This is me @adityabansal095

You can ping me directly here if you need any assistance or any help.

Power BI Roadmap: Step-by-Step Guide to Master Power BI 📊💻

Whether you're aiming to be a data analyst, business intelligence pro, or dashboard expert - this roadmap has you covered 👇

📍 1. Power BI Basics
⦁ Get familiar with Power BI Desktop interface
⦁ Connect to data sources (Excel, CSV, databases)
⦁ Learn Basic visualizations: tables, charts, slicers

📍 2. Data Transformation & Modeling
⦁ Use Power Query Editor to clean & shape data
⦁ Create relationships between tables
⦁ Understand data types & formats

📍 3. DAX Fundamentals
⦁ Master calculated columns & measures
⦁ Learn core functions: SUM, CALCULATE, FILTER, RELATED
⦁ Use variables and time intelligence functions

📍 4. Advanced Visualizations
⦁ Build interactive reports and dashboards
⦁ Use bookmarks, buttons & drill-throughs
⦁ Customize visuals & layouts for storytelling

📍 5. Data Refresh & Gateway
⦁ Set up scheduled refresh with data gateways
⦁ Understand live vs import modes
⦁ Manage refresh performance

📍 6. Row-Level Security (RLS)
⦁ Learn to restrict data access by user roles
⦁ Implement roles & test security in reports

📍 7. Power BI Service & Collaboration
⦁ Publish reports to Power BI Service
⦁ Share dashboards and collaborate with teams
⦁ Use workspaces, apps, and permissions

📍 8. Power BI Mobile & Embedded
⦁ Optimize reports for mobile devices
⦁ Embed Power BI visuals in apps or websites

📍 9. Performance Optimization
⦁ Use Performance Analyzer to tune reports
⦁ Optimize data models & DAX queries
⦁ Best practices for large datasets

📍 10. Power BI API & Automation
⦁ Use Power BI REST API for automation
⦁ Integrate with Power Automate & Azure services

📍 11. Real Projects & Practice
⦁ Build sample dashboards: Sales, Marketing, Finance
⦁ Join challenges on platforms like Enterprise DNA, Radacad

📍 12. Certification & Career Growth
⦁ Prepare for DA-100 / PL-300 certification
⦁ Build portfolio & LinkedIn presence
⦁ Apply for BI Analyst & Power BI Developer roles

💡 Pro Tip: Combine Power BI skills with SQL and Python for a powerful data career combo!

Pandas Interview Q&A (Frequently Asked)

📊 Q1. What is Pandas and why is it used?

👉 Python library for data manipulation & analysis
👉 Provides powerful data structures like DataFrame & Series
👉 Used for cleaning, transforming, and analyzing data

📊 Q2. What is the difference between Series and DataFrame?

👉 Series → 1D labeled array (single column)
👉 DataFrame → 2D tabular structure (rows & columns)
👉 DataFrame is a collection of multiple Series

📊 Q3. How do you handle missing values in Pandas?

👉 isnull() / notnull() to detect missing values
👉 fillna() to replace missing data
👉 dropna() to remove missing records

📊 Q4. What is the difference between loc[] and iloc[]?

👉 loc[] → Label-based indexing
👉 iloc[] → Integer position-based indexing
👉 Use loc for named indexes, iloc for numeric positions

📊 Q5. What is groupby() in Pandas?

👉 Used for splitting data into groups
👉 Apply aggregation functions (sum, mean, count)
👉 Essential for data summarization

📊 Q6. What is the difference between merge() and concat()?

👉 merge() → SQL-like joins (inner, left, right, outer)
👉 concat() → Stacks data vertically or horizontally
👉 Use merge for relational data combining

📊 Q7. How do you filter data in Pandas?

👉 Use boolean conditions (df[df['col'] > value])
👉 Multiple conditions using & and |
👉 Helps in extracting specific insights

📊 Q8. What is apply() function?

👉 Applies a function across rows or columns
👉 Used for custom transformations
👉 More flexible than built-in functions

🚀 Data engineering roles worth checking for SQL learners



💼 Role: Data Engineer
🏢 Company: EXL
📍 Location: India, IN
🌎 Work mode: remote
✨ Why it is worth checking: Strong exposure to AWS Glue, Redshift, Athena, PySpark, ETL/ELT pipelines, and data warehousing concepts.
🔗 https://artha.link/@adityabansal095/jobs/data-engineer-exl-india-ab6b2cd2

💼 Role: Data Engineer
🏢 Company: Newtap Finance
📍 Location: Bengaluru, Karnataka, IN
🌎 Work mode: onsite
✨ Why it is worth checking: Hands-on work with Airflow, EMR, Spark, Athena, CDC ingestion, and lakehouse architecture.
🔗 https://artha.link/@adityabansal095/jobs/data-engineer-newtap-finance-bengaluru-36d7cd70

💼 Role: Data Engineer – Analytics & Aws
🏢 Company: Health Catalyst
📍 Location: Hyderabad, Telangana, IN
🌎 Work mode: remote
✨ Why it is worth checking: Good mix of data engineering and analytics using AWS Glue, Snowflake, SQL, Python, Kafka, and Airflow.
🔗 https://artha.link/@adityabansal095/jobs/data-engineer-analytics-aws-health-catalyst-hyderabad-14f9084d

Save this for later or share with someone preparing for data engineering interviews.

#DataEngineering #SQL #AWS #Snowflake #Airflow #ETL #Python #Jobs #JobAlert

Most people still get confused between RANK, DENSE_RANK and ROW_NUMBER.

I made a short video explaining the exact difference with a simple example.

Took only 30 seconds.

Watch it here: https://youtube.com/shorts/vptXC6QRjOE

Save it if you have interviews coming up.

𝐒𝐐𝐋 𝐂𝐚𝐬𝐞 𝐒𝐭𝐮𝐝𝐢𝐞𝐬 𝐟𝐨𝐫 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰:

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

🚀 Data engineering roles with strong cloud stack exposure

If you are working on SQL, ETL pipelines, Databricks, Snowflake, or PySpark projects, these roles are worth checking.

💼 Role: Aws Data Engineer
🏢 Company: Tata Consultancy Services
📍 Location: Bengaluru, Noida, Gandhi Nagar, Pune, Chennai
✨ Why it is worth checking: Strong focus on Databricks on AWS, Delta Lake, Structured Streaming, CDC, and Medallion Architecture.
🔗 https://artha.link/@adityabansal095/jobs/aws-data-engineer-tata-consultancy-gandhinagar-92feef28

💼 Role: Azure Data Engineer
🏢 Company: Recro
📍 Location: Hyderabad, Bangalore, Pune
🌎 Work mode: hybrid
✨ Why it is worth checking: Good match if you are building Azure data pipelines with ADF, SQL, Python, PySpark, and Databricks.
🔗 https://artha.link/@adityabansal095/jobs/azure-data-engineer-recro-pune-district-75c72d64

💼 Role: Azure Data Engineer
🏢 Company: EXL
📍 Location: Noida, Gurgaon, Pune, Bangalore, Hyderabad, Chennai
🌎 Work mode: remote
✨ Why it is worth checking: Useful for engineers working with ADF, DBT, Snowflake, and ETL/ELT workflows.
🔗 https://artha.link/@adityabansal095/jobs/azure-data-engineer-exl-noida-6c18eb62

Save this for later or send it to someone job hunting.

#DataEngineering #SQL #Databricks #Snowflake #AzureDataFactory #PySpark #ETL

💥𝐌𝐲 𝐟𝐫𝐢𝐞𝐧𝐝 𝐣𝐮𝐬𝐭 𝐠𝐨𝐭 𝐚 𝟏𝟐 𝐋𝐏𝐀 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐫𝐨𝐥𝐞 𝐚𝐭 𝐏𝐰𝐂.

🎯What stood out in his interviews wasn’t basic SELECT and WHERE.
He was comfortable with JOINs, CTEs, window functions, and actual problem-solving questions.

That’s the gap I see with a lot of people preparing right now.
They know the syntax.

But when the question gets a bit messy, they freeze.

🧠If you’re preparing for data roles, focus less on memorising commands and more on solving real problems.

𝐒𝐭𝐚𝐫𝐭 𝐟𝐫𝐨𝐦 𝐬𝐜𝐫𝐚𝐭𝐜𝐡. 𝐋𝐞𝐚𝐫𝐧. 𝐁𝐮𝐢𝐥𝐝. 𝐑𝐞𝐩𝐞𝐚𝐭.

Don’t mean to offend anyone
But if anyone does not find value in this post or things shared in this channel

Please leave or please ping me on what needs to be improved

Data Science Interview Prep Guide

1️⃣ Core Data Science Concepts
• What is Data Science vs Data Analytics vs ML
• Descriptive, diagnostic, predictive, prescriptive analytics
• Structured vs unstructured data
• Data-driven decision making
• Business problem framing

2️⃣ Statistics Probability (Non-Negotiable)
• Mean, median, variance, standard deviation
• Probability distributions (normal, binomial, Poisson)
• Hypothesis testing p-values
• Confidence intervals
• Correlation vs causation
• Sampling bias

3️⃣ Data Cleaning EDA
• Handling missing values outliers
• Data normalization scaling
• Feature engineering
• Exploratory data analysis (EDA)
• Data leakage detection
• Data quality validation

4️⃣ Python SQL for Data Science
• Python (NumPy, Pandas)
• Data manipulation transformations
• Vectorization performance optimization
• SQL joins, CTEs, window functions
• Writing business-ready queries

5️⃣ Machine Learning Essentials
• Supervised vs unsupervised learning
• Regression vs classification
• Model selection baseline models
• Overfitting, underfitting
• Bias–variance tradeoff
• Hyperparameter tuning

6️⃣ Model Evaluation Metrics
• Accuracy, precision, recall, F1
• ROC AUC
• Confusion matrix
• RMSE, MAE, log loss
• Metrics for imbalanced data
• Linking ML metrics to business KPIs

7️⃣ Real-World Deployment Knowledge
• Feature stores
• Model deployment (batch vs real-time)
• Model monitoring drift
• Experiment tracking
• Data model versioning
• Model explainability (business-friendly)

8️⃣ Must-Have Projects
• Customer churn prediction
• Fraud detection
• Sales or demand forecasting
• Recommendation system
• End-to-end ML pipeline
• Business-focused case study

9️⃣ Common Interview Questions
• Walk me through an end-to-end DS project
• How do you choose evaluation metrics?
• How do you handle imbalanced data?
• How do you explain a model to leadership?
• How do you improve a failing model?

🔟 Pro Tips
✔️ Always connect answers to business impact
✔️ Explain why, not just how
✔️ Be clear about trade-offs
✔️ Discuss failures learnings
✔️ Show structured thinking

📊 Data engineering and BI roles worth exploring

If you are building SQL, ETL, analytics, or AI engineering skills, these opportunities align well with real-world data workflows and production systems.

💼 Role: Lead Ai Engineer (agentic Systems)
🏢 Company: S&P Global
📍 Location: Haryana, Gurugram, IN
✨ Why it is worth checking: Strong mix of Python, ETL, Databricks, Snowflake, Kubernetes, LangGraph, and production-grade AI systems.
🔗 https://artha.link/@adityabansal095/jobs/lead-ai-engineer-agentic-systems-s-p-global-haryana-402f311a

💼 Role: Data Analyst / Engineer
🏢 Company: Kyndryl
📍 Location: Karnataka, Bangalore, IN
✨ Why it is worth checking: Good fit if you work with SQL, Python, Snowflake, ETL/ELT, ODI, Power BI, and enterprise data warehousing.
🔗 https://artha.link/@adityabansal095/jobs/data-analyst-engineer-kyndryl-karnataka-42df3c00

💼 Role: Business Intelligence Engineer, Aop - Row Apex
🏢 Company: ASSPL - Karnataka
📍 Location: Hyderabad, Telangana, IN
✨ Why it is worth checking: Covers BI reporting, SQL, Python, ETL pipelines, Tableau/Quicksight, and analytics frameworks at scale.
🔗 https://artha.link/@adityabansal095/jobs/business-intelligence-engineer-aop-row-asspl-karnataka-hyderabad-28f3112f

Save this for later or share with someone preparing for data engineering interviews.

#DataEngineering #SQL #ETL #BusinessIntelligence #Snowflake #Python #Analytics

Hey Engineers

#jobalert #dailyjobalert

🚨 Hiring: Snowflake + SQL Developer | Immediate Joiners
🚨We are looking for experienced professionals with strong expertise in Snowflake and SQL for an exciting opportunity.
🔹 Role: Snowflake + SQL
🔹 Total Experience: 6–12 Years
🔹 Snowflake Experience: 3+ Years
🔹 Primary Skill: Snowflake
🔹 Secondary Skill: SQL
🔹 Notice Period: Immediate Joiners Only
🔹 Location: Hyderabad – Priority | PAN India
Interview mode : F2F Drive 10 OCT 2026 PAN India

🔑 Key Skills amp; Responsibilities:• Strong hands-on experience in Snowflake and SQL• Good knowledge of ETL/ELT processes and data transformation• Strong understanding of Data Warehousing and Data Modeling• Experience with Dimensions, Facts, and Change Data Capture (CDC)• Hands-on experience with Snowflake Architecture, Data Warehousing amp; Snowpipe• Develop and optimize complex SQL queries for transformation, aggregation, and analysis• Design and implement scalable data pipelines and architectures in Snowflake• Experience working with large historical snapshot tables and feature-based datasets• Knowledge of Python for data analysis and ad hoc query development is an advantage• Understanding of data security, compliance, and performance optimization📩 Interested candidates can contact 9392791250
Mail Id: asmithar@intellicsglobal.com.

Role: Data Engineer - Snowflake
Location: Remote - PAN INDIA
Exp: 8+ Years
Job Type: Full Time
Mandatory skills: Snowflake, Apache Airflow, Python, ETL, SQL
NOTE: Need Only Immediate Joiners

Interested Candidate please share me your Updated Resume to
lidiya.r@vdartinc.com

Hello Engineers
#jobalert #dataengineer


Coforge is Hiring | Big Data Engineer & Big Data Lead.
Coforge Ltd. is looking for experienced Big Data Engineers and Big Data Leads with strong expertise in Spark, Scala, and AWS to join our growing Data Engineering team.

Note: Immediate Joiners Preferred.
Experience:- 3 to 10 Years
Mandatory Skills:- Apache Spark, Scala & AWS Services

Job Locations:- Hyderabad, Pune & Greater Noida
Interview Details:-
17 Interview Date: 10th and 11th October 2026.
Mode: Microsoft Teams (Video Call)
Time: 9:00 AM to 6 PM Interview Duration: 45 Minutes

Share Your CV:- Gaurav.2.Kumar@coforge.com
WhatsApp - 9667427662

Top 10 Theoretical Interview Questions Every Data Analyst Must Prepare

Data Analyst interviews are not just about writing SQL queries — interviewers also test your understanding of core concepts across different tools.

1️⃣ What is the difference between WHERE and HAVING clauses in SQL?
2️⃣ Explain the difference between INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN.
3️⃣ What are Primary Keys and Foreign Keys? Why are they important in databases?
4️⃣ What is the difference between VLOOKUP, XLOOKUP, and INDEX-MATCH in Excel?
5️⃣ What is the difference between a Series and a DataFrame in Pandas?
6️⃣ How do you handle missing values in a dataset?
7️⃣ What is the difference between calculated columns and measures in Power BI?
8️⃣ Explain the difference between Power Query and DAX in Power BI.
9️⃣ Explain the difference between ETL and ELT.
🔟 What is the difference between correlation and causation?

A–Z of Data Analyst 📊💼

A – Analytics 
The process of analyzing data to discover insights and support decision-making.

B – Business Intelligence (BI) 
Technologies and tools used to analyze business data (Power BI, Tableau).

C – Cleaning (Data Cleaning) 
Removing errors, duplicates, and inconsistencies from data.

D – Dashboard 
A visual display of key metrics and insights.

E – ETL (Extract, Transform, Load) 
Process of collecting, cleaning, and storing data for analysis.

F – Forecasting 
Predicting future trends using historical data.

G – Group By 
A method to organize data into categories for analysis.

H – Hypothesis Testing 
Testing assumptions using statistical methods.

I – Insight 
Meaningful information derived from data analysis.

J – Join 
Combining data from multiple tables (SQL concept).

K – KPI (Key Performance Indicator) 
A measurable value showing business performance.

L – Linear Regression 
A statistical method used to predict relationships between variables.

M – Metrics 
Quantifiable measures used to track performance.

N – Normalization 
Organizing data to reduce redundancy and improve efficiency.

O – Outlier 
A data point significantly different from others.

P – Pivot Table 
A tool used to summarize and analyze data quickly.

Q – Query 
A request to retrieve data from a database.

R – Reporting 
Presenting data insights through charts and summaries.

S – SQL 
Language used to manage and analyze structured data.

T – Trend Analysis 
Identifying patterns or changes over time.

U – Unstructured Data 
Data without predefined format (text, images).

V – Visualization 
Representing data using charts or graphs.

W – Warehousing (Data Warehouse) 
Central storage of large structured datasets.

X – X-axis 
Horizontal axis in charts representing variables.

Y – YoY (Year-over-Year) 
Comparing data from one year to another.

Z – Z-Score
Statistical measure showing how far a value is from the mean

Like for more ❤️👍

Top 10 Theoretical Interview Questions Every Data Analyst Must Prepare

Data Analyst interviews are not just about writing SQL queries — interviewers also test your understanding of core concepts across different tools.

1️⃣ What is the difference between WHERE and HAVING clauses in SQL?
2️⃣ Explain the difference between INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN.
3️⃣ What are Primary Keys and Foreign Keys? Why are they important in databases?
4️⃣ What is the difference between VLOOKUP, XLOOKUP, and INDEX-MATCH in Excel?
5️⃣ What is the difference between a Series and a DataFrame in Pandas?
6️⃣ How do you handle missing values in a dataset?
7️⃣ What is the difference between calculated columns and measures in Power BI?
8️⃣ Explain the difference between Power Query and DAX in Power BI.
9️⃣ Explain the difference between ETL and ELT.
🔟 What is the difference between correlation and causation?

A–Z of Data Analyst 📊💼

A – Analytics 
The process of analyzing data to discover insights and support decision-making.

B – Business Intelligence (BI) 
Technologies and tools used to analyze business data (Power BI, Tableau).

C – Cleaning (Data Cleaning) 
Removing errors, duplicates, and inconsistencies from data.

D – Dashboard 
A visual display of key metrics and insights.

E – ETL (Extract, Transform, Load) 
Process of collecting, cleaning, and storing data for analysis.

F – Forecasting 
Predicting future trends using historical data.

G – Group By 
A method to organize data into categories for analysis.

H – Hypothesis Testing 
Testing assumptions using statistical methods.

I – Insight 
Meaningful information derived from data analysis.

J – Join 
Combining data from multiple tables (SQL concept).

K – KPI (Key Performance Indicator) 
A measurable value showing business performance.

L – Linear Regression 
A statistical method used to predict relationships between variables.

M – Metrics 
Quantifiable measures used to track performance.

N – Normalization 
Organizing data to reduce redundancy and improve efficiency.

O – Outlier 
A data point significantly different from others.

P – Pivot Table 
A tool used to summarize and analyze data quickly.

Q – Query 
A request to retrieve data from a database.

R – Reporting 
Presenting data insights through charts and summaries.

S – SQL 
Language used to manage and analyze structured data.

T – Trend Analysis 
Identifying patterns or changes over time.

U – Unstructured Data 
Data without predefined format (text, images).

V – Visualization 
Representing data using charts or graphs.

W – Warehousing (Data Warehouse) 
Central storage of large structured datasets.

X – X-axis 
Horizontal axis in charts representing variables.

Y – YoY (Year-over-Year) 
Comparing data from one year to another.

Z – Z-Score
Statistical measure showing how far a value is from the mean

Like for more ❤️👍

SQL Projects with Datasets 👇

📌E-commerce Sales Analysis:
Dataset: Online retail dataset from the UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/Online+Retail)

📌Social Media Analytics:
Twitter API or Twitter datasets available on platforms like Kaggle (https://www.kaggle.com/datasets?search=twitter)

📌Healthcare Data Management:
MIMIC-III (Medical Information Mart for Intensive Care III) dataset (https://mimic.mit.edu/docs/iii/)

📌Retail Inventory Management:
Sample retail sales dataset available on platforms like Kaggle (https://www.kaggle.com/datasets?search=retail)

📌Financial Portfolio Analysis:
Yahoo Finance API or finance datasets available on platforms like Kaggle (https://www.kaggle.com/datasets?search=finance)

📌Real Estate Market Analysis:
Zillow dataset (https://www.zillow.com/research/data/) or real estate datasets available on platforms like Kaggle (https://www.kaggle.com/datasets?search=real+estate)