Shailesh Shakya

Excel vs SQL vs Python (pandas): 1️⃣ Filtering Data ↳ Excel: =FILTER(A2:D100, B2:B100>50) (Excel 365 users) ↳ SQL: SELECT * FROM table WHERE column > 50; ↳ Python: df_filtered = df[df['column'] > 50] 2️⃣ Sorting Data ↳ Excel: Data → Sort (or =SORT(A2:A100, 1, TRUE)) ↳ SQL: SELECT * FROM table ORDER BY column ASC; ↳ Python: df_sorted = df.sort_values(by="column") 3️⃣ Counting Rows ↳ Excel: =COUNTA(A:A) ↳ SQL: SELECT COUNT(*) FROM table; ↳ Python: row_count = len(df) 4️⃣ Removing Duplicates ↳ Excel: Data → Remove Duplicates ↳ SQL: SELECT DISTINCT * FROM table; ↳ Python: df_unique = df.drop_duplicates() 5️⃣ Joining Tables ↳ Excel: Power Query → Merge Queries (or VLOOKUP/XLOOKUP) ↳ SQL: SELECT * FROM table1 JOIN table2 ON table1.id = table2.id; ↳ Python: df_merged = pd.merge(df1, df2, on="id") 6️⃣ Ranking Data ↳ Excel: =RANK.EQ(A2, $A$2:$A$100) ↳ SQL: SELECT column, RANK() OVER (ORDER BY column DESC) AS rank FROM table; ↳ Python: df["rank"] = df["column"].rank(method="min", ascending=False) 7️⃣ Moving Average Calculation ↳ Excel: =AVERAGE(B2:B4) (manually for rolling window) ↳ SQL: SELECT date, AVG(value) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg FROM table; ↳ Python: df["moving_avg"] = df["value"].rolling(window=3).mean() 8️⃣ Running Total ↳ Excel: =SUM($B$2:B2) (drag down) ↳ SQL: SELECT date, SUM(value) OVER (ORDER BY date) AS running_total FROM table; ↳ Python: df["running_total"] = df["value"].cumsum() Best SQL & Data Analytics Courses 💯💯💯💯 1. Google Data Analytics https://lnkd.in/gZCtcd5H 2. IBM Data Analyst https://lnkd.in/gKv8Sr5J 1. SQL for Data Science – UC Davis https://lnkd.in/gg6Ph_aY 3. Databases and SQL for Data Science with Python – IBM https://lnkd.in/gjkrPufW 4. Introduction to Querying Databases https://lnkd.in/gxi6RgbX   5. Data Analysis with SQL – Google https://lnkd.in/gQFKGDaX 6. Advanced SQL for Data Scientists – LinkedIn Learning https://lnkd.in/gw2GFpE9 7. Analyzing Big Data with SQL – Cloudera https://lnkd.in/ghqfZRFY 8. Data Science: Querying with SQL – HarvardX https://edx.sjv.io/6yk13E 9. Database Design & Basic SQL in PostgreSQL – University of Michigan https://lnkd.in/gSu53gWF 10. PostgreSQL for Everybody Specialization – University of Michigan https://lnkd.in/g6H7qxry 11. NoSQL Systems – UC San Diego https://lnkd.in/ggukxzGb 12.Data Management & Visualization – Wesleyan University https://lnkd.in/gSKfu2tF . . Join my telegram: https://lnkd.in/gaaR8jxM . . .

Excel vs SQL vs Python (pandas): 1️⃣ Filtering Data ↳ Excel: =FILTER(A2:D100, B2:B100>50) (Excel 365 users) ↳ SQL: SELECT * FROM table WHERE column > 50; ↳ Python: df_filtered = df[df['column'] > 50] 2️⃣ Sorting Data ↳ Excel: Data → Sort (or =SORT(A2:A100, 1, TRUE)) ↳ SQL: SELECT * FROM table ORDER BY column ASC; ↳ Python: df_sorted = df.sort_values(by="column") 3️⃣ Counting Rows ↳ Excel: =COUNTA(A:A) ↳ SQL: SELECT COUNT(*) FROM table; ↳ Python: row_count = len(df) 4️⃣ Removing Duplicates ↳ Excel: Data → Remove Duplicates ↳ SQL: SELECT DISTINCT * FROM table; ↳ Python: df_unique = df.drop_duplicates() 5️⃣ Joining Tables ↳ Excel: Power Query → Merge Queries (or VLOOKUP/XLOOKUP) ↳ SQL: SELECT * FROM table1 JOIN table2 ON table1.id = table2.id; ↳ Python: df_merged = pd.merge(df1, df2, on="id") 6️⃣ Ranking Data ↳ Excel: =RANK.EQ(A2, $A$2:$A$100) ↳ SQL: SELECT column, RANK() OVER (ORDER BY column DESC) AS rank FROM table; ↳ Python: df["rank"] = df["column"].rank(method="min", ascending=False) 7️⃣ Moving Average Calculation ↳ Excel: =AVERAGE(B2:B4) (manually for rolling window) ↳ SQL: SELECT date, AVG(value) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg FROM table; ↳ Python: df["moving_avg"] = df["value"].rolling(window=3).mean() 8️⃣ Running Total ↳ Excel: =SUM($B$2:B2) (drag down) ↳ SQL: SELECT date, SUM(value) OVER (ORDER BY date) AS running_total FROM table; ↳ Python: df["running_total"] = df["value"].cumsum() Best SQL & Data Analytics Courses 💯💯💯💯 1. Google Data Analytics https://lnkd.in/gZCtcd5H 2. IBM Data Analyst https://lnkd.in/gKv8Sr5J 1. SQL for Data Science – UC Davis https://lnkd.in/gg6Ph_aY 3. Databases and SQL for Data Science with Python – IBM https://lnkd.in/gjkrPufW 4. Introduction to Querying Databases https://lnkd.in/gxi6RgbX   5. Data Analysis with SQL – Google https://lnkd.in/gQFKGDaX 6. Advanced SQL for Data Scientists – LinkedIn Learning https://lnkd.in/gw2GFpE9 7. Analyzing Big Data with SQL – Cloudera https://lnkd.in/ghqfZRFY 8. Data Science: Querying with SQL – HarvardX https://edx.sjv.io/6yk13E 9. Database Design & Basic SQL in PostgreSQL – University of Michigan https://lnkd.in/gSu53gWF 10. PostgreSQL for Everybody Specialization – University of Michigan https://lnkd.in/g6H7qxry 11. NoSQL Systems – UC San Diego https://lnkd.in/ggukxzGb 12.Data Management & Visualization – Wesleyan University https://lnkd.in/gSKfu2tF . . Join my telegram: https://lnkd.in/gaaR8jxM . . .

Machine Learning Algorithms 👇 🔍 Algorithms →  Use Case → Formula → ⚠️ Avoid When 1. Linear Regression → Predict continuous values → Y = b0 + b1X + ... → Non-linear data 2. Logistic Regression → Binary classification → Sigmoid curve → Non-linear boundaries 3. Decision Tree → Interpret models → Split rules → Overfitting risk 4. Random Forest → High accuracy → Multiple trees voting → Slower, less transparent 5. Gradient Boosting → Precision tasks → Trees + loss minimization → Needs tuning 6. SVM → Margin-based classification → Kernel tricks → Too slow on big data 7. KNN → Small-scale prediction → Distance voting → Noisy/large data 8. Naive Bayes → Text classification → Probabilistic → Feature correlation breaks it 9. K-Means → Customer segmentation → Cluster center → Wrong with irregular shapes 10. PCA → Reduce features → Max variance → When interpretability is key 11. Neural Nets → Pattern recognition → Weights + activations → Low data? Not ideal 12. CNN → Image/video tasks → Convolutions → Not for sequences 13. RNN → Sequence prediction → Feedback loops → Long-term memory fades 14. Transformers (GPT/BERT) → NLP/AI chat → Attention mechanism → Heavy compute 15. DBSCAN → Shape-flexible clustering → Density → Sparse high-dim data Best online courses to MASTER Machine Learning 👇 1️⃣ Machine Learning by Andrew Ng (Stanford University) https://lnkd.in/gNXTg8aP 2️⃣ Deep Learning Specialization by deeplearning.ai https://lnkd.in/gSUchfqm 3️⃣ Mathematics for Machine Learning Specialization by Imperial College London https://lnkd.in/gDYg95YQ 4️⃣ Applied Data Science with Python Specialization by University of Michigan https://lnkd.in/dS9ywegq 5️⃣ Advanced Machine Learning by Google Cloud https://lnkd.in/gZWWWPUV 6️⃣ Machine Learning with Python by IBM https://lnkd.in/gnrmhJ6H 7️⃣ Supervised Machine Learning: Regression and Classification https://lnkd.in/g72BY4zt 8️⃣ Unsupervised Learning, Recommenders, Reinforcement Learning by University of Alberta https://lnkd.in/gT-rwir5 9️⃣ Practical Machine Learning by Johns Hopkins University https://lnkd.in/gHBVRYCW 🔟 How Google does Machine Learning by Google Cloud https://lnkd.in/gQa3bMhS . . . Join NextCareerStep to master demanding IT skills in every week. Plus, Receive FREE resources like 👇 - Roadmaps - Cheatsheet - Checklist - Resume temples 5000 people already joined 👇 https://lnkd.in/gna2uuht Join Telegram: https://lnkd.in/gaaR8jxM . . . 💬 Want a high quality version of this? Comment “CHEATSHEET” and I’ll send it your way! . . . #MachineLearning #AI #DataScience #ML #CheatSheet #LinkedInLearning #CareerGrowth