🔥 𝗘𝘃𝗲𝗿𝘆 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗠𝘂𝘀𝘁 𝗞𝗻𝗼𝘄 𝗧𝗵𝗶𝘀!📌

Want to level up in Data Analytics, SQL, Excel, Power BI & Data Science without burning out?

Here are some 𝗲𝗮𝘀𝘆 𝗵𝗮𝗰𝗸𝘀 & 𝗽𝗿𝗼 𝘁𝗿𝗶𝗰𝗸𝘀 every analyst should know 👇

🧭 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 — 𝗦𝗺𝗮𝗿𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀
✅ 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗮 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻: Always define the business question first — every analysis should answer one clear question.
✅ 𝗦𝗮𝗺𝗽𝗹𝗲 𝗙𝗶𝗿𝘀𝘁: Work on a representative sample before running heavy queries on full datasets. Saves time and cost.
✅ 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲 𝗘𝗮𝗿𝗹𝘆: Plot early — charts reveal patterns and oddities faster than tables.
✅ 𝗥𝗲𝗽𝗿𝗼𝗱𝘂𝗰𝗶𝗯𝗶𝗹𝗶𝘁𝘆: Keep scripts/notebooks with comments so results can be repeated and audited.
✅ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗿𝗲𝗽𝗼𝗿𝘁𝘀: Schedule daily/weekly exports for recurring dashboards — frees up time for deep work.
✅ 𝗩𝗲𝗿𝘀𝗶𝗼𝗻 𝗰𝗼𝗻𝘁𝗿𝗼𝗹: Save SQL/notebook changes in Git — small habit that prevents big regressions.

📊 𝗘𝘅𝗰𝗲𝗹 — 𝗦𝗽𝗲𝗲𝗱 𝗵𝗮𝗰𝗸𝘀
✅ 𝗙𝗹𝗮𝘀𝗵 𝗙𝗶𝗹𝗹: Start typing a pattern (e.g., names) and press Ctrl+E to auto-fill — massive time saver.
✅ 𝗣𝗶𝘃𝗼𝘁 𝗧𝗮𝗯𝗹𝗲 𝗶𝗻 𝟮 𝗰𝗹𝗶𝗰𝗸𝘀: Select data → Alt + N + V (or Insert → PivotTable) to build summaries fast.
✅ 𝗜𝗡𝗗𝗘𝗫 + 𝗠𝗔𝗧𝗖𝗛 (𝗶𝗻𝘀𝘁𝗲𝗮𝗱 𝗼𝗳 𝗩𝗟𝗢𝗢𝗞𝗨𝗣): More flexible and faster for large sheets.
✅ 𝗦𝗵𝗼𝗿𝘁𝗰𝘂𝘁𝘀: Ctrl+; (insert date), Ctrl+Shift+L (toggle filters), Alt+Enter (line break in cell).
✅ 𝗙𝗼𝗿𝗺𝘂𝗹𝗮 𝗿𝗲𝗮𝗱𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Use Named Ranges instead of raw cell refs for clarity.
✅ 𝗜𝗙𝗘𝗥𝗥𝗢𝗥: Wrap tricky formulas with IFERROR(formula, "—") to hide ugly errors.
✅ 𝗦𝗽𝗿𝗲𝗮𝗱𝘀𝗵𝗲𝗲𝘁 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: Avoid volatile functions (e.g., INDIRECT) in big sheets.
✅ 𝗦𝗽𝗹𝗶𝘁 & 𝗙𝗶𝘅: Use Text to Columns to split data quickly; Freeze Panes to keep headers visible.

📈 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 — 𝗣𝗿𝗲𝘀𝘀-𝗽𝗹𝗮𝘆 𝘁𝗶𝗽𝘀
✅ 𝗤𝘂𝗲𝗿𝘆 𝗘𝗱𝗶𝘁𝗼𝗿 𝗵𝗮𝰀𝗹𝗽𝗲𝗿: Clean data in Power Query (remove columns, pivot/unpivot) — do heavy transforms here, not in visuals.
✅ 𝗗𝗔𝗫 𝘃𝘀 𝗖𝗼𝗹𝘂𝗺𝗻𝘀: Use DAX measures for aggregations (faster, memory-efficient); use calculated columns only when necessary.
✅ 𝗕𝗼𝗼𝗸𝗺𝗮𝗿𝗸𝘀 & 𝗛𝗶𝗱𝗲𝗻 𝗣𝗮𝗴𝗲𝘀: Create bookmarks for storytelling and to toggle views for users.
✅ 𝗗𝗿𝗶𝗹𝗹𝘁𝗵𝗿𝗼𝘂𝗴𝗵: Add drillthrough pages so users can click a value and see detailed records — great UX.
✅ 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: Use query folding in Power Query to push transforms to the source (faster).
✅ 𝗜𝗻𝗰𝗿𝗲𝗺𝗲𝗻𝘁𝗮𝗹 𝗥𝗲𝗳𝗿𝗲𝘀𝗵: For large datasets, enable incremental refresh to avoid reloading everything daily.

🛠️ 𝗦𝗤𝗟 — 𝗖𝗹𝗲𝘃𝗲𝗿 𝗛𝗮𝗰𝗸𝘀
✅ *𝗔𝘃𝗼𝗶𝗱 𝗦𝗘𝗟𝗘𝗖𝗧 : Always select only needed columns — reduces IO and speeds queries.
✅ 𝗨𝘀𝗲 𝗟𝗜𝗠𝗜𝗧 / 𝗧𝗢𝗣: Test queries with LIMIT 100 or TOP 100 before running full scans.
✅ 𝗘𝗫𝗣𝗟𝗔𝗜𝗡 / 𝗘𝗫𝗣𝗟𝗔𝗡 𝗔𝗡𝗔𝗟𝗬𝗭𝗘: Run explain plans to find bottlenecks and missing indexes.
✅ 𝗖𝗧𝗘𝘀 𝗳𝗼𝗿 𝗰𝗹𝗲𝗮𝗻𝗲𝗿 𝗾𝘂𝗲𝗿𝗶𝗲𝘀: Use WITH (CTE) to break complex logic into readable parts.
✅ 𝗪𝗶𝗻𝗱𝗼𝘄 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀: Use ROW_NUMBER(), RANK(), SUM() OVER() for top-N, running totals, and rankings — often faster than subqueries.
✅ 𝗜𝗻𝗱𝗲𝘅 𝗵𝗶𝘁𝗿𝗮𝘁𝗶𝗼𝗻: Index columns used in WHERE, JOIN, ORDER BY — but don’t over-index (slows writes).
✅ 𝗣𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿𝗶𝘇𝗲 𝗾𝘂𝗲𝗿𝗶𝗲𝘀: Use parameters or prepared statements to prevent SQL injection and reuse plans.
✅ 𝗦𝗺𝗮𝗹𝗹 𝗰𝗵𝗲𝗰𝗸𝘀: Use EXISTS instead of IN for subquery existence checks on large tables.

🤖 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 — 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗛𝗮𝗰𝗸𝘀
✅ 𝗕𝗮𝘀𝗲𝗹𝗶𝗻𝗲 𝗠𝗼𝗱𝗲𝗹: Always train a simple baseline (mean predictor, logistic regression) — hard to beat!
✅ 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝗰𝗲: Quick feature importance check (tree models) shows where to focus engineering.

✅ 𝗦𝗰𝗮𝗹𝗶𝗻𝗴: Scale numeric features for algorithms sensitive to magnitude (SVM, KNN, neural nets).
✅ 𝗖𝗿𝗼𝘀𝘀-𝘃𝗮𝗹𝗶𝗱𝗮𝘁𝗲: Use k-fold CV to get realistic performance, not a single train/test split.
✅ 𝗠𝗼𝗱𝗲𝗹 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀: Put preprocessing + model into pipelines to avoid data leakage and save time.
✅ 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Use SHAP/LIME or simple coefficients to explain model decisions to stakeholders.

✨ 𝗤𝘂𝗶𝗰𝗸 𝗠𝗶𝘅-𝗕𝘂𝗴 𝗙𝗶𝘅𝗲𝘀 & 𝗛𝗮𝗰𝗸𝘀
✅ 𝗗𝗮𝘁𝗮 𝗶𝗻𝗰𝗼𝗺𝗶𝗻𝗴 𝗺𝗮𝘁𝗰𝗵𝗲𝘀 𝗷𝗶𝘁𝘁𝗲𝗿: Add TRIM() and lowercase conversions to normalize texts before joins.
✅ 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗳𝗼𝗿 𝗿𝗲𝗽𝗼𝗿𝘁𝘀: Cache intermediate results (materialized view or temp table) for dashboards that run slow.
✅ 𝗗𝗮𝘁𝗮 𝗾𝗰 𝗰𝗵𝗲𝗰𝗸: Quick sanity checks — row counts, null % per column, min/max ranges — do these first.

🎯 𝗦𝗺𝗮𝗿𝘁 𝗪𝗼𝗿𝗸 > 𝗛𝗮𝗿𝗱 𝗪𝗼𝗿𝗸!✨️

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𝗣𝗮𝘆 𝗔𝗳𝘁𝗲𝗿 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 - 𝗚𝗲𝘁 𝗣𝗹𝗮𝗰𝗲𝗱 𝗜𝗻 𝗧𝗼𝗽 𝗠𝗡𝗖𝘀 😍

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📊 𝗠𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀: 𝗞𝗲𝘆 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 & 𝗕𝗲𝘀𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀

𝗕𝗮𝘀𝗶𝗰𝘀 & 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄

🔹 𝗗𝗮𝘁𝗮 𝗟𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲:

1. Define the Question / Goal – Be crystal clear about what you’re trying to find.


2. Collect & Clean Data – Remove duplicates, fix missing values, standardize formats.


3. Transform & Feature Engineering – Create new helpful variables.


4. Model / Analyze – Use statistical methods, machine learning or aggregations.


5. Visualization & Communication – Present insights clearly to stakeholders.


6. Feedback & Iterate – Learn, refine, and repeat.

🔹 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀:
Uses machine learning & NLP to auto-generate insights, reducing human workload.

𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 & 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀

🔗 Kaggle Learn (Python, Data Viz, ML)
Hands-on tutorials & micro-courses.
https://www.kaggle.com/learn

🔗 Mode Analytics – SQL for Data Analysis Tutorial
Great for SQL in analytics contexts.
https://mode.com/sql-tutorial/introduction-to-sql/

🔗 Microsoft Learn – Data Analytics Path
A structured free learning path by Microsoft.
https://learn.microsoft.com/en-us/training/paths/data-analytics-microsoft/

🔗 CareerFoundry – Best Free Data Analytics Courses Guide
Good overview & links to multiple free courses.
https://careerfoundry.com/en/blog/data-analytics/free-data-analytics-courses/

🔗 Grow with Google – Data Analytics Certificate
Launch your career with Google’s data analytics curriculum.
https://grow.google/certificates/data-analytics/

𝗧𝗶𝗽𝘀 & 𝗧𝗿𝗶𝗰𝗸𝘀 𝗧𝗵𝗮𝘁 𝗔𝗱𝘃𝗮𝗻𝗰𝗲 𝗬𝗼𝘂𝗿 𝗦𝗸𝗶𝗹𝗹𝘀

✅ 𝗕𝘂𝗶𝗹𝗱 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀: Real datasets, real problems. Use public data from Kaggle or government portals.
✅ 𝗧𝗲𝗮𝗰𝗵 𝗧𝗼 𝗙𝗶𝗻𝗲 𝗢𝘁𝗵𝗲𝗿𝘀: Explaining concepts deepens your understanding.
✅ 𝗛𝗮𝗯𝗶𝘁𝘂𝗮𝗹 𝗛𝗮𝗰𝗸 — 𝗗𝗮𝘁𝗮 𝗖𝗵𝗲𝗰𝗸s 𝗙𝗶𝗿𝘀𝘁: Always inspect min, max, null %, duplicates.
✅ 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝗶𝗲𝘀: Join data meetups, Slack groups, Kaggle forums — learn faster together.
✅ 𝗦𝗰𝗮𝗹𝗲 𝘄𝗶𝘁𝗵 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: Use scripts or pipelines (Python, SQL) rather than manual Excel steps.
✅ 𝗤𝗨𝗜𝗖𝗞 𝗤𝗨𝗘𝗥𝗬 𝗖𝗵𝗲𝗰𝗸: Wrap your heavy queries in LIMIT 100 during development to test logic first.

𝗢𝗻𝗲 𝗣𝗼𝗶𝗻𝘁 𝗜𝗻𝘀𝗶𝗴𝗵𝘁

🔹 𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿𝗮𝗹 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀
Go beyond “what happened” — this branch studies how users behave & why. Useful in e-commerce, apps & web.

𝗜𝗳 𝗬𝗼𝘂 𝗡𝗲𝗲𝗱 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝘃𝗲 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗱𝗮𝗶𝗹𝘆 𝘁𝗵𝗲𝗻 𝗿𝗲𝘀𝗽𝗼𝗻𝗱 𝘁𝗼 𝘁𝗵𝗶𝘀 𝗺𝗲𝘀𝘀𝗮𝗴𝗲 😊

Comment Below If You Liked This Content and was Helpful 📩📌

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𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄👇:- 

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📈 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗘𝗱𝗴𝗲 𝗖𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀: 𝗛𝗼𝘄 𝘁𝗼 𝗳𝗶𝗻𝗱 𝗣𝗼𝘀𝘁𝗶𝘃𝗲 & 𝗡𝗲𝗴𝗮𝘁𝗶𝘃𝗲 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀 𝗶𝗻 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮

🔹 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗘𝗱𝗴𝗲 𝗖𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻?
It refers to correlation values near +1 or –1 — indicating strong positive or negative relationships between two variables. It helps identify which factors move together.

🔹 𝗪𝗵𝘆 𝗶𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 𝗶𝗻 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀
✅ Find features that really influence your target variable
✅ Serve as candidates for predictive modeling
✅ Help in variable reduction & understanding multicollinearity

🔹 𝗛𝗼𝘄 𝘁𝗼 𝗖𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲 (𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 / 𝗣𝗮𝗻𝗱𝗮𝘀)

df[['var1','var2']].corr()

This gives you a correlation matrix.
Or for a single pair:

df['var1'].corr(df['var2'])

🔹 𝗛𝗼𝘄 𝘁𝗼 𝗜𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁

+1.0 → perfect positive correlation

–1.0 → perfect negative correlation

0 or near zero → little or no linear relation

Very high correlations may imply multicollinearity, which can hurt regression models

🔹 𝗪𝗵𝗲𝗻 𝘁𝗼 𝗨𝘀𝗲 𝗣𝗼𝘀𝗶𝘁𝗶𝘃𝗲 / 𝗡𝗲𝗴𝗮𝘁𝗶𝘃𝗲 𝗖𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻 𝗙𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴
✅ Filter pairs with abs(corr) > 0.7 for strong correlations
✅ Use heatmaps to visualize many correlations at once
✅ Drop redundant features with extremely high mutual correlation

🔹 𝗖𝗮𝘂𝘁𝗶𝗼𝗻:
Correlation ≠ Causation. Even if two variables move together strongly, one does not necessarily cause the other.

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🔍 𝗗𝗲𝗰𝗼𝗱𝗶𝗻𝗴 𝗢𝘂𝘁𝗹𝗶𝗲𝗿𝘀 — 𝗪𝗵𝘆 𝗧𝗵𝗲𝘆 𝗠𝗮𝘁𝘁𝗲𝗿 𝗶𝗻 𝗗𝗮𝘁𝗮𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀

🔹 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮𝗻 𝗢𝘂𝘁𝗹𝗶𝗲𝗿?
It is a data point that differs significantly from the rest — unusually high or low. Could be valid, error, or an extreme case.

🔹 𝗪𝗵𝘆 𝗖𝗮𝗿𝗲 𝗔𝗯𝗼𝘂𝘁 𝗢𝘂𝘁𝗹𝗶𝗲𝗿𝘀?
✅ They can skew averages & models
✅ Distort regression lines and parameter estimates
✅ Hide true patterns when left unchecked
✅ Sometimes they reveal valuable anomalies (fraud, system errors)

🔹 𝗛𝗼𝘄 𝗧𝗼 𝗗𝗲𝘁𝗲𝗰𝘁 𝗧𝗵𝗲𝗺

Use boxplots & IQR method (points outside 1.5×IQR)

Z-score / standard score (values with |z| > 3)

Scatter plots and visual inspection

Use Mahalanobis distance for multivariate outliers

🔹 𝗪𝗵𝗮𝘁 𝗧𝗼 𝗗𝗼 𝗪𝗶𝘁𝗵 𝗖𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲𝘀
✅ Verify them — is it data entry error or true?
✅ Transform or cap (winsorize) extreme values
✅ Drop only if justified
✅ Use robust models (e.g. median regression) that handle outliers

💬 𝗙𝗼𝗿 𝗬𝗼𝘂:
👉 Did you find any surprising outliers in your data? What method did you use to handle them? Share below ⬇️

𝗙𝘂𝗹𝗹𝘀𝘁𝗮𝗰𝗸 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗙𝗥𝗘𝗘 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 𝗛𝘆𝗱𝗲𝗿𝗮𝗯𝗮𝗱/𝗣𝘂𝗻𝗲/𝗡𝗼𝗶𝗱𝗮😍

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 Eligibility:- BE/BTech / BCA / BSc / MCA / MSc

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💥 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 + 𝗦𝗺𝗮𝗿𝘁 𝗥𝗲𝘀𝘂𝗺𝗲 = 𝗖𝗮𝗿𝗲𝗲𝗿 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 😍

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𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀:- https://pdlink.in/48W1Coy

𝗔𝗧𝗦 𝗙𝗿𝗶𝗲𝗻𝗱𝗹𝘆 𝗥𝗲𝘀𝘂𝗺𝗲 :- https://pdlink.in/46PZD3V

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𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝘆𝘁𝗶𝗰𝘀 𝗙𝗥𝗘𝗘 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 (𝗛𝘆𝗱/𝗣𝘂𝗻𝗲/𝗡𝗼𝗶𝗱𝗮 )😍

Learn from the Top 1% of the data analytics industry

Learn Data Analytics with Hands-on Training, Industry Projects, and 100% Placement Assistance.

 Unlock Opportunities With 500+ Hiring Partners

𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄👇:-

🔹 Online :- https://pdlink.in/3J4i7E6

🔹 Hyderabad :- https://pdlink.in/3VAmsCe

🔹 Pune:-  https://pdlink.in/4hsc6yg

🔹 Noida :-  https://pdlink.in/3VAkxO2

Hurry Up 🏃‍♂️! Limited seats are available.

𝐆𝐨𝐨𝐠𝐥𝐞 𝐈𝐬 𝐇𝐢𝐫𝐢𝐧𝐠 😍

Grab this chance to join Google’s analytics team! 🚀

Role:- Data Analyst (or similar Data Analytics position)

Qualification:- Bachelor’s degree or equivalent practical experience; strong skills in SQL, data visualization, and analytics

Job Location:- Multiple locations globally (including Bengaluru, India)

Salary Package:- ₹12 – 25 LPA (Based on role, experience & location)

𝐀𝐩𝐩𝐥𝐲 𝐍𝐨𝐰👇

CHECK FOR LINK BELOW📌

Apply before the link expires ✨

🎯 Interview Questions & Answers for the Google Data Analyst Role

Technical / SQL & Data Manipulation

1️⃣ Question:
Write a query to count the number of customers who were upsold (i.e., made more than one purchase).

Answer:

SELECT customer_id, COUNT(order_id) AS total_orders
FROM orders
GROUP BY customer_id
HAVING COUNT(order_id) > 1;

✅ This query groups customers and filters those with more than one order — identifying customers who were upsold.

2️⃣ Question:
Write a SQL query to compute cumulative sales for each product by date.

Answer:

SELECT
product_id,
order_date,
SUM(sales) OVER (PARTITION BY product_id ORDER BY order_date) AS cumulative_sales
FROM sales_data;

✅ Uses a window function to calculate running totals of sales by product.

3️⃣ Question:
How would you optimise a very slow SQL query joining multiple large tables?

Answer:
✅ Approach:
• Use EXPLAIN to analyze query execution plan.
• Ensure indexes on join keys and filters.
• Replace SELECT * with only required columns.
• Consider materialized views or CTEs for reuse.
• Check data partitioning in big tables.

4️⃣ Question:
Given two tables (clicks and conversions), write a query to compute click-through-rate (CTR) and conversion rate for each ad.

Answer:

SELECT
c.ad_id,
COUNT(DISTINCT c.user_id) AS total_clicks,
COUNT(DISTINCT cv.user_id) AS total_conversions,
COUNT(DISTINCT cv.user_id) * 1.0 / COUNT(DISTINCT c.user_id) AS conversion_rate
FROM clicks c
LEFT JOIN conversions cv
ON c.ad_id = cv.ad_id AND c.user_id = cv.user_id
GROUP BY c.ad_id;

✅ Calculates CTR and conversion rate per ad by joining user-level click and conversion data.

Product / Business Case / Analytics Thinking

5️⃣ Question:
How would you measure the success of a new feature (for example, audio chat) introduced on an online marketplace?

Answer:
✅ Define Key Metrics:
• Engagement: # of audio chats started, avg duration.
• Retention: % of users using audio chat repeatedly.
• Conversion: % of users who completed a transaction after using audio chat.
✅ Run an A/B test: Compare metrics between feature and control groups.

6️⃣ Question:
Explain a scatterplot showing “Completion Rate vs Video Length” for a video platform. What insights might you draw?

Answer:
✅ Analysis:
• Negative correlation indicates longer videos → lower completion rates.
• Identify “optimal length” range with highest engagement.
• Look for outliers (short videos with poor completion → low interest).
✅ Business Insight: Helps guide ideal video length for creators.

Behavioural / Fit Questions

7️⃣ Question:
Describe a data-analysis project you worked on: what were the challenges and how did you overcome them?

Answer:
✅ Example: “I analyzed user churn data using SQL and Python. The challenge was missing demographic fields. I collaborated with the data engineering team to impute missing values using averages from similar cohorts, improving model accuracy by 12%.”

8️⃣ Question:
How do you prioritise tasks when working on multiple analytics projects simultaneously?

Answer:
✅ Answer: “I assess business impact and urgency, align with stakeholders on deadlines, and use Agile methods — maintaining a backlog and weekly sprint goals. I also ensure regular communication to avoid blockers.”

9️⃣ Question:
Tell us about a time when you received incomplete or conflicting data. What did you do?

Answer:
✅ Answer: “While analyzing marketing data, campaign IDs were missing for 15% of rows. I investigated source logs, validated against CRM data, and applied conditional joins to recover 90% of missing entries before analysis.”

🔟 Question:
What are your favorite data visualization tools, and how do you decide which one to use?

Answer:
✅ Answer: “I use Google Data Studio and Power BI for dashboards, and Python (Matplotlib/Seaborn) for exploratory analysis. Choice depends on audience — dashboards for business stakeholders, Jupyter visuals for internal analytics.

𝐂𝐥𝐢𝐜𝐤 𝐨𝐧 𝐭𝐡𝐞 𝐋𝐢𝐧𝐤 𝐁𝐞𝐥𝐨𝐰 𝐓𝐨 𝐀𝐩𝐩𝐥𝐲👇

https://t.me/careeralertswithHeena/74

𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗙𝘂𝗹𝗹𝘀𝘁𝗮𝗰𝗸 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗪𝗶𝘁𝗵 𝗣𝗮𝘆 𝗔𝗳𝘁𝗲𝗿 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 😍

Learn coding from Top Tech Professionals 

Unlock Opportunities With 500+ Hiring Partners

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Eligibility :- All Degrees & Backgrounds Eligible

𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄👇:- 

https://pdlink.in/3Jia4Ux

( Hurry Up 🏃‍♂️Limited Slots )

𝐎𝐩𝐭𝐢𝐦𝐬𝐩𝐚𝐜𝐞.𝐢𝐧 𝐢𝐬 𝐡𝐢𝐫𝐢𝐧𝐠 😍

Role: Data Science Intern (Freshers / Students)

Qualifications: UG/PG in Data Science, Computer Science, Statistics, Mathematics — Python, R, SQL, Excel, Machine Learning, Data Visualization skills preferred

Location: Remote (India)

Stipend / Package: ₹7,500 – ₹15,000 (Performance-based, Paid Internship)

𝐀𝐩𝐩𝐥𝐲 𝐍𝐨𝐰(check below)

𝗛𝗼𝘄 𝘁𝗼 𝗔𝗽𝗽𝗹𝘆 – 𝗦𝘁𝗲𝗽-𝗯𝘆-𝗦𝘁𝗲𝗽 𝗚𝘂𝗶𝗱𝗲

𝗢𝗽𝗲𝗻 𝘁𝗵𝗲 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗟𝗶𝗻𝗸
Click or tap on: https://in.indeed.com/q-data-science-internship-jobs.html

𝗦𝗲𝗮𝗿𝗰𝗵 𝗳𝗼𝗿 ‘Optimspace .in Data Science Intern’

If the internship doesn't appear immediately, use the search bar with keywords “Optimspace .in Data Science Intern” and select “Remote” under location.

𝗥𝗲𝘃𝗶𝗲𝘄 𝘁𝗵𝗲 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗗𝗲𝘁𝗮𝗶𝗹𝘀
Check the job description, skills required, stipend, and eligibility to ensure you match their requirements.

𝗣𝗿𝗲𝗽𝗮𝗿𝗲 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲
Update your resume, focusing on relevant skills like Python, SQL, Excel, and any analytics projects or coursework you’ve completed.

𝗖𝗹𝗶𝗰𝗸 ‘Apply Now’
Find the ‘Apply Now’ button on the internship listing page and click it.

𝗙𝗶𝗹𝗹 𝗢𝘂𝘁 𝘁𝗵𝗲 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗙𝗼𝗿𝗺
Enter your personal information, upload your resume (PDF preferred), and add a cover letter if requested.

𝗦𝘂𝗯𝗺𝗶𝘁 𝘁𝗵𝗲 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻
Double check your entries, then submit the application.

𝗧𝗿𝗮𝗰𝗸 𝗬𝗼𝘂𝗿 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻
Log in to Indeed to check the status of your application and watch for updates or messages from the employer.

Check out this link to apply for this internship 📌

https://t.me/careeralertswithHeena/75

🦄 ⚪ 𝗙𝗥𝗢𝗠 𝗡𝗢𝗩𝗘𝗠𝗕𝗘𝗥 𝟰 — 𝗖𝗛𝗔𝗧𝗚𝗣𝗧 𝗚𝗢 𝗙𝗢𝗥 𝗙𝗥𝗘𝗘 𝗜𝗡 𝗜𝗡𝗗𝗜𝗔! 🇮🇳

Yes, you read that right! OpenAI is giving India exclusive early access to premium ChatGPT Go features — absolutely FREE, starting November 4th! 🎉
No Plus plan, no payment, no hidden cost. Just log in and enjoy upgraded ChatGPT power 💥

⚪ 𝗪𝗵𝗮𝘁’𝘀 𝗜𝗻𝗰𝗹𝘂𝗱𝗲𝗱:
⚡ Higher daily message limits
🎨 More image generations & file uploads
🧠 Longer memory for smarter, context-aware replies
💬 GPT-4-level capabilities (rolling out gradually across users)

⚪ 𝗛𝗲𝗿𝗲’𝘀 𝗛𝗼𝘄 𝗧𝗼 𝗚𝗲𝘁 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗙𝗿𝗲𝗲 (𝗦𝗧𝗘𝗣 𝗕𝗬 𝗦𝗧𝗘𝗣):
1️⃣ Update or Install ChatGPT App – Go to Play Store / App Store and make sure you’re on the latest version. (You can also visit chat.openai.com).
2️⃣ Sign In or Create Account – Use your email or Google/Apple login. Make sure your region is set to India.
3️⃣ Watch for “ChatGPT Go Free for a Year” Banner – Once you log in after Nov 4, you’ll see a pop-up or banner offering free ChatGPT Go access.
4️⃣ Click “Activate” or “Claim Offer” – Follow the on-screen steps; no payment info is needed.
5️⃣ Enjoy Premium Features – You’ll automatically get access to GPT-4-level tools, image generation, and longer chats for 1 full year!
6️⃣ Check Settings → My Plan – Confirm that your plan shows ChatGPT Go (Free for 1 Year).

⚪ 𝗪𝗵𝗮𝘁 𝗧𝗼 𝗗𝗼 𝗜𝗳 𝗬𝗼𝘂 𝗗𝗼𝗻’𝘁 𝗦𝗲𝗲 𝗧𝗵𝗲 𝗢𝗳𝗳𝗲𝗿:
🔹 Log out & back in after Nov 4
🔹 Update your app manually
🔹 Try the web version
🔹 Rollout is gradual — check again after a few hours

⚪ 𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀:
India becomes one of the first countries in the world to get this free premium rollout 🌏
Perfect for students, data professionals, creators, and tech learners who use AI daily.

✨ 𝗤𝘂𝗶𝗰𝗸 𝗧𝗶𝗽:
Comment “GO” 👇🏼 and I’ll share the early access link + activation guide directly in your DMs or broadcast channel 🔗

🔍 𝗧𝗮𝗴𝘀 𝗳𝗼𝗿 𝗬𝗼𝘂𝗿 𝗣𝗼𝘀𝘁:
• ChatGPT Free India 2025
• OpenAI Free Access November 4
• ChatGPT Go Activation
• AI Tools for Students & Professionals
• Data & AI for Everyone

🚨𝐋𝐚𝐬𝐭 𝐂𝐡𝐚𝐧𝐜𝐞 𝐭𝐨 𝐆𝐞𝐭 𝐆𝐨𝐯𝐞𝐫𝐧𝐦𝐞𝐧𝐭 𝐏𝐚𝐢𝐝 𝐈𝐧𝐭𝐞𝐫𝐧𝐬𝐡𝐢𝐩😍

Here’s your golden opportunity to earn ₹40,000 per month while gaining real-world experience through an official Government Internship Program 💼

⏳ Deadline Extended till 1st November 2025 — Don’t miss this final chance to apply!

📅 Internship Duration: 6 Months
💰 Stipend: ₹40,000 per month

💻 Domains Available:
• Data Science
• Software Development
• Cybersecurity
• Product Management
• UI/UX Design

✨ You’ll Also Get:
✅ Mentorship from Industry Experts
✅ Hands-on Project Experience
✅ Certificate of Completion from an Official Government Platform

🎓 This internship is 100% legit, designed to help students and freshers build experience + income while studying!

⚠️ Registration Closes Soon!

or access it directly here 👉 https://www.bharatdigital.io/fellowship

𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 𝗢𝗻 𝗙𝘂𝗹𝗹𝘀𝘁𝗮𝗰𝗸𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁😍

Build  Full Stack Skills That Crack Product-Based Companies

- Break into the Tech industry as a Full-stack developer with the right guidance.

Eligibility :- Working Professionals & Graduates 

𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:- 

https://pdlink.in/4nwSPwU

Date :- November 02 , 2025  Time:-7:00 PM

𝟑 𝐈𝐧𝐭𝐞𝐫𝐧𝐬𝐡𝐢𝐩 𝐎𝐩𝐞𝐧𝐢𝐧𝐠𝐬 𝐀𝐩𝐩𝐥𝐲 𝐛𝐞𝐟𝐨𝐫𝐞 𝐢𝐭 𝐞𝐱𝐩𝐢𝐫𝐞𝐬😍

𝗢𝗽𝘁𝗶𝗺𝗮𝘀𝘆𝘀 📊
Role:- Data Analytics Intern
Location:- Work From Home (India)
Qualification:- Any graduate / fresher
Salary Package:- ₹10,000 – ₹20,000 / month (approx)

𝗔𝗺𝗿𝗮𝘁𝗮 📊
Role:- Data Analytics Intern
Location:- Work From Home
Qualification:- Fresher / Student
Salary Package:- ₹15,000 / ₹25,000 month (estimated)

𝗞𝗼𝗿𝗲𝘀 📊
Role:- Digital Transformation / Data Analytics Intern
Location:- Mumbai, India
Qualification:- Bachelor’s degree / MBA / Data Analytics background
Salary Package:- ₹8,000 – ₹12,000 / month

👉 𝐅𝐨𝐫 𝐦𝐨𝐫𝐞 𝐣𝐨𝐛 𝐮𝐩𝐝𝐚𝐭𝐞𝐬 𝐚𝐧𝐝 𝐝𝐞𝐭𝐚𝐢𝐥𝐬, 𝐯𝐢𝐬𝐢𝐭 𝐦𝐲 𝐉𝐨𝐛-𝐔𝐩𝐝𝐚𝐭𝐞𝐬 𝐂𝐡𝐚𝐧𝐧𝐞𝐥:
https://t.me/careeralertswithHeena/78 👈(Click Here to Apply)📌

Step-by-Step: How to apply📍🧑‍💻

1. Open the apply link — click the direct link in the post (Internshala link opens the internship page).
2. Read full listing — check duration, stipend, responsibilities, eligibility, start date.
3. Prepare your resume (1 page) — highlight: SQL, Excel, Power BI, Python, projects, and any coursework. Filename: Firstname_Lastname_Resume.pdf.
4. Write a 2-3 line cover message (if there’s a form box) — example: “Hi, I’m Ayesha — a fresher with hands-on SQL & Power BI. I’m excited to intern at [Company] and contribute to analytics projects.”
5. Sign in / Create account — Internshala requires login. Use the email you monitor.
6. Fill apply form carefully — attach resume, add academic details, answer screening questions honestly. Paste your short cover message if there’s a message box.
7. Submit & note confirmation — screenshot the confirmation or email. Save the internship URL and submission date.
8. Follow up after 7–10 days — if contact details are listed, send a polite follow-up message: short, 1–2 lines reaffirming interest and availability.
9. Prepare for interview — review SQL basics (joins, group by, window functions), Excel shortcuts & formulas, Power BI basics (data model, measures), and one small project to discuss.
10. Stay organized — keep a Google Sheet of: Company | Role | Link | Applied on | Status | Notes.✅️