Solution

𝐓𝐨𝐩 𝐜𝐨𝐦𝐩𝐚𝐧𝐲 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐒𝐐𝐋 𝐑𝐞𝐚𝐥 𝐖𝐨𝐫𝐥𝐝 𝐒𝐜𝐞𝐧𝐞𝐫𝐢𝐨:

𝐒𝐜𝐞𝐧𝐞𝐫𝐢𝐨:
You're a data analyst working for a retail company that wants to optimize its sales strategies. The company has a SQL Server database that contains various tables related to sales transactions, customer information, product details, and store locations. Your task is to analyze the data and provide insights to improve sales performance.

𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝟏:
Using SQL Server, how would you determine the top-selling products for the current month?
𝐀𝐧𝐚𝐥𝐲𝐳𝐞𝟏:
To determine the top-selling products for the current month, I would start by querying the sales transactions table to retrieve all sales made within the current month. I would then join this table with the product table using the product ID to get the product names and group the results by product name while summing up the quantities sold. Finally, I would order the results by the total quantity sold in descending order and limit the output to the top N products.

𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝟐:
How would you analyze customer purchasing behavior to identify potential upsell or cross-sell opportunities?
𝐀𝐧𝐚𝐥𝐲𝐳𝐞 𝟐:
To analyze customer purchasing behavior and identify upsell or cross-sell opportunities, I would examine the sales transactions table and look for patterns in customer buying habits. This could involve identifying frequently co-purchased products or analyzing the average order value per customer. Additionally, I might segment customers based on their purchase history, demographics, or other relevant factors to tailor specific upsell or cross-sell strategies.

𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝟑:
How would you assess the performance of different store locations in terms of sales revenue?
𝐀𝐧𝐚𝐥𝐲𝐳𝐞 𝟑:
To assess the performance of different store locations in terms of sales revenue, I would query the sales transactions table and join it with the store locations table using the store ID. Then, I would group the results by store location and calculate the total sales revenue for each location by summing up the transaction amounts. Finally, I would order the results by sales revenue in descending order to identify the top-performing locations.

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𝐓𝐨𝐩 𝐜𝐨𝐦𝐩𝐚𝐧𝐲 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐒𝐐𝐋 𝐑𝐞𝐚𝐥 𝐖𝐨𝐫𝐥𝐝 𝐒𝐜𝐞𝐧𝐞𝐫𝐢𝐨:

𝐒𝐜𝐞𝐧𝐞𝐫𝐢𝐨:
You're working as a data analyst for a healthcare provider organization. The organization manages patient data in a SQL Server database, including information about medical appointments, diagnoses, treatments, and patient demographics. Your task is to analyze the data to improve patient care, operational efficiency, and resource allocation.

𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝟒:
How would you identify patients who are at risk of missing their upcoming appointments based on their historical appointment attendance patterns?


𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝟓:
How would you analyze the effectiveness of different treatments for a specific medical condition based on patient outcomes?

𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝟔:
How would you analyze patient demographics to identify disparities in healthcare access or outcomes?


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Resume key words for data scientist role explained in points:

1. Data Analysis:
- Proficient in extracting, cleaning, and analyzing data to derive insights.
- Skilled in using statistical methods and machine learning algorithms for data analysis.
- Experience with tools such as Python, R, or SQL for data manipulation and analysis.

2. Machine Learning:
- Strong understanding of machine learning techniques such as regression, classification, clustering, and neural networks.
- Experience in model development, evaluation, and deployment.
- Familiarity with libraries like TensorFlow, scikit-learn, or PyTorch for implementing machine learning models.

3. Data Visualization:
- Ability to present complex data in a clear and understandable manner through visualizations.
- Proficiency in tools like Matplotlib, Seaborn, or Tableau for creating insightful graphs and charts.
- Understanding of best practices in data visualization for effective communication of findings.

4. Big Data:
- Experience working with large datasets using technologies like Hadoop, Spark, or Apache Flink.
- Knowledge of distributed computing principles and tools for processing and analyzing big data.
- Ability to optimize algorithms and processes for scalability and performance.

5. Problem-Solving:
- Strong analytical and problem-solving skills to tackle complex data-related challenges.
- Ability to formulate hypotheses, design experiments, and iterate on solutions.
- Aptitude for identifying opportunities for leveraging data to drive business outcomes and decision-making.


Resume key words for a data analyst role

1. SQL (Structured Query Language):
- SQL is a programming language used for managing and querying relational databases.
- Data analysts often use SQL to extract, manipulate, and analyze data stored in databases, making it a fundamental skill for the role.

2. Python/R:
- Python and R are popular programming languages used for data analysis and statistical computing.
- Proficiency in Python or R allows data analysts to perform various tasks such as data cleaning, modeling, visualization, and machine learning.

3. Data Visualization:
- Data visualization involves presenting data in graphical or visual formats to communicate insights effectively.
- Data analysts use tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn to create visualizations that help stakeholders understand complex data patterns and trends.

4. Statistical Analysis:
- Statistical analysis involves applying statistical methods to analyze and interpret data.
- Data analysts use statistical techniques to uncover relationships, trends, and patterns in data, providing valuable insights for decision-making.

5. Data-driven Decision Making:
- Data-driven decision making is the process of making decisions based on data analysis and evidence rather than intuition or gut feelings.
- Data analysts play a crucial role in helping organizations make informed decisions by analyzing data and providing actionable insights that drive business strategies and operations.

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𝐄𝐘 for the Power BI role, these were some of the questions asked during the interview.

𝟏. Can you explain the difference between duplicating and referencing a query in Power Query Editor? How do these operations impact data transformation and query dependencies?

𝟐. What is the distinction between DirectQuery and Live Connection in Power BI? How do these connectivity options affect data retrieval and report performance?

𝟑. Describe the difference between UserPrincipalName (UPN) and UserName in Power BI. How are these identifiers used for user authentication and access control within the platform?

𝟒. What is a Key Performance Indicator (KPI) in the context of Power BI? How do you define and visualize KPIs to monitor business performance effectively?

𝟓. How can you enable clients to modify visualizations in a report after it has been shared or published in Power BI? Explain the approach to empower end-users to customize visuals dynamically.

𝟔. What is the Power Query Editor in Power BI, and how does it facilitate data transformation tasks? Discuss its role in shaping data for use in reports and dashboards.

𝟕. What is a Composite Model in Power BI, and how does it enhance data modeling flexibility? Explain how it allows combining imported data with DirectQuery sources within a single report.

𝟖. Can you highlight significant updates or features introduced in the 2024 version of Power BI that impact data analysis and visualization capabilities?

𝟗. Is it feasible to schedule a report refresh on a monthly basis in Power BI? Describe the available options for scheduling report refresh and any constraints related to monthly refresh cycles.

𝟏𝟎. What are the file formats available to save a Power BI file (e.g., PBIX, PBIT, PBIP )? How do these file formats differ in terms of portability, sharing, and collaboration capabilities within the Power BI ecosystem? Please explain the advantages and use cases for each format.

I am sharing real interview questions asked in companies nowadays to help you prepare more practically for interviews.

7 Data Science Project with Github 🔗 Link


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1. Home Price Predictions - Dataset
https://www.kaggle.com/code/ashydv/housing-price-prediction-linear-regression

2. Credit Card Approval Prediction - You can use the dataset on Kaggle
Kaggle - https://www.kaggle.com/datasets/rikdifos/credit-card-approval-prediction
Blog -
https://medium.com/analytics-vidhya/machine-learning-steps-explained-using-credit-card-approval-dataset-b18555c48b5a

3. Text Summarization -
Github - https://github.com/dipanjanS/text-analytics-with-python
Kaggle - https://www.kaggle.com/code/mallaavinash/text-summarization

4. Uber Data Analysis
Blog -
https://www.analyticsvidhya.com/blog/2021/10/end-to-end-predictive-analysis-on-ubers-data/
Dataset -
https://www.kaggle.com/datasets/fivethirtyeight/uber-pickups-in-new-york-city


5. Forest Fire Detection.
Github - https://github.com/ErtugrulKusva/Forest_Fire_Detector


6. Video Classification
Github - https://github.com/topics/video-classification


7. Language Detection
Dataset - https://www.kaggle.com/datasets/basilb2s/language-detection
Blog -
https://www.analyticsvidhya.com/blog/2021/03/language-detection-using-natural-language-processing/



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This repository contains a list of awesome open-source libraries that will help you deploy, monitor, version, scale, and secure your production machine learning


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Data Scientist Problems and Tools 🧵

🧹 Data Cleaning - Pandas
📊 Data Visualization - Matplotlib
📈 Statistical Analysis - SciPy
🤖 Machine Learning - Scikit-Learn
🧠 Deep Learning - TensorFlow
💾 Big Data Processing - Apache Spark
📝 Natural Language Processing - NLTK
🚀 Model Deployment - Flask
🔀 Version Control - GitHub
🗄️ Data Storage - PostgreSQL
☁️ Cloud Computing - AWS
🧪 Experiment Tracking - MLflow

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AI tools for data analyst role.

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