15 Best Project Ideas for Python : 🐍

🚀 Beginner Level:
1. Simple Calculator
2. To-Do List
3. Number Guessing Game
4. Dice Rolling Simulator
5. Word Counter

🌟 Intermediate Level:
6. Weather App
7. URL Shortener
8. Movie Recommender System
9. Chatbot
10. Image Caption Generator

🌌 Advanced Level:
11. Stock Market Analysis
12. Autonomous Drone Control
13. Music Genre Classification
14. Real-Time Object Detection
15. Natural Language Processing (NLP) Sentiment Analysis

Python List Methods

4 Python practical projects to do for freshers in data analytics

🧵⬇️

1️⃣ Exploratory Data Analysis (EDA) on a Public Dataset

Use a dataset from Kaggle or data.gov

Clean and preprocess the data

Perform statistical analysis and visualization

Draw insights and present findings

2️⃣ Stock Market Analysis Tool

Fetch real-time stock data using an API (e.g., yfinance)

Implement technical indicators (e.g., moving averages, RSI)

Create visualizations of stock performance

Build a simple prediction model

3️⃣ Social Media Sentiment Analysis

Collect tweets or Reddit posts using APIs

Preprocess text data

Perform sentiment analysis

Visualize sentiment trends over time

4️⃣ Customer Churn Prediction

Use a telecom or e-commerce dataset

Perform feature engineering

Build and compare multiple machine learning models

Evaluate model performance and interpret results

Hope it helps :)

FREE RESOURCES TO LEARN PYTHON
👇👇

Free Udacity Course to learn Python

https://imp.i115008.net/5bK93j

Data Structure and OOPS in Python Free Courses

https://bit.ly/3t1WEBt

Free Certified Python course by Freecodecamp

https://www.freecodecamp.org/learn/scientific-computing-with-python/

Free Python Course from Google

https://developers.google.com/edu/python

Free Python Tutorials from Kaggle

https://www.kaggle.com/learn/python

Python hands-on Project

https://t.me/Programming_experts/23

Free Python Books Collection

https://cfm.ehu.es/ricardo/docs/python/Learning_Python.pdf

https://static.realpython.com/python-basics-sample-chapters.pdf

👨‍💻Websites to Practice Python

1. http://codingbat.com/python
2. https://www.hackerrank.com/
3. https://www.hackerearth.com/practice/
4. https://projecteuler.net/archives
5. http://www.codeabbey.com/index/task_list
6. http://www.pythonchallenge.com/

Beginner's guide to Python Free Book

https://t.me/pythondevelopersindia/144

Official Documentation

https://docs.python.org/3/

Join @free4unow_backup for more free courses

ENJOY LEARNING 👍👍

Essentials for Acing any Data Analytics Interviews-

SQL:
1. Beginner
- Fundamentals: SELECT, WHERE, ORDER BY, GROUP BY, HAVING
- Essential JOINS: INNER, LEFT, RIGHT, FULL
- Basics of database and table creation

2. Intermediate
- Aggregate functions: COUNT, SUM, AVG, MAX, MIN
- Subqueries and nested queries
- Common Table Expressions with the WITH clause
- Conditional logic in queries using CASE statements

3. Advanced
- Complex JOIN techniques: self-join, non-equi join
- Window functions: OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag
- Query optimization through indexing
- Manipulating data: INSERT, UPDATE, DELETE

Python:
1. Basics
- Understanding syntax, variables, and data types: integers, floats, strings, booleans
- Control structures: if-else, loops (for, while)
- Core data structures: lists, dictionaries, sets, tuples
- Functions and error handling: lambda functions, try-except
- Using modules and packages

2. Pandas & Numpy
- DataFrames and Series: creation and manipulation
- Techniques: indexing, selecting, filtering
- Handling missing data with fillna and dropna
- Data aggregation: groupby, data summarizing
- Data merging techniques: merge, join, concatenate

3. Visualization
- Plotting basics with Matplotlib: line plots, bar plots, histograms
- Advanced visualization with Seaborn: scatter plots, box plots, pair plots
- Plot customization: sizes, labels, legends, colors
- Introduction to interactive visualizations with Plotly

Excel:
1. Basics
- Cell operations and basic formulas: SUMIFS, COUNTIFS, AVERAGEIFS
- Charts and introductory data visualization
- Data sorting and filtering, Conditional formatting

2. Intermediate
- Advanced formulas: V/XLOOKUP, INDEX-MATCH, complex IF scenarios
- Summarizing data with PivotTables and PivotCharts
- Tools for data validation and what-if analysis: Data Tables, Goal Seek

3. Advanced
- Utilizing array formulas and sophisticated functions
- Building a Data Model & using Power Pivot
- Advanced filtering, Slicers and Timelines in Pivot Tables
- Crafting dynamic charts and interactive dashboards

Power BI:
1. Data Modeling
- Importing data from diverse sources
- Creating and managing dataset relationships
- Data modeling essentials: star schema, snowflake schema

2. Data Transformation
- Data cleaning and transformation with Power Query
- Advanced data shaping techniques
- Implementing calculated columns and measures with DAX

3. Data Visualization and Reporting
- Developing interactive reports and dashboards
- Visualization types: bar, line, pie charts, maps
- Report publishing and sharing, scheduling data refreshes

Statistics:
Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution

⌨️ Calculate derivatives in Python