🚀Stanford just completed a must-watch series about AI:

If you’re building your AI career, stop scrolling.
This isn’t another surface-level overview. It’s the clearest, most structured intro to LLMs you could follow, straight from the Stanford Autumn 2025 curriculum.

📚 𝗧𝗼𝗽𝗶𝗰𝘀 𝗰𝗼𝘃𝗲𝗿𝗲𝗱 𝗶𝗻𝗰𝗹𝘂𝗱𝗲:
• How Transformers actually work (tokenization, attention, embeddings)
• Decoding strategies & MoEs
• LLM finetuning (LoRA, RLHF, supervised)
• Evaluation techniques (LLM-as-a-judge)
• Optimization tricks (RoPE, quantization, approximations)
• Reasoning & scaling
• Agentic workflows (RAG, tool calling)

- Lecture 1: https://lnkd.in/dDER-qyp
- Lecture 2: https://lnkd.in/dk-tGUDm
- Lecture 3: https://lnkd.in/drAPdjJY
- Lecture 4: https://lnkd.in/e_RSgMz7
- Lecture 5: https://lnkd.in/eivMA9pe
- Lecture 6: https://lnkd.in/eYwwwMXn
- Lecture 7: https://lnkd.in/eKwkEDXV
- Lecture 8: https://lnkd.in/eEWvyfyK
- Lecture 9: https://lnkd.in/euiKRGaQ

Power BI Interview Questions with Answers

Question: How would you write a DAX formula to calculate a running total that resets every year?
RunningTotal =
CALCULATE( SUM('Sales'[Amount]),
  FILTER( ALL('Sales'),
    'Sales'[Year] = EARLIER('Sales'[Year]) &&
    'Sales'[Date] <= EARLIER('Sales'[Date])))

Question: How would you manage and optimize Power BI reports that need to handle very large datasets (millions of rows)?
Solution:
1. Use DirectQuery mode if real-time data is needed.
2. Pre-aggregate data in the data source.
3. Use dataflows for preprocessing.
4. Implement incremental refresh.

Question: What steps would you take if a scheduled data refresh in Power BI fails?
Solution:
Check the Power BI service for error messages.
Verify data source connectivity and credentials.
Review gateway configuration.
Optimize and simplify the query.

Question: How would you create a report that dynamically updates based on user input or selections?
Solution: Use slicers and what-if parameters. Create dynamic measures using DAX that respond to user selections.

Question: How would you incorporate advanced analytics or machine learning models into Power BI?
Solution:
Use R or Python noscripts in Power BI to apply advanced analytics.
Integrate with Azure Machine Learning to embed predictive models.
Use AI visuals like Key Influencers or Decomposition Tree.

Question: How would you integrate Power BI with other Microsoft services like SharePoint, Teams, or PowerApps?
Solution: Embed Power BI reports in SharePoint Online and Microsoft Teams. Use PowerApps to create custom forms that interact with Power BI data. Automate workflows with Power Automate.

Question: How to use if Parameters in Power BI?
Go to "Manage Parameters":
Navigate to the "Home" tab in the ribbon.
Click on "Manage Parameters" from the "External Tools" group.
Click on "New Parameter."
Enter a name for the parameter and select its data type (e.g., Text, Decimal Number, Integer, Date/Time).
Optionally, set the default value and any available values (for dropdown selection).

Question: What is the role of Power BI Paginated Reports and when are they used?
Solution: Power BI Paginated Reports (formerly SQL Server Reporting Services or SSRS) are used for pixel-perfect, printable, and paginated reports. They are typically used for operational and transactional reporting scenarios where precise formatting and layout control are required, such as invoices, statements, or regulatory reports.

Question: What are the options available for managing query parameters in Power Query Editor?
Solution: Power Query Editor allows users to define and manage query parameters to dynamically control data loading and transformation. Parameters can be created from values in the data source, entered manually, or generated from expressions, providing flexibility and reusability in query design.

OOPs

📈 Data Visualisation Cheatsheet: 13 Must-Know Chart Types ✅

1️⃣ Gantt Chart
Tracks project schedules over time.
🔹 Advantage: Clarifies timelines & tasks
🔹 Use case: Project management & planning

2️⃣ Bubble Chart
Shows data with bubble size variations.
🔹 Advantage: Displays 3 data dimensions
🔹 Use case: Comparing social media engagement

3️⃣ Scatter Plots
Plots data points on two axes.
🔹 Advantage: Identifies correlations & clusters
🔹 Use case: Analyzing variable relationships

4️⃣ Histogram Chart
Visualizes data distribution in bins.
🔹 Advantage: Easy to see frequency
🔹 Use case: Understanding age distribution in surveys

5️⃣ Bar Chart
Uses rectangular bars to visualize data.
🔹 Advantage: Easy comparison across groups
🔹 Use case: Comparing sales across regions

6️⃣ Line Chart
Shows trends over time with lines.
🔹 Advantage: Clear display of data changes
🔹 Use case: Tracking stock market performance

7️⃣ Pie Chart
Represents data in circular segments.
🔹 Advantage: Simple proportion visualization
🔹 Use case: Displaying market share distribution

8️⃣ Maps
Geographic data representation on maps.
🔹 Advantage: Recognizes spatial patterns
🔹 Use case: Visualizing population density by area

9️⃣ Bullet Charts
Measures performance against a target.
🔹 Advantage: Compact alternative to gauges
🔹 Use case: Tracking sales vs quotas

🔟 Highlight Table
Colors tabular data based on values.
🔹 Advantage: Quickly identifies highs & lows
🔹 Use case: Heatmapping survey responses

1️⃣1️⃣ Tree Maps
Hierarchical data with nested rectangles.
🔹 Advantage: Efficient space usage
🔹 Use case: Displaying file system usage

1️⃣2️⃣ Box & Whisker Plot
Summarizes data distribution & outliers.
🔹 Advantage: Concise data spread representation
🔹 Use case: Comparing exam scores across classes

1️⃣3️⃣ Waterfall Charts / Walks
Visualizes sequential cumulative effect.
🔹 Advantage: Clarifies source of final value
🔹 Use case: Understanding profit & loss components

💡 Use the right chart to tell your data story clearly.

Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c

Tap ♥️ for more!

𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗥𝗼𝗮𝗱𝗺𝗮𝗽

𝟭. 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀: Master Python, SQL, and R for data manipulation and analysis.

𝟮. 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Use Excel, Pandas, and ETL tools like Alteryx and Talend for data processing.

𝟯. 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Learn Tableau, Power BI, and Matplotlib/Seaborn for creating insightful visualizations.

𝟰. 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝘀: Understand Denoscriptive and Inferential Statistics, Probability, Regression, and Time Series Analysis.

𝟱. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Get proficient in Supervised and Unsupervised Learning, along with Time Series Forecasting.

𝟲. 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗧𝗼𝗼𝗹𝘀: Utilize Google BigQuery, AWS Redshift, and NoSQL databases like MongoDB for large-scale data management.

𝟳. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴: Implement Data Quality Monitoring (Great Expectations) and Performance Tracking (Prometheus, Grafana).

𝟴. 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗧𝗼𝗼𝗹𝘀: Work with Data Orchestration tools (Airflow, Prefect) and visualization tools like D3.js and Plotly.

𝟵. 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗿: Manage resources using Jupyter Notebooks and Power BI.

𝟭𝟬. 𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗘𝘁𝗵𝗶𝗰𝘀: Ensure compliance with GDPR, Data Privacy, and Data Quality standards.

𝟭𝟭. 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴: Leverage AWS, Google Cloud, and Azure for scalable data solutions.

𝟭𝟮. 𝗗𝗮𝘁𝗮 𝗪𝗿𝗮𝗻𝗴𝗹𝗶𝗻𝗴 𝗮𝗻𝗱 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴: Master data cleaning (OpenRefine, Trifacta) and transformation techniques.

Data Analytics Resources
👇👇
https://news.1rj.ru/str/sqlspecialist

Hope this helps you 😊

✅ How to Get a Data Analyst Job as a Fresher in 2025 📊💼

🔹 What’s the Market Like in 2025?
• High demand in BFSI, healthcare, retail & tech
• Companies expect Excel, SQL, BI tools & storytelling skills
• Python & data visualization give a strong edge
• Remote jobs are fewer, but freelance & internship opportunities are growing

🔹 Skills You MUST Have:
1️⃣ Excel – Pivot tables, formulas, dashboards
2️⃣ SQL – Joins, subqueries, CTEs, window functions
3️⃣ Power BI / Tableau – For interactive dashboards
4️⃣ Python – Data cleaning & analysis (Pandas, Matplotlib)
5️⃣ Statistics – Mean, median, correlation, hypothesis testing
6️⃣ Business Understanding – KPIs, revenue, churn etc.

🔹 Build a Strong Profile:
✔️ Do real-world projects (sales, HR, e-commerce data)
✔️ Publish dashboards on Tableau Public / Power BI
✔️ Share work on GitHub & LinkedIn
✔️ Earn certifications (Google Data Analytics, Power BI, SQL)
✔️ Practice mock interviews & case studies

🔹 Practice Platforms:
• Kaggle
• StrataScratch
• DataLemur

🔹 Fresher-Friendly Job Titles:
• Junior Data Analyst
• Business Analyst
• MIS Executive
• Reporting Analyst

🔹 Companies Hiring Freshers in 2025:
• TCS
• Infosys
• Wipro
• Cognizant
• Fractal Analytics
• EY, KPMG
• Startups & EdTech companies

📝 Tip: If a job says "1–2 yrs experience", apply anyway if your skills & projects match!

👍 Tap ❤️ if you found this helpful!

Data Science courses with Certificates (FREE)

❯ Python
cs50.harvard.edu/python/

❯ SQL
https://www.kaggle.com/learn/advanced-sql

❯ Tableau
openclassrooms.com/courses/5873606-learn-how-to-master-tableau-for-data-science

❯ Data Cleaning
kaggle.com/learn/data-cleaning

❯ Data Analysis
freecodecamp.org/learn/data-analysis-with-python/

❯ Mathematics & Statistics
matlabacademy.mathworks.com

❯ Probability
mygreatlearning.com/academy/learn-for-free/courses/statistics-for-data-science-probability

❯ Deep Learning
kaggle.com/learn/intro-to-deep-learning

Double Tap ❤️ For More

🧠 7 Golden Rules to Crack Data Science Interviews 📊🧑‍💻

1️⃣ Master the Fundamentals
⦁ Be clear on stats, ML algorithms, and probability
⦁ Brush up on SQL, Python, and data wrangling

2️⃣ Know Your Projects Deeply
⦁ Be ready to explain models, metrics, and business impact
⦁ Prepare for follow-up questions

3️⃣ Practice Case Studies & Product Thinking
⦁ Think beyond code — focus on solving real problems
⦁ Show how your solution helps the business

4️⃣ Explain Trade-offs
⦁ Why Random Forest vs. XGBoost?
⦁ Discuss bias-variance, precision-recall, etc.

5️⃣ Be Confident with Metrics
⦁ Accuracy isn’t enough — explain F1-score, ROC, AUC
⦁ Tie metrics to the business goal

6️⃣ Ask Clarifying Questions
⦁ Never rush into an answer
⦁ Clarify objective, constraints, and assumptions

7️⃣ Stay Updated & Curious
⦁ Follow latest tools (like LangChain, LLMs)
⦁ Share your learning journey on GitHub or blogs

💬 Double tap ❤️ for more!

6 months roadmap to crack any product based companies 🚀

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Q. Explain the data preprocessing steps in data analysis.

Ans. Data preprocessing transforms the data into a format that is more easily and effectively processed in data mining, machine learning and other data science tasks.
1. Data profiling.
2. Data cleansing.
3. Data reduction.
4. Data transformation.
5. Data enrichment.
6. Data validation.

Q. What Are the Three Stages of Building a Model in Machine Learning?

Ans. The three stages of building a machine learning model are:

Model Building: Choosing a suitable algorithm for the model and train it according to the requirement

Model Testing: Checking the accuracy of the model through the test data

Applying the Model: Making the required changes after testing and use the final model for real-time projects


Q. What are the subsets of SQL?

Ans. The following are the four significant subsets of the SQL:

Data definition language (DDL): It defines the data structure that consists of commands like CREATE, ALTER, DROP, etc.

Data manipulation language (DML): It is used to manipulate existing data in the database. The commands in this category are SELECT, UPDATE, INSERT, etc.

Data control language (DCL): It controls access to the data stored in the database. The commands in this category include GRANT and REVOKE.

Transaction Control Language (TCL): It is used to deal with the transaction operations in the database. The commands in this category are COMMIT, ROLLBACK, SET TRANSACTION, SAVEPOINT, etc.


Q. What is a Parameter in Tableau? Give an Example.

Ans. A parameter is a dynamic value that a customer could select, and you can use it to replace constant values in calculations, filters, and reference lines.
For example, when creating a filter to show the top 10 products based on total profit instead of the fixed value, you can update the filter to show the top 10, 20, or 30 products using a parameter.

✅ Machine Learning Basics – Must-Know Concepts 🤖📊

1️⃣ What is Machine Learning?
📌 A branch of AI where systems learn patterns from data without explicit programming.
💡 Goal: Make predictions or decisions based on past data.

2️⃣ Types of ML
– Supervised Learning: Labeled data → predicts outcomes (e.g., spam detection)
– Unsupervised Learning: Finds patterns in unlabeled data (e.g., clustering)
– Reinforcement Learning: Learns via rewards/punishments (e.g., game AI)

3️⃣ Key Algorithms
– Linear Regression → predicts continuous values
– Logistic Regression → predicts probabilities/class
– Decision Trees → interpretable classification/regression
– K-Means → clustering similar data points
– Random Forest, SVM, Gradient Boosting → advanced predictive models

4️⃣ Model Evaluation Metrics
– Accuracy, Precision, Recall, F1-Score (classification)
– RMSE, MAE (regression)
– Confusion Matrix → visualize true vs predicted labels

5️⃣ Feature Engineering
⚙️ Transform raw data into meaningful inputs
💡 Examples: normalization, encoding categorical variables, handling missing data

6️⃣ Overfitting vs Underfitting
🔺 Overfitting → model too complex, memorizes training data
🔻 Underfitting → model too simple, misses patterns
🛠 Solutions: Regularization, cross-validation, more data

7️⃣ Training & Testing Split
📊 Split data into train (learn) and test (evaluate) sets to measure performance.

8️⃣ Popular Tools & Libraries
– Python: scikit-learn, TensorFlow, PyTorch, Pandas, NumPy
– R, MATLAB for specialized ML tasks

💬 Tap ❤️ for more!