*How to forget the past?*

Do you know the two usual ways to remember something?

First—trying to remember it. Put up reminders here and there, and you will probably remember.

Second—by saying daily: I want to forget it. This is another way to remember something and never forget it.
I want to forget A, and so I remember daily to forget A.
And what am I remembering daily? A.

➖✨➖✨➖

'Truth Without Apology'
Available on Amazon: https://amzn.in/d/61CYEr4

✅ Top 10 Data Science Interview Questions (2025) 🔥

1️⃣ What is the difference between supervised and unsupervised learning?
⦁ Supervised: trainings with labeled data (e.g., classification)
⦁ Unsupervised: no labels, finds hidden patterns (e.g., clustering)

2️⃣ How is data science different from data analytics?
⦁ Data science builds models & algorithms; data analytics interprets data patterns for decisions.

3️⃣ Explain the steps to build a decision tree.
⦁ Select best feature (e.g., using entropy/Gini) to split data recursively until stopping criteria.

4️⃣ How do you handle a dataset with >30% missing values?
⦁ Options: drop columns/rows, impute using mean/median/mode or advanced methods.

5️⃣ How do you maintain a deployed machine learning model?
⦁ Monitor performance, retrain with new data, handle data drift & errors.

6️⃣ What is overfitting and how do you prevent it?
⦁ Model fits training data too well, generalizes poorly. Use cross-validation, regularization, pruning.

7️⃣ What is A/B testing and why is it important?
⦁ Controlled experiments to compare two versions for better business decisions.

8️⃣ How often should algorithms/models be updated?
⦁ Depends on data drift, new patterns, or model performance decay.

9️⃣ What techniques do you prefer for text analysis?
⦁ NLP basics: Bag of Words, TF-IDF, and advanced ones like word embeddings (Word2Vec, BERT).

🔟 What are common evaluation metrics for classification?
⦁ Accuracy, Precision, Recall, F1-score, AUC-ROC.

💬 Tap ❤️ for more

The Pioneer, 13th Sep'25

Before outer revolutions, we first need an inner one

➖✨➖✨➖

"The Nepali youth’s courage is unquestionable. The demand now is that their sacrifice should not end in repetition. The real honour to their lives lies not in hashtags or fleeting slogans, but in a revolution that strikes at the root of slavery."

➖✨➖✨➖

📍 Print :
Available in all 8 English editions of The Pioneer across India

🌐 Read online : https://www.dailypioneer.com/2025/columnists/before-outer-revolutions--we-first-need-an-inner-one.html

*Boost Life & Work with AI: 20 Must-Visit Sites 🔥💯*

★ ChatGPT – AI chatbot chat.openai.com
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💡 Explore, Create & Grow with AI – The Future is Yours!

For more posts:
https://t.me/TechAndEvents

> React "❤️" & Share with your Friends!!

Oracle 12c: SQL
Joan Casteel
Cengage, 3rd ed, 2016

Oracle 12c: SQL
Joan Casteel, 2016

Master Power BI with this Cheat Sheet🔥

If you're preparing for a Power BI interview, this cheat sheet covers the key concepts and DAX commands you'll need. Bookmark it for last-minute revision!

📝 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗕𝗮𝘀𝗶𝗰𝘀:

DAX Functions:

- SUMX: Sum of values based on a condition.
- FILTER: Filter data based on a given condition.
- RELATED: Retrieve a related column from another table.
- CALCULATE: Perform dynamic calculations.
- EARLIER: Access a column from a higher context.
- CROSSJOIN: Create a Cartesian product of two tables.
- UNION: Combine the results from multiple tables.
- RANKX: Rank data within a column.
- DISTINCT: Filter unique rows.

Data Modeling:

- Relationships: Create, manage, and modify relationships.
- Hierarchies: Build time-based hierarchies (e.g., Date, Month, Year).
- Calculated Columns: Create calculated columns to extend data.
- Measures: Write powerful measures to analyze data effectively.

Data Visualization:

- Charts: Bar charts, line charts, pie charts, and more.
- Table & Matrix: Display tabular data and matrix visuals.
- Slicers: Create interactive filters.
- Tooltips: Enhance visual interactivity with tooltips.
- Map: Display geographical data effectively.

✨ 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗧𝗶𝗽𝘀:

✅ Use DAX for efficient data analysis.

✅ Optimize data models for performance.

✅ Utilize drill-through and drill-down for deeper insights.

✅ Leverage bookmarks for enhanced navigation.

✅ Annotate your reports with comments for clarity.

Like this post if you need more content like this 👍❤️

𝐒𝐐𝐋 𝐂𝐚𝐬𝐞 𝐒𝐭𝐮𝐝𝐢𝐞𝐬 𝐟𝐨𝐫 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰:

Join for more: https://t.me/TechAndEvents

1. Danny’s Diner:
Restaurant analytics to understand the customer orders pattern.
Link: https://8weeksqlchallenge.com/case-study-1/

2. Pizza Runner
Pizza shop analytics to optimize the efficiency of the operation
Link: https://8weeksqlchallenge.com/case-study-2/

3. Foodie Fie
Subscription-based food content platform
Link: https://lnkd.in/gzB39qAT

4. Data Bank: That’s money
Analytics based on customer activities with the digital bank
Link: https://lnkd.in/gH8pKPyv

5. Data Mart: Fresh is Best
Analytics on Online supermarket
Link: https://lnkd.in/gC5bkcDf

6. Clique Bait: Attention capturing
Analytics on the seafood industry
Link: https://lnkd.in/ggP4JiYG

7. Balanced Tree: Clothing Company
Analytics on the sales performance of clothing store
Link: https://8weeksqlchallenge.com/case-study-7

8. Fresh segments: Extract maximum value
Analytics on online advertising
Link: https://8weeksqlchallenge.com/case-study-8

Placement Material 💯🎯: https://topmate.io/sumit_kumar80/1151675

Data Analyst Interview Questions 👇

1.How to create filters in Power BI?

Filters are an integral part of Power BI reports. They are used to slice and dice the data as per the dimensions we want. Filters are created in a couple of ways.

Using Slicers: A slicer is a visual under Visualization Pane. This can be added to the design view to filter our reports. When a slicer is added to the design view, it requires a field to be added to it. For example- Slicer can be added for Country fields. Then the data can be filtered based on countries.
Using Filter Pane: The Power BI team has added a filter pane to the reports, which is a single space where we can add different fields as filters. And these fields can be added depending on whether you want to filter only one visual(Visual level filter), or all the visuals in the report page(Page level filters), or applicable to all the pages of the report(report level filters)


2.How to sort data in Power BI?

Sorting is available in multiple formats. In the data view, a common sorting option of alphabetical order is there. Apart from that, we have the option of Sort by column, where one can sort a column based on another column. The sorting option is available in visuals as well. Sort by ascending and descending option by the fields and measure present in the visual is also available.


3.How to convert pdf to excel?

Open the PDF document you want to convert in XLSX format in Acrobat DC.
Go to the right pane and click on the “Export PDF” option.
Choose spreadsheet as the Export format.
Select “Microsoft Excel Workbook.”
Now click “Export.”
Download the converted file or share it.


4. How to enable macros in excel?

Click the file tab and then click “Options.”
A dialog box will appear. In the “Excel Options” dialog box, click on the “Trust Center” and then “Trust Center Settings.”
Go to the “Macro Settings” and select “enable all macros.”
Click OK to apply the macro settings.

Machine Learning Algorithms Overview

▌1. Supervised Learning

Supervised learning algorithms learn from labeled data — input features with corresponding output labels.

- Linear Regression
- Used for predicting continuous numerical values.
- Example: Predicting house prices based on features like size, location.
- Learns the linear relationship between input variables and output.

- Logistic Regression
- Used for binary classification problems.
- Example: Spam detection (spam or not spam).
- Outputs probabilities using a logistic (sigmoid) function.

- Decision Trees
- Used for classification and regression.
- Splits data based on feature values to make predictions.
- Easy to interpret but can overfit if not pruned.

- Random Forest
- An ensemble of decision trees.
- Reduces overfitting by averaging multiple trees.
- Good accuracy and robustness.

- Support Vector Machines (SVM)
- Used for classification tasks.
- Finds the hyperplane that best separates classes with maximum margin.
- Can handle non-linear boundaries with kernel tricks.

- K-Nearest Neighbors (KNN)
- Classification and regression based on proximity to neighbors.
- Simple but computationally expensive on large datasets.

- Gradient Boosting Machines (GBM), XGBoost, LightGBM
- Ensemble methods that build models sequentially to correct previous errors.
- Powerful, widely used for structured/tabular data.

- Neural Networks (Basic)
- Can be used for both regression and classification.
- Consists of layers of interconnected nodes (neurons).
- Basis for deep learning but also useful in simpler forms.

▌2. Unsupervised Learning

Unsupervised algorithms learn patterns from unlabeled data.

- K-Means Clustering
- Groups data into K clusters based on feature similarity.
- Used for customer segmentation, anomaly detection.

- Hierarchical Clustering
- Builds a tree of clusters (dendrogram).
- Useful for understanding data structure.

- Principal Component Analysis (PCA)
- Dimensionality reduction technique.
- Projects data into fewer dimensions while preserving variance.
- Helps in visualization and noise reduction.

- Autoencoders (Neural Networks)
- Learn efficient data encodings.
- Used for anomaly detection and data compression.

▌3. Reinforcement Learning (Brief)

- Learns by interacting with an environment to maximize cumulative reward.
- Used in robotics, game playing (e.g., AlphaGo), recommendation systems.

▌4. Other Important Algorithms and Concepts

- Naive Bayes
- Probabilistic classifier based on Bayes theorem.
- Assumes feature independence.
- Fast and effective for text classification.

- Dimensionality Reduction
- Techniques like t-SNE, UMAP for visualization and noise reduction.

- Deep Learning (Advanced Neural Networks)
- Convolutional Neural Networks (CNN) for images.
- Recurrent Neural Networks (RNN), LSTM for sequence data.

React ♥️ for more

✅ Basic SQL Commands Cheat Sheet 🗃️

⦁  SELECT — Select data from database
⦁  FROM — Specify table
⦁  WHERE — Filter query by condition
⦁  AS — Rename column or table (alias)
⦁  JOIN — Combine rows from 2+ tables
⦁  AND — Combine conditions (all must match)
⦁  OR — Combine conditions (any can match)
⦁  LIMIT — Limit number of rows returned
⦁  IN — Specify multiple values in WHERE
⦁  CASE — Conditional expressions in queries
⦁  IS NULL — Select rows with NULL values
⦁  LIKE — Search patterns in columns
⦁  COMMIT — Write transaction to DB
⦁  ROLLBACK — Undo transaction block
⦁  ALTER TABLE — Add/remove columns
⦁  UPDATE — Update data in table
⦁  CREATE — Create table, DB, indexes, views
⦁  DELETE — Delete rows from table
⦁  INSERT — Add single row to table
⦁  DROP — Delete table, DB, or index
⦁  GROUP BY — Group data into logical sets
⦁  ORDER BY — Sort result (use DESC for reverse)
⦁  HAVING — Filter groups like WHERE but for grouped data
⦁  COUNT — Count number of rows
⦁  SUM — Sum values in a column
⦁  AVG — Average value in a column
⦁  MIN — Minimum value in column
⦁  MAX — Maximum value in column

💬 Tap ❤️ for more!

Some important questions to crack data science interview

Q. Describe how Gradient Boosting works.

A. Gradient boosting is a type of machine learning boosting. It relies on the intuition that the best possible next model, when combined with previous models, minimizes the overall prediction error. If a small change in the prediction for a case causes no change in error, then next target outcome of the case is zero. Gradient boosting produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.


Q. Describe the decision tree model.

A. Decision Trees are a type of Supervised Machine Learning where the data is continuously split according to a certain parameter. The leaves are the decisions or the final outcomes. A decision tree is a machine learning algorithm that partitions the data into subsets.


Q. What is a neural network?

A. Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. They, also known as Artificial Neural Networks, are the subset of Deep Learning.


Q. Explain the Bias-Variance Tradeoff

A. The bias–variance tradeoff is the property of a model that the variance of the parameter estimated across samples can be reduced by increasing the bias in the estimated parameters.


Q. What’s the difference between L1 and L2 regularization?

A. The main intuitive difference between the L1 and L2 regularization is that L1 regularization tries to estimate the median of the data while the L2 regularization tries to estimate the mean of the data to avoid overfitting. That value will also be the median of the data distribution mathematically.

ENJOY LEARNING 👍👍