Python for Data Analysts: From Basics to Advanced Level

🔹 Basics of Python

➊ Python Syntax & Data Types
↳ Variables, data types (int, float, string, bool)
↳ Type conversion and basic operations

➋ Control Flow & Loops
↳ if-else, for, while loops
↳ List comprehensions for efficient iteration

➌ Functions & Lambda Expressions
↳ Defining functions and using *args & **kwargs
↳ Anonymous functions with lambda

➍ Error Handling
↳ try-except for handling errors gracefully
↳ Raising custom exceptions

🔹 Intermediate Python for Data Analytics

➎ Working with Lists, Tuples, and Dictionaries
↳ List, tuple, and dictionary operations
↳ Dictionary and list comprehensions

➏ String Manipulation & Regular Expressions
↳ String formatting and manipulation
↳ Extracting patterns with re module

➐ Date & Time Handling
↳ Working with datetime and pandas.to_datetime()
↳ Formatting, extracting, and calculating time differences

➑ File Handling (CSV, JSON, Excel)
↳ Reading and writing structured files using pandas
↳ Handling large files efficiently using chunks

🔹 Data Analysis with Python

➒ Pandas for Data Manipulation
↳ Reading, cleaning, filtering, and transforming data
↳ Aggregations using .groupby(), .pivot_table()
↳ Merging and joining datasets

➓ NumPy for Numerical Computing
↳ Creating and manipulating arrays
↳ Vectorized operations for performance optimization

⓫ Handling Missing Data
↳ .fillna(), .dropna(), .interpolate()
↳ Imputing missing values for better analytics

⓬ Data Visualization with Matplotlib & Seaborn
↳ Creating plots (line, bar, scatter, histogram)
↳ Customizing plots for presentations
↳ Heatmaps for correlation analysis

🔹 Advanced Topics for Data Analysts

⓭ SQL with Python
↳ Connecting to databases using sqlalchemy
↳ Writing and executing SQL queries in Python (pandas.read_sql())
↳ Merging SQL and Pandas for analysis

⓮ Working with APIs & Web Scraping
↳ Fetching data from APIs using requests
↳ Web scraping using BeautifulSoup and Selenium

⓯ ETL (Extract, Transform, Load) Pipelines
↳ Automating data ingestion and transformation
↳ Cleaning and loading data into databases

⓰ Time Series Analysis
↳ Working with time-series data in Pandas
↳ Forecasting trends using moving averages

⓱ Machine Learning Basics for Data Analysts
↳ Introduction to Scikit-learn (Linear Regression, KNN, Clustering)
↳ Feature engineering and model evaluation

🚀 The best way to learn Python is by working on real-world projects!

Data Analytics Projects: https://t.me/sqlproject

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

Hope it helps :)