Post #2294

✅Python Checklist for Data Analysts🧠 1. Python Basics ▪ Variables, data types (int, float, str, bool) ▪ Control flow: if-else, loops (for, while) ▪ Functions and lambda expressions ▪ List, dict, tuple, set basics 2. Data Handling & Manipulation ▪ NumPy: arrays, vectorized operations, broadcasting ▪ Pandas: Series & DataFrame, reading/writing CSV, Excel ▪ Data inspection: head(), info(), describe() ▪ Filtering, sorting, grouping (groupby), merging/joining datasets ▪ Handling missing data (isnull(), fillna(), dropna()) 3. Data Visualization ▪ Matplotlib basics: plots, histograms, scatter plots ▪ Seaborn: statistical visualizations (heatmaps, boxplots) ▪ Plotly (optional): interactive charts 4. Statistics & Probability ▪ Descriptive stats (mean, median, std) ▪ Probability distributions, hypothesis testing (SciPy, statsmodels) ▪ Correlation, covariance 5. Working with APIs & Data Sources ▪ Fetching data via APIs (requests library) ▪ Reading JSON, XML ▪ Web scraping basics (BeautifulSoup, Scrapy) 6. Automation & Scripting ▪ Automate repetitive data tasks using loops, functions ▪ Excel automation (openpyxl, xlrd) ▪ File handling and regular expressions 7. Machine Learning Basics (Optional starting point) ▪ Scikit-learn for basic models (regression, classification) ▪ Train-test split, evaluation metrics 8. Version Control & Collaboration ▪ Git basics: init, commit, push, pull ▪ Sharing notebooks or scripts via GitHub 9. Environment & Tools ▪ Jupyter Notebook / JupyterLab for interactive analysis ▪ Python IDEs (VSCode, PyCharm) ▪ Virtual environments (venv, conda) 10. Projects & Portfolio ▪ Analyze real datasets (Kaggle, UCI) ▪ Document insights in notebooks or blogs ▪ Showcase code & analysis on GitHub 💡Tips: ⦁ Practice coding daily with mini-projects and challenges ⦁ Use interactive platforms like Kaggle, DataCamp, or LeetCode (Python) ⦁ Combine SQL + Python skills for powerful data querying & analysis Python Programming Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L Double Tap ♥️ For More