratory Data Analysis (EDA)
   - Question: How did you perform EDA in a Python project? What tools did you use?
   - Answer: I used Pandas for data exploration, generating summary statistics with describe() and checking for correlations with corr(). For visualization, I used Matplotlib and Seaborn to create histograms, scatter plots, and box plots. For instance, I used sns.pairplot() to visually assess relationships between numerical features, which helped me detect potential multicollinearity. Additionally, I applied pivot tables to analyze key metrics by different categorical variables.
   - Tip: Focus on how you used visualization tools like Matplotlib, Seaborn, or Plotly, and mention any specific insights you gained from EDA (e.g., data distributions, relationships, outliers).

3. Pandas Operations
   - Question: Can you explain a situation where you had to manipulate a large dataset in Python using Pandas?
   - Answer: In a project, I worked with a dataset containing over a million rows. I optimized my operations by using vectorized operations instead of Python loops. For example, I used apply() with a lambda function to transform a column, and groupby() to aggregate data by multiple dimensions efficiently. I also leveraged merge() to join datasets on common keys.
   - Tip: Emphasize your understanding of efficient data manipulation with Pandas, mentioning functions like groupby(), merge(), concat(), or pivot().

4. Data Visualization
   - Question: How do you create visualizations in Python to communicate insights from data?
   - Answer: I primarily use Matplotlib and Seaborn for static plots and Plotly for interactive dashboards. For example, in one project, I used sns.heatmap() to visualize the correlation matrix and sns.barplot() for comparing categorical data. For time-series data, I used Matplotlib to create line plots that displayed trends over time. When presenting the results, I tailored visualizations to the audience, ensuring clarity and simplicity.
   - Tip: Mention the specific plots you created and how you customized them (e.g., adding labels, titles, adjusting axis scales). Highlight the importance of clear communication through visualization.

Here is the list of few projects (found on kaggle). They cover Basics of Python, Advanced Statistics, Supervised Learning (Regression and Classification problems) & Data Science

Please also check the discussions and notebook submissions for different approaches and solution after you tried yourself.

1. Basic python and statistics

Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database
Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness
Automobile :- https://www.kaggle.com/toramky/automobile-dataset

2. Advanced Statistics

Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones
World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings
IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset

3. Supervised Learning

a) Regression Problems

How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview
Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand
Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction
Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data
IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview

b) Classification problems

Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview
Titanic :- https://www.kaggle.com/c/titanic
San Francisco crime:- https://www.kaggle.com/c/sf-crime
Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction
Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification
Categorize cusine:- https://www.kaggle.com/c/whats-cooking

4. Some helpful Data science projects for beginners

https://www.kaggle.com/c/house-prices-advanced-regression-techniques

https://www.kaggle.com/c/digit-recognizer

https://www.kaggle.com/c/titanic

5. Intermediate Level Data science Projects

Black Friday Data : https://www.kaggle.com/sdolezel/black-friday

Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones

Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset

Million Song Data : https://www.kaggle.com/c/msdchallenge

Census Income Data : https://www.kaggle.com/c/census-income/data

Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset

Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2

Share with credits: https://t.me/sqlproject

ENJOY LEARNING 👍👍

✅ Learn Trending Skills in 2025 🔰

1. Web Development ➝
◀️ https://t.me/webdevcoursefree

2. CSS ➝
◀️ http://css-tricks.com

3. JavaScript ➝
◀️ http://t.me/javascript_courses

4. React ➝
◀️ http://react-tutorial.app

5. Tailwind CSS ➝
◀️ http://scrimba.com

6. Data Science  ➝
◀️ https://t.me/datasciencefun

7. Python ➝
◀️ http://pythontutorial.net

8. SQL ➝
◀️  https://t.me/sqlanalyst

◀️ https://stratascratch.com/?via=free

9. Git and GitHub ➝
◀️ http://GitFluence.com

10. Blockchain ➝
◀️ https://t.me/Bitcoin_Crypto_Web

11. Mongo DB ➝
◀️ http://mongodb.com

12. Node JS ➝
◀️ http://nodejsera.com

13. English Speaking ➝
◀️ https://t.me/englishlearnerspro

14. C#➝
◀️https://learn.microsoft.com/en-us/training/paths/get-started-c-sharp-part-1/

15. Excel➝
◀️ https://t.me/excel_analyst

16. Generative AI➝
◀️ https://t.me/generativeai_gpt

17. App Development ➝
◀️ https://t.me/appsuser

18. Power BI ➝
◀️ https://t.me/powerbi_analyst

19. Tableau ➝
◀️ https://www.tableau.com/learn/training

20. Machine Learning ➝
◀️ http://developers.google.com/machine-learning/crash-course

21. Artificial intelligence ➝
◀️ http://t.me/machinelearning_deeplearning/

22. Data Analytics ➝
◀️ https://medium.com/@data_analyst

◀️ https://www.linkedin.com/company/sql-analysts

23. Java ➝
◀️ https://t.me/Java_Programming_Notes

◀️ http://learn.microsoft.com/shows/java-for-beginners/

24. C/C++ ➝
◀️ http://imp.i115008.net/kjoq9V

◀️ https://docs.microsoft.com/en-us/cpp/c-language/?view=msvc-170&viewFallbackFrom=vs-2019

25. Data Structures ➝
◀️ https://leetcode.com/study-plan/data-structure/

26. Cybersecurity ➝
◀️ https://t.me/EthicalHackingToday

27. Linux ➝
◀️ https://bit.ly/3KhPdf1

◀️ https://training.linuxfoundation.org/resources/

28. Typescript ➝
◀️ http://learn.microsoft.com/training/paths/build-javascript-applications-typescript/

29. Deep Learning ➝
◀️ http://introtodeeplearning.com

30. Compiler Design ➝
◀️ http://online.stanford.edu/courses/soe-ycscs1-compilers

31. DSA ➝
◀️ http://techdevguide.withgoogle.com/paths/data-structures-and-algorithms/

32. Prompt Engineering ➝
◀️ https://www.promptingguide.ai/

◀️ https://t.me/aiindi

Join @free4unow_backup for more free courses

Like for more ❤️

ENJOY LEARNING👍👍

Many people pay too much to learn Python, but my mission is to break down barriers. I have shared complete learning series to learn Python from scratch.

Here are the links to the Python series

Complete Python Topics for Data Analyst: https://t.me/sqlspecialist/548

Part-1: https://t.me/sqlspecialist/562

Part-2: https://t.me/sqlspecialist/564

Part-3: https://t.me/sqlspecialist/565

Part-4: https://t.me/sqlspecialist/566

Part-5: https://t.me/sqlspecialist/568

Part-6: https://t.me/sqlspecialist/570

Part-7: https://t.me/sqlspecialist/571

Part-8: https://t.me/sqlspecialist/572

Part-9: https://t.me/sqlspecialist/578

Part-10: https://t.me/sqlspecialist/577

Part-11: https://t.me/sqlspecialist/578

Part-12:
https://t.me/sqlspecialist/581

Part-13: https://t.me/sqlspecialist/583

Part-14: https://t.me/sqlspecialist/584

Part-15: https://t.me/sqlspecialist/585

I saw a lot of big influencers copy pasting my content after removing the credits. It's absolutely fine for me as more people are getting free education because of my content.

But I will really appreciate if you share credits for the time and efforts I put in to create such valuable content. I hope you can understand.

Complete SQL Topics for Data Analysts: https://t.me/sqlspecialist/523

Complete Power BI Topics for Data Analysts: https://t.me/sqlspecialist/588

I'll continue with learning series on Excel & Tableau.

Thanks to all who support our channel and share the content with proper credits. You guys are really amazing.

Hope it helps :)

Candlestick chart plot using Python