Learning SQL for Data Analytics with Python

🚀 Day 30/100 – Python, Data Analytics & Machine Learning Journey 📊 Started SQL – The Backbone of Data Analytics Today I learned: 13. Views in SQL 14. Stored Procedures 15. Window Functions 16. Functions in SQL 📌 Code & notes :- https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic

🚀 Day 29/100 – Python, Data Analytics & Machine Learning Journey 📊 Started SQL – The Backbone of Data Analytics Today I learned: 11. Constraints in SQL(PRIMARY KEY, FOREIGN KEY, UNIQUE,NOTNULL, CHECK,DEFAULT) 12. ORDER BY Clause 📌 Code & notes :- https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic

🐍 Python for Data Analytics Python has become one of the most powerful tools in my data analytics workflow. From data cleaning with Pandas, visualization with Matplotlib & Seaborn, to automation and analysis, Python helps convert raw data into meaningful insights. Combining Python, SQL, Excel, and BI tools, I focus on building data-driven solutions that support better business decisions. What’s your most-used Python library in analytics? #Python #DataAnalytics #DataScience

🚀 Day 34/100 – Python, Data Analytics & Machine Learning Journey 📊 Started Power BI – The Pillar of Data Visualization Today I learned: 7. Pie Chart 8. Donut Chart 9. Scatter Plot 10. Funnel Chart 📌 Code & notes :- https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic

🚀 Day 36/100 – Python, Data Analytics & Machine Learning Journey 📊 Started Power BI – The Pillar of Data Visualization Today I learned: 14. Drill Down 15. Tooltip 📌 Code & notes :- https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic

🚀 Day 33/100 – Python, Data Analytics & Machine Learning Journey 📊 Started Power BI – The Pillar of Data Visualization Today I learned: 5. Bar Chart 6. Line Charts 📌 Code & notes :- https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic

🐍 Day 2/70 – Introduction to Python for Data Analytics Today, I officially started learning Python for Data Analytics. Why Python? Because it helps in: • Cleaning messy data • Analyzing large datasets • Automating repetitive tasks • Performing statistical analysis • Building data visualizations I revised the basics: • Variables • Data types (int, float, string, list) • Conditional statements • Loops Python is powerful because it allows analysts to go beyond dashboards and deeply explore data. This is just the beginning — next step: Pandas & data manipulation 🚀 Consistency > Motivation. #Python #DataAnalytics #LearningInPublic #70DaysChallenge #CareerGrowth

Mastering Pandas is a must for every data professional. From importing data to cleaning, analyzing, and transforming it - these methods form the backbone of efficient data analysis in Python. If you're starting your Data Science / Data Analytics journey, these Pandas functions are worth bookmarking. 📊🐍 Which Pandas function do you use the most? #DataScience #Python #Pandas #DataAnalytics #MachineLearning #DataCleaning #DataTransformation #DataAnalysis #Analytics #LearnPython #DataScientist #TechLearning

SQL vs Python — I used to think one would replace the other… I was wrong. 🤯 In real-world data science, it’s not SQL or Python — it’s SQL + Python. 🔗 📊 SQL is where the story begins: extracting, filtering, and understanding data 🐍 Python is where the magic happens: modeling, predicting, and building intelligence After working with both, I realized: 👉 SQL makes you a better analyst 👉 Python makes you a better problem solver The best data scientists don’t choose one — they master both. 💡 Curious — which one do you use more in your professional workflow? 👇 #DataScience #Python #SQL #MachineLearning #Analytics #CareerGrowth

• SQL → get data • Pandas → clean it • Python → analyze it • Charts → explain it #DATAANALYST #datascience #dataengineer #careercue