Python Projects & Free Books

⌨️ Python Tips & Tricks

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 :)

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

2. CSS ➝
◀️ http://css-tricks.com

3. JavaScript ➝
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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

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9. Git and GitHub ➝
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15. Excel➝
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17. App Development ➝
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18. Power BI ➝
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◀️ https://www.promptingguide.ai/

◀️ https://t.me/aiindi

Join @free4unow_backup for more free courses

Like for more ❤️

ENJOY LEARNING👍👍

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 👍👍

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.